From f8a41ff713af80f6776fb266bb174f2b2e84e3fc Mon Sep 17 00:00:00 2001 From: "Yue, Wenjiao" Date: Mon, 21 Nov 2022 11:09:03 +0800 Subject: [PATCH 01/17] modify auto-api --- .github/workflows/publish.yml | 10 +- Makefile | 34 - README.md | 64 +- _static/custom.css | 18 - _static/index.html | 1 - api-documentation/api-reference.rst | 16 - api-documentation/benchmark-api.rst | 10 - api-documentation/objective-api.rst | 13 - api-documentation/pruning-api.rst | 10 - api-documentation/quantization-api.rst | 7 - docs/Makefile | 53 + make.bat => docs/make.bat | 72 +- docs/source/_static/custom.css | 176 ++ .../_static}/imgs/Distillation_workflow.png | Bin docs/{ => source/_static}/imgs/INC_GUI.gif | Bin docs/{ => source/_static}/imgs/PTQ.png | Bin docs/{ => source/_static}/imgs/QAT.png | Bin .../_static}/imgs/architecture.png | Bin .../_static}/imgs/bench/benchmark-details.png | Bin .../imgs/bench/benchmark-edit-wizard.png | Bin .../_static}/imgs/bench/benchmarks-table.png | Bin 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source/io_doc}/Quantization.md | 0 docs/source/io_doc/README.md | 251 +++ docs/{ => source/io_doc}/adaptor.md | 0 .../io_doc/api-documentation/adaptor.rst | 9 + .../api-documentation/api-introduction.md | 210 +++ .../source/io_doc/api-documentation}/apis.rst | 7 +- .../io_doc/api-documentation/common.rst | 15 + .../api-documentation/common/benchmark.rst | 7 + .../io_doc/api-documentation/common/bleu.rst | 61 + .../api-documentation/common/criterion.rst | 211 +++ .../io_doc/api-documentation/common/data.rst | 11 + .../common/data/basic_nas.rst | 13 + .../common/data/dataloader.rst | 19 + .../common/data/datasets.rst | 12 + .../common/data/transforms.rst | 1562 +++++++++++++++++ .../api-documentation/common/metric.rst | 12 + .../api-documentation/common/metrics.rst | 622 +++++++ .../io_doc/api-documentation/common/model.rst | 7 + .../api-documentation/common/optimizer.rst | 203 +++ .../io_doc/api-documentation/component.rst | 15 + .../api-documentation/component/Benchmark.rst | 13 + .../component/Distillation.rst | 13 + .../component/MixedPrecision.rst | 13 + .../component/ModelConversion.rst | 13 + .../api-documentation/component/Nas.rst | 120 ++ .../api-documentation/component/Pruning.rst | 13 + .../component/Quantization.rst | 13 + .../api-documentation/component/Scheduler.rst | 13 + .../io_doc/api-documentation/pythonic.rst | 8 + .../io_doc/api-documentation/strategy.rst | 9 + docs/{ => source/io_doc}/api-introduction.md | 0 docs/{ => source/io_doc}/backend_quant.md | 0 docs/{ => source/io_doc}/bench.md | 0 docs/{ => source/io_doc}/benchmark.md | 0 docs/{ => source/io_doc}/contributions.md | 0 docs/{ => source/io_doc}/dataloader.md | 0 docs/{ => source/io_doc}/dataset.md | 0 docs/{ => source/io_doc}/design.md | 0 docs/{ => source/io_doc}/distillation.md | 276 +-- .../io_doc}/distillation_quantization.md | 0 docs/{ => source/io_doc}/distributed.md | 0 docs/{ => source/io_doc}/doclist.rst | 0 .../io_doc}/dynamic_quantization.md | 0 docs/{ => 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source/io_doc}/tensorboard.md | 0 docs/{ => source/io_doc}/transform.md | 0 docs/{ => source/io_doc}/tuning_strategies.md | 0 .../io_doc}/validated_model_list.md | 0 docs/{ => source/io_doc}/welcome.md | 0 .../sphinx-requirements.txt | 0 .../data/datasets/bert_dataset.py | 3 +- 155 files changed, 4438 insertions(+), 750 deletions(-) delete mode 100644 Makefile delete mode 100755 _static/custom.css delete mode 100644 _static/index.html delete mode 100755 api-documentation/api-reference.rst delete mode 100755 api-documentation/benchmark-api.rst delete mode 100755 api-documentation/objective-api.rst delete mode 100755 api-documentation/pruning-api.rst delete mode 100755 api-documentation/quantization-api.rst create mode 100644 docs/Makefile rename make.bat => docs/make.bat (95%) create mode 100644 docs/source/_static/custom.css rename docs/{ => source/_static}/imgs/Distillation_workflow.png (100%) rename docs/{ => source/_static}/imgs/INC_GUI.gif (100%) rename docs/{ => 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(100%) rename docs/{ => source/io_doc}/tuning_strategies.md (100%) rename docs/{ => source/io_doc}/validated_model_list.md (100%) rename docs/{ => source/io_doc}/welcome.md (100%) rename sphinx-requirements.txt => docs/sphinx-requirements.txt (100%) mode change 100755 => 100644 diff --git a/.github/workflows/publish.yml b/.github/workflows/publish.yml index e4e37f8fc72..66211ff1eb9 100644 --- a/.github/workflows/publish.yml +++ b/.github/workflows/publish.yml @@ -3,7 +3,7 @@ name: Publish on: push: branches: - - master + - api-docs jobs: build: @@ -14,16 +14,16 @@ jobs: - uses: actions/checkout@v1 - name: Install dependencies run: | - export PATH="$HOME/.local/bin:$PATH" + export PATH="$HOME/.local/bin:$PATH/docs" sudo apt-get install -y python3-setuptools - pip3 install --user -r sphinx-requirements.txt + pip3 install --user -r docs/sphinx-requirements.txt - name: Build the docs run: | - export PATH="$HOME/.local/bin:$PATH" + export PATH="$HOME/.local/bin:$PATH/docs" make html - name: Push the docs uses: peaceiris/actions-gh-pages@v3 with: github_token: ${{ secrets.GITHUB_TOKEN }} - publish_dir: _build/html + publish_dir: docs/_build/html publish_branch: latestHTML \ No newline at end of file diff --git a/Makefile b/Makefile deleted file mode 100644 index 7b308f858cc..00000000000 --- a/Makefile +++ /dev/null @@ -1,34 +0,0 @@ -# Minimal makefile for Sphinx documentation -# - -# You can set these variables from the command line. -SPHINXOPTS = -SPHINXBUILD = sphinx-build -SPHINXPROJ = ProjectnameIntelLowPrecisionOptimizationTool -SOURCEDIR = . -BUILDDIR = _build - -# Put it first so that "make" without argument is like "make help". -help: - @$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) - -.PHONY: help Makefile - - -html: - $(SPHINXBUILD) -b html "$(SOURCEDIR)" "$(BUILDDIR)/html" $(SPHINXOPTS) $(O) - cp _static/index.html $(BUILDDIR)/html/index.html - mkdir "$(BUILDDIR)/html/docs/imgs" - cp docs/imgs/architecture.png "$(BUILDDIR)/html/docs/imgs/architecture.png" - cp docs/imgs/workflow.png "$(BUILDDIR)/html/docs/imgs/workflow.png" - cp docs/imgs/INC_GUI.gif "$(BUILDDIR)/html/docs/imgs/INC_GUI.gif" - cp docs/imgs/release_data.png "$(BUILDDIR)/html/docs/imgs/release_data.png" - cp "$(BUILDDIR)/html/README.html" "$(BUILDDIR)/html/README.html.tmp" - sed 's/.md/.html/g' "$(BUILDDIR)/html/README.html.tmp" > "$(BUILDDIR)/html/README.html" - rm -f "$(BUILDDIR)/html/README.html.tmp" - - -# Catch-all target: route all unknown targets to Sphinx using the new -# "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS). -%: Makefile - @$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) \ No newline at end of file diff --git a/README.md b/README.md index 29239cbeee8..d7f7fda1447 100644 --- a/README.md +++ b/README.md @@ -44,7 +44,7 @@ Python version: 3.7, 3.8, 3.9, 3.10 # Or install nightly full version from pip (including GUI) pip install -i https://test.pypi.org/simple/ neural-compressor-full ``` -More installation methods can be found at [Installation Guide](./docs/installation_guide.md). Please check out our [FAQ](./docs/faq.md) for more details. +More installation methods can be found at [Installation Guide](./docs/source/io_doc/installation_guide.md). Please check out our [FAQ](./docs/source/io_doc/faq.md) for more details. ## Getting Started ### Quantization with Python API @@ -71,7 +71,7 @@ Search for ```jupyter-lab-neural-compressor``` in the Extension Manager in Jupyt Extension -### Quantization with [GUI](./docs/bench.md) +### Quantization with [GUI](./docs/source/io_doc/bench.md) ```shell # An ONNX Example pip install onnx==1.12.0 onnxruntime==1.12.1 onnxruntime-extensions @@ -80,8 +80,8 @@ wget https://github.com/onnx/models/raw/main/vision/classification/resnet/model/ # Start GUI inc_bench ``` - - Architecture + + Architecture ## System Requirements @@ -98,7 +98,7 @@ inc_bench #### Intel® Neural Compressor quantized ONNX models support multiple hardware vendors through ONNX Runtime: -* Intel CPU, AMD/ARM CPU, and NVidia GPU. Please refer to the validated model [list](./docs/validated_model_list.md#Validated-ONNX-QDQ-INT8-models-on-multiple-hardware-through-ONNX-Runtime). +* Intel CPU, AMD/ARM CPU, and NVidia GPU. Please refer to the validated model [list](./docs/source/io_doc/validated_model_list.md#Validated-ONNX-QDQ-INT8-models-on-multiple-hardware-through-ONNX-Runtime). ### Validated Software Environment @@ -146,11 +146,11 @@ inc_bench > Set the environment variable ``TF_ENABLE_ONEDNN_OPTS=1`` to enable oneDNN optimizations if you are using TensorFlow v2.6 to v2.8. oneDNN is the default for TensorFlow v2.9. ### Validated Models -Intel® Neural Compressor validated 420+ [examples](./examples) for quantization with a performance speedup geomean of 2.2x and up to 4.2x on VNNI while minimizing accuracy loss. Over 30 pruning and knowledge distillation samples are also available. More details for validated models are available [here](docs/validated_model_list.md). +Intel® Neural Compressor validated 420+ [examples](./examples) for quantization with a performance speedup geomean of 2.2x and up to 4.2x on VNNI while minimizing accuracy loss. Over 30 pruning and knowledge distillation samples are also available. More details for validated models are available [here](./docs/source/io_doc/validated_model_list.md).
- - Architecture + + Architecture
@@ -164,10 +164,10 @@ Intel® Neural Compressor validated 420+ [examples](./examples) for quantization - Architecture + Architecture Examples - GUI - APIs + GUI + APIs Intel oneAPI AI Analytics Toolkit @@ -181,10 +181,10 @@ Intel® Neural Compressor validated 420+ [examples](./examples) for quantization - Transform - Dataset - Metric - Objective + Transform + Dataset + Metric + Objective @@ -194,20 +194,20 @@ Intel® Neural Compressor validated 420+ [examples](./examples) for quantization - Quantization - Pruning(Sparsity) - Knowledge Distillation - Mixed Precision - Orchestration + Quantization + Pruning(Sparsity) + Knowledge Distillation + Mixed Precision + Orchestration - Benchmarking - Distributed Training - Model Conversion - TensorBoard + Benchmarking + Distributed Training + Model Conversion + TensorBoard - Distillation for Quantization + Distillation for Quantization Neural Coder @@ -219,9 +219,9 @@ Intel® Neural Compressor validated 420+ [examples](./examples) for quantization - Adaptor - Strategy - Reference Example + Adaptor + Strategy + Reference Example @@ -237,13 +237,13 @@ Intel® Neural Compressor validated 420+ [examples](./examples) for quantization * [Alibaba Cloud and Intel Neural Compressor Deliver Better Productivity for PyTorch Users](https://medium.com/intel-analytics-software/alibaba-cloud-collaborates-with-intel-neural-compressor-for-better-productivity-and-performance-83cdb6500420) (Sep 2022) * [Efficient Text Classification with Intel Neural Compressor](https://medium.com/intel-analytics-software/efficient-text-classification-with-intel-neural-compressor-4853296deeac) (Sep 2022) -> View our [full publication list](docs/publication_list.md). +> View our [full publication list](./docs/source/io_doc/publication_list.md). ## Additional Content -* [Release Information](docs/releases_info.md) -* [Contribution Guidelines](docs/contributions.md) -* [Legal Information](docs/legal_information.md) +* [Release Information](./docs/source/io_doc/releases_info.md) +* [Contribution Guidelines](./docs/source/io_doc/contributions.md) +* [Legal Information](./docs/source/io_doc/legal_information.md) * [Security Policy](SECURITY.md) * [Intel® Neural Compressor Website](https://intel.github.io/neural-compressor) diff --git a/_static/custom.css b/_static/custom.css deleted file mode 100755 index b2d7a2ec6c2..00000000000 --- a/_static/custom.css +++ /dev/null @@ -1,18 +0,0 @@ -/* make the page 1000px */ -.wy-nav-content { - max-width: 1000px; -} - -/* code block highlight color in rtd changed to lime green, no no no */ - -.rst-content tt.literal, .rst-content code.literal, .highlight { - background: #f0f0f0; -} -.rst-content tt.literal, .rst-content code.literal { - color: #000000; -} - -table.docutils th { - text-align: center; - vertical-align: middle; -} \ No newline at end of file diff --git a/_static/index.html b/_static/index.html deleted file mode 100644 index 5f62e3d9bef..00000000000 --- a/_static/index.html +++ /dev/null @@ -1 +0,0 @@ - \ No newline at end of file diff --git a/api-documentation/api-reference.rst b/api-documentation/api-reference.rst deleted file mode 100755 index 784f6bae5eb..00000000000 --- a/api-documentation/api-reference.rst +++ /dev/null @@ -1,16 +0,0 @@ -API Reference -############# - -Read an `introduction to Intel Neural Compressor APIs <../docs/api-introduction.md>`__. - -The following APIs are available: - -.. toctree:: - :maxdepth: 1 - - benchmark-api - objective-api - pruning-api - quantization-api - - \ No newline at end of file diff --git a/api-documentation/benchmark-api.rst b/api-documentation/benchmark-api.rst deleted file mode 100755 index c6f3da1e87b..00000000000 --- a/api-documentation/benchmark-api.rst +++ /dev/null @@ -1,10 +0,0 @@ -.. _benchmark-api - -Benchmark -######### - -.. automodule:: neural_compressor.benchmark - :members: - -.. autoclass:: neural_compressor.benchmark.Benchmark - :members: \ No newline at end of file diff --git a/api-documentation/objective-api.rst b/api-documentation/objective-api.rst deleted file mode 100755 index de63375c256..00000000000 --- a/api-documentation/objective-api.rst +++ /dev/null @@ -1,13 +0,0 @@ -.. _objective-api - -Objective -######### - -.. automodule:: neural_compressor.objective - :members: - -.. autoclass:: neural_compressor.objective.Measurer - :members: - -.. autoclass:: neural_compressor.objective.FootprintMeasure - :members: \ No newline at end of file diff --git a/api-documentation/pruning-api.rst b/api-documentation/pruning-api.rst deleted file mode 100755 index ff2fd584546..00000000000 --- a/api-documentation/pruning-api.rst +++ /dev/null @@ -1,10 +0,0 @@ -.. _pruning-api - -Pruning -####### - -.. automodule:: neural_compressor.pruning - :members: - -.. autoclass:: neural_compressor.pruning.Pruning - :members: \ No newline at end of file diff --git a/api-documentation/quantization-api.rst b/api-documentation/quantization-api.rst deleted file mode 100755 index af1017d0ef1..00000000000 --- a/api-documentation/quantization-api.rst +++ /dev/null @@ -1,7 +0,0 @@ -.. _quantization-api - -Quantization -############ - -.. automodule:: neural_compressor.quantization - :members: \ No newline at end of file diff --git a/docs/Makefile b/docs/Makefile new file mode 100644 index 00000000000..9acd52f5f18 --- /dev/null +++ b/docs/Makefile @@ -0,0 +1,53 @@ +# Minimal makefile for Sphinx documentation +# + +# You can set these variables from the command line. +SPHINXOPTS = +SPHINXBUILD = sphinx-build +SOURCEDIR = source +BUILDDIR = _build +IMGDIR = source/_static/imgs +BUILDIMGDIR = _build/html/io_doc/imgs +CODEIMGDIR = _build/html/_static + +# Put it first so that "make" without argument is like "make help". +help: + @$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) + +.PHONY: help Makefile + + +html: + # cp README.md to docs, modify response-link + cp -f "../README.md" "./source/io_doc/README.md" + cp -f "./source/io_doc/README.md" "./source/io_doc/README.md.tmp" + sed 's/.md/.html/g; s/.\/docs\/source\/io_doc/./g; s/.\/examples/..\/..\/..\/..\/examples/g; s/.\/neural_coder/..\/..\/..\/..\/neural_coder/g; s/.\/docs\/source\/_static/..\/\/_static/g;' "./source/io_doc/README.md.tmp" > "./source/io_doc/README.md" + rm -f "./source/io_doc/README.md.tmp" + + # make sure other png can display normal + $(SPHINXBUILD) -b html "$(SOURCEDIR)" "$(BUILDDIR)/html" $(SPHINXOPTS) $(O) + + cp source/_static/index.html $(BUILDDIR)/html/index.html + mkdir -p "$(BUILDIMGDIR)" + # common svg + cp -f "$(CODEIMGDIR)/imgs/common/code.svg" "$(CODEIMGDIR)/images/view-page-source-icon.svg" + cp -f "$(CODEIMGDIR)/imgs/common/right.svg" "$(CODEIMGDIR)/images/chevron-right-orange.svg" + # README.md imgs + # README.md link + + cp "../neural_coder/extensions/screenshots/extmanager.png" "_build/html/_static/imgs/extmanager.png" + cp "$(IMGDIR)/INC_GUI.gif" "$(BUILDIMGDIR)/INC_GUI.gif" + cp "$(IMGDIR)/release_data.png" "$(BUILDIMGDIR)/release_data.png" + # cp docs/imgs/architecture.png "$(BUILDDIR)/html/docs/imgs/architecture.png" + # cp docs/imgs/workflow.png "$(BUILDDIR)/html/docs/imgs/workflow.png" + # cp docs/imgs/INC_GUI.gif "$(BUILDDIR)/html/docs/imgs/INC_GUI.gif" + # cp docs/imgs/release_data.png "$(BUILDDIR)/html/docs/imgs/release_data.png" + # cp "$(BUILDDIR)/html/README.html" "$(BUILDDIR)/html/README.html.tmp" + # sed 's/.md/.html/g' "$(BUILDDIR)/html/README.html.tmp" > "$(BUILDDIR)/html/README.html" + # rm -f "$(BUILDDIR)/html/README.html.tmp" + + +# Catch-all target: route all unknown targets to Sphinx using the new +# "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS). +%: Makefile + @$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) \ No newline at end of file diff --git a/make.bat b/docs/make.bat similarity index 95% rename from make.bat rename to docs/make.bat index 695a8b3ecfd..f9a02b02da3 100644 --- a/make.bat +++ b/docs/make.bat @@ -1,36 +1,36 @@ -@ECHO OFF - -pushd %~dp0 - -REM Command file for Sphinx documentation - -if "%SPHINXBUILD%" == "" ( - set SPHINXBUILD=sphinx-build -) -set SOURCEDIR=. -set BUILDDIR=_build -set SPHINXPROJ=ProjectnameIntelLowPrecisionOptimizationTool - -if "%1" == "" goto help - -%SPHINXBUILD% >NUL 2>NUL -if errorlevel 9009 ( - echo. - echo.The 'sphinx-build' command was not found. Make sure you have Sphinx - echo.installed, then set the SPHINXBUILD environment variable to point - echo.to the full path of the 'sphinx-build' executable. Alternatively you - echo.may add the Sphinx directory to PATH. - echo. - echo.If you don't have Sphinx installed, grab it from - echo.http://sphinx-doc.org/ - exit /b 1 -) - -%SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% -goto end - -:help -%SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% - -:end -popd +@ECHO OFF + +pushd %~dp0 + +REM Command file for Sphinx documentation + +if "%SPHINXBUILD%" == "" ( + set SPHINXBUILD=sphinx-build +) +set SOURCEDIR=. +set BUILDDIR=_build +set SPHINXPROJ=ProjectnameIntelLowPrecisionOptimizationTool + +if "%1" == "" goto help + +%SPHINXBUILD% >NUL 2>NUL +if errorlevel 9009 ( + echo. + echo.The 'sphinx-build' command was not found. Make sure you have Sphinx + echo.installed, then set the SPHINXBUILD environment variable to point + echo.to the full path of the 'sphinx-build' executable. Alternatively you + echo.may add the Sphinx directory to PATH. + echo. + echo.If you don't have Sphinx installed, grab it from + echo.http://sphinx-doc.org/ + exit /b 1 +) + +%SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% +goto end + +:help +%SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% + +:end +popd diff --git a/docs/source/_static/custom.css b/docs/source/_static/custom.css new file mode 100644 index 00000000000..dd15f99b60b --- /dev/null +++ b/docs/source/_static/custom.css @@ -0,0 +1,176 @@ +.site-footer { + display: none; +} + +.container { + padding-left: 0px; +} +.header-holder .main-menu { + left: 65%; +} + +.header-holder .container { + padding-left: 0px; +} + +.headTitleStyle { + font-size: 1.3rem; + font-family: auto; + color: #eee; +} + +.headTitleStyle:before{ + content: "🏠"; +} + +.header-container { + display: flex; +} + +.iconStyle { + width: 100%; +} + +.docs-tutorials-resources { + display: none; +} + +.navbar-logo { + background: #0068b5; + line-height: 1; + width: 25.7%; + padding: 0; + margin-right: 12px; + position: relative; + display: flex; + justify-content: center; + align-items: center; + height: 70px; +} + +article.pytorch-article h2 { + margin-top: 0; +} + +article.pytorch-article ul li { + line-height: 1.75rem; + margin-bottom: 0; + font-family: FreightSans, Helvetica Neue, Helvetica, Arial, sans-serif; +} + +ul.pytorch-breadcrumbs a, p a:link, p a:visited, p a:hover, +.pytorch-left-menu li.toctree-l1.current > a, .pytorch-right-menu li.toctree-l1.current > a, +.pytorch-left-menu li.toctree-l1.current > a:before, .pytorch-right-menu li.toctree-l1.current > a:before, +.header-holder .main-menu ul li a:hover, +a:link, a:visited, a:hover { + color: #0068b5; +} + +article.pytorch-article .class dt { + border-left: none; + border-top: 3px solid #0068b5; + padding-right: 1.25rem; +} + +article.pytorch-article .class .method dt, article.pytorch-article .class .staticmethod dt { + border-left: 3px solid #0068b5; + border-top: none; +} + + +article.pytorch-article .function dt, article.pytorch-article .attribute dt, article.pytorch-article .class .attribute dt { + border-left: 3px solid #0068b5; +} + +article.pytorch-article table tbody td { + color: #6c6c6d; + white-space: normal; + padding: 0.9375rem; + font-size: 1rem; + line-height: 1.375rem; +} + +article.pytorch-article table tr td:first-of-type { + width: 30%; +} + +article.pytorch-article dl { + margin-bottom: 0rem; +} +ol, ul, dl { + margin-top: revert; + margin-bottom: 0rem; +} + +article.pytorch-article .class dd { + padding-left: 3.75rem; +} + +article.pytorch-article .class dd p { + color: #262626; +} + +article.pytorch-article table tr td:nth-of-type(2) { + text-align: left; +} + +article.pytorch-article table tbody td .pre { + color: #0068b5; + font-size: 1rem; +} + +article.pytorch-article table tr td > p { + margin-bottom: 0rem; +} + +article.pytorch-article p, article.pytorch-article ol li, article.pytorch-article dl dt, article.pytorch-article dl dd, article.pytorch-article blockquote { + font-size: 1rem; + line-height: 1.375rem; + color: #262626; + letter-spacing: 0.01px; + font-weight: 500; +} + +p { + line-height: 24px; + font-size: 16px; + margin: 0 0 24px; +} + +.rst-content p a { + overflow-wrap: anywhere; +} + +.rst-content img { + max-width: 100%; + height: auto; +} + +article.pytorch-article blockquote { + width: 100%; +} + +article.pytorch-article img { + width: auto; +} + +article.pytorch-article hr { + margin: 0px; +} +article.pytorch-article table th, article.pytorch-article table tr, +article.pytorch-article table tr th:first-of-type, article.pytorch-article table tr td:first-of-type { + border: 1px solid #eee; + text-align: center; +} + +article.pytorch-article table td { + border: 1px solid #eee; +} + +h1 { + margin-bottom: 1.375rem; +} + +article.pytorch-article table tbody .row-odd { + background-color: #f3f4f7; +} \ No newline at end of file diff --git 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+
+
+ +
+ +
+ + +
+
+
+{% endblock %} + +{% block menu %} +
+ + ⚠ You are viewing old API docs. + +
+{{ super() }} +{% endblock %} + +{% block sidebartitle %} + +{% endblock %} + + +{%- block footer %} + + + + + +{% endblock %} + + + + diff --git a/conf.py b/docs/source/conf.py old mode 100755 new mode 100644 similarity index 85% rename from conf.py rename to docs/source/conf.py index e6316de8938..16270865632 --- a/conf.py +++ b/docs/source/conf.py @@ -12,12 +12,15 @@ # add these directories to sys.path here. If the directory is relative to the # documentation root, use os.path.abspath to make it absolute, like shown here. # +from os import getenv +import importlib.util import os import sys -sys.path.insert(0, os.path.abspath('.')) -import importlib.util +sys.path.insert(0, os.path.abspath('../../')) moduleName = 'version' -modulePath = os.getcwd() + '/neural_compressor/version.py' +# get version.py +modulePathNeu = os.path.abspath(os.path.join(os.getcwd(), "../..")) +modulePath = modulePathNeu + '/neural_compressor/version.py' spec = importlib.util.spec_from_file_location(moduleName,modulePath) NCversion = importlib.util.module_from_spec(spec) spec.loader.exec_module(NCversion) @@ -44,15 +47,20 @@ # Add any Sphinx extension module names here, as strings. They can be # extensions coming with Sphinx (named 'sphinx.ext.*') or your custom # ones. -extensions = ["recommonmark","sphinx_markdown_tables","sphinx_md", "sphinx.ext.autodoc"] - +extensions = ['recommonmark', 'sphinx_markdown_tables', 'sphinx.ext.coverage', 'sphinx.ext.autosummary', + 'sphinx_md', 'autoapi.extension', 'sphinx.ext.napoleon'] +autoapi_dirs = ['../../neural_compressor'] +autoapi_add_toctree_entry = False +autosummary_generate = True +autoapi_options = ['members', 'show-inheritance', + 'show-module-summary', 'imported-members', ] +autoapi_ignore = [] # Add any paths that contain templates here, relative to this directory. templates_path = ['_templates'] # The suffix(es) of source filenames. # You can specify multiple suffix as a list of string: # -# source_suffix = ['.rst', '.md'] source_suffix = ['.rst', '.md'] # The master toctree document. @@ -77,13 +85,13 @@ # -- Options for HTML output ------------------------------------------------- # The theme to use for HTML and HTML Help pages. See the documentation for -# a list of builtin themes. +# a list of builtin themes. # # html_theme = "asteroid_sphinx_theme" # html_theme = "classic" # html_theme = "alabaster" # html_theme = "sphinx_book_theme" -html_theme = "sphinx_rtd_theme" +html_theme = "pytorch_sphinx_theme" # Theme options are theme-specific and customize the look and feel of a theme # further. For a list of options available for each theme, see the @@ -96,6 +104,17 @@ # so a file named "default.css" will overwrite the builtin "default.css". html_static_path = ['_static'] +def skip_util_classes(app, what, name, obj, skip, options): + if what == "class" and obj.docstring.startswith("Not displayed") : + skip = True + return skip + + +def setup(app): + app.add_css_file("custom.css") + app.connect("autoapi-skip-member", skip_util_classes) + +html_favicon = '_static/imgs/common/intel.svg' # Custom sidebar templates, must be a dictionary that maps document names # to template names. # @@ -163,13 +182,9 @@ 'Miscellaneous'), ] -def setup(app): - app.add_css_file("custom.css") - -from os import getenv sphinx_md_useGitHubURL = True -baseBranch = "master" +baseBranch = "api-docs" commitSHA = getenv('GITHUB_SHA') githubBaseURL = 'https://github.com/' + (getenv('GITHUB_REPOSITORY') or 'intel/neural-compressor') + '/' githubFileURL = githubBaseURL + "blob/" diff --git a/index.rst b/docs/source/index.rst old mode 100755 new mode 100644 similarity index 50% rename from index.rst rename to docs/source/index.rst index 3b323adc4c8..c915ba2f3fa --- a/index.rst +++ b/docs/source/index.rst @@ -10,15 +10,16 @@ Sections .. toctree:: :maxdepth: 1 - README.md - docs/tutorial.md - docs/examples_readme.md - api-documentation/apis.rst - docs/doclist.rst - docs/releases_info.md - docs/contributions.md - docs/legal_information.md - docs/security_policy.md + io_doc/README.md + io_doc/tutorial.md + io_doc/examples_readme.md + io_doc/api-documentation/apis.rst + io_doc/doclist.rst + io_doc/releases_info.md + io_doc/contributions.md + io_doc/legal_information.md + io_doc/security_policy.md + Intel® Neural Compressor repository diff --git a/docs/CODE_OF_CONDUCT.md b/docs/source/io_doc/CODE_OF_CONDUCT.md similarity index 100% rename from docs/CODE_OF_CONDUCT.md rename to docs/source/io_doc/CODE_OF_CONDUCT.md diff --git a/docs/FX.md b/docs/source/io_doc/FX.md similarity index 100% rename from docs/FX.md rename to docs/source/io_doc/FX.md diff --git a/docs/NAS.md b/docs/source/io_doc/NAS.md similarity index 100% rename from docs/NAS.md rename to docs/source/io_doc/NAS.md diff --git a/docs/PTQ.md b/docs/source/io_doc/PTQ.md similarity index 100% rename from docs/PTQ.md rename to docs/source/io_doc/PTQ.md diff --git a/docs/QAT.md b/docs/source/io_doc/QAT.md similarity index 100% rename from docs/QAT.md rename to docs/source/io_doc/QAT.md diff --git a/docs/Quantization.md b/docs/source/io_doc/Quantization.md similarity index 100% rename from docs/Quantization.md rename to docs/source/io_doc/Quantization.md diff --git a/docs/source/io_doc/README.md b/docs/source/io_doc/README.md new file mode 100644 index 00000000000..9e86cd28029 --- /dev/null +++ b/docs/source/io_doc/README.md @@ -0,0 +1,251 @@ +
+ +Intel® Neural Compressor +=========================== +

An open-source Python library supporting popular model compression techniques on all mainstream deep learning frameworks (TensorFlow, PyTorch, ONNX Runtime, and MXNet)

+ +[![python](https://img.shields.io/badge/python-3.7%2B-blue)](https://github.com/intel/neural-compressor) +[![version](https://img.shields.io/badge/release-1.14-green)](https://github.com/intel/neural-compressor/releases) +[![license](https://img.shields.io/badge/license-Apache%202-blue)](https://github.com/intel/neural-compressor/blob/master/LICENSE) +[![coverage](https://img.shields.io/badge/coverage-90%25-green)](https://github.com/intel/neural-compressor) +[![Downloads](https://static.pepy.tech/personalized-badge/neural-compressor?period=total&units=international_system&left_color=grey&right_color=green&left_text=downloads)](https://pepy.tech/project/neural-compressor) +
+ +--- +
+ +Intel® Neural Compressor, formerly known as Intel® Low Precision Optimization Tool, is an open-source Python library that runs on Intel CPUs and GPUs, which delivers unified interfaces across multiple deep-learning frameworks for popular network compression technologies such as quantization, pruning, and knowledge distillation. This tool supports automatic accuracy-driven tuning strategies to help the user quickly find out the best quantized model. It also implements different weight-pruning algorithms to generate a pruned model with predefined sparsity goal. It also supports knowledge distillation to distill the knowledge from the teacher model to the student model. +Intel® Neural Compressor is a critical AI software component in the [Intel® oneAPI AI Analytics Toolkit](https://software.intel.com/content/www/us/en/develop/tools/oneapi/ai-analytics-toolkit.html). + + +**Visit the Intel® Neural Compressor online document website at: .** + +## Installation + +### Prerequisites + +Python version: 3.7, 3.8, 3.9, 3.10 + +### Install on Linux +- Release binary install + ```Shell + # install stable basic version from pip + pip install neural-compressor + # Or install stable full version from pip (including GUI) + pip install neural-compressor-full + ``` +- Nightly binary install + ```Shell + git clone https://github.com/intel/neural-compressor.git + cd neural-compressor + pip install -r requirements.txt + # install nightly basic version from pip + pip install -i https://test.pypi.org/simple/ neural-compressor + # Or install nightly full version from pip (including GUI) + pip install -i https://test.pypi.org/simple/ neural-compressor-full + ``` +More installation methods can be found at [Installation Guide](./installation_guide.html). Please check out our [FAQ](./faq.html) for more details. + +## Getting Started +### Quantization with Python API + +```shell +# A TensorFlow Example +pip install tensorflow +# Prepare fp32 model +wget https://storage.googleapis.com/intel-optimized-tensorflow/models/v1_6/mobilenet_v1_1.0_224_frozen.pb +``` +```python +import tensorflow as tf +from neural_compressor.experimental import Quantization, common +quantizer = Quantization() +quantizer.model = './mobilenet_v1_1.0_224_frozen.pb' +dataset = quantizer.dataset('dummy', shape=(1, 224, 224, 3)) +quantizer.calib_dataloader = common.DataLoader(dataset) +quantizer.fit() +``` +### Quantization with [JupyterLab Extension](../../../../neural_coder/extensions/neural_compressor_ext_lab/README.html) +Search for ```jupyter-lab-neural-compressor``` in the Extension Manager in JupyterLab and install with one click: + + + Extension + + +### Quantization with [GUI](./bench.html) +```shell +# An ONNX Example +pip install onnx==1.12.0 onnxruntime==1.12.1 onnxruntime-extensions +# Prepare fp32 model +wget https://github.com/onnx/models/raw/main/vision/classification/resnet/model/resnet50-v1-12.onnx +# Start GUI +inc_bench +``` + + Architecture + + +## System Requirements + +### Validated Hardware Environment +#### Intel® Neural Compressor supports CPUs based on [Intel 64 architecture or compatible processors](https://en.wikipedia.org/wiki/X86-64): + +* Intel Xeon Scalable processor (formerly Skylake, Cascade Lake, Cooper Lake, and Icelake) +* Future Intel Xeon Scalable processor (code name Sapphire Rapids) + +#### Intel® Neural Compressor supports GPUs built on Intel's Xe architecture: + +* [Intel® Data Center GPU Flex Series](https://www.intel.com/content/www/us/en/products/docs/discrete-gpus/data-center-gpu/flex-series/overview.html) + +#### Intel® Neural Compressor quantized ONNX models support multiple hardware vendors through ONNX Runtime: + +* Intel CPU, AMD/ARM CPU, and NVidia GPU. Please refer to the validated model [list](./validated_model_list.html#Validated-ONNX-QDQ-INT8-models-on-multiple-hardware-through-ONNX-Runtime). + +### Validated Software Environment + +* OS version: CentOS 8.4, Ubuntu 20.04 +* Python version: 3.7, 3.8, 3.9, 3.10 + + + + + + + + + + + + + + + + + + + + + + +
FrameworkTensorFlowIntel TensorFlowPyTorchIntel® Extension for PyTorch*ONNX RuntimeMXNet
Version2.10.0
+ 2.9.1
+ 2.8.2
+ 		2.10.0
+ 2.9.1
+ 2.8.0
+ 		1.12.1+cpu
+ 1.11.0+cpu
+ 1.10.0+cpu		1.12.0
+ 1.11.0
+ 1.10.0		1.12.1
+ 1.11.0
+ 1.10.0		1.8.0
+ 1.7.0
+ 1.6.0
+ +> **Note:** +> Set the environment variable ``TF_ENABLE_ONEDNN_OPTS=1`` to enable oneDNN optimizations if you are using TensorFlow v2.6 to v2.8. oneDNN is the default for TensorFlow v2.9. + +### Validated Models +Intel® Neural Compressor validated 420+ [examples](../../../../examples) for quantization with a performance speedup geomean of 2.2x and up to 4.2x on VNNI while minimizing accuracy loss. Over 30 pruning and knowledge distillation samples are also available. More details for validated models are available [here](./validated_model_list.html). + +
+ + Architecture + +
+ +## Documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Overview
ArchitectureExamplesGUIAPIs
Intel oneAPI AI Analytics ToolkitAI and Analytics Samples
Basic API
TransformDatasetMetricObjective
Deep Dive
QuantizationPruning(Sparsity)Knowledge DistillationMixed PrecisionOrchestration
BenchmarkingDistributed TrainingModel ConversionTensorBoard
Distillation for QuantizationNeural Coder
Advanced Topics
AdaptorStrategyReference Example
+ +## Selected Publications/Events +* [Personalized Stable Diffusion with Few-Shot Fine-Tuning](https://medium.com/intel-analytics-software/personalized-stable-diffusion-with-few-shot-fine-tuning-on-a-single-cpu-f01a3316b13) (Nov 2022) +* [Meet the Innovation of Intel AI Software: Intel® Extension for TensorFlow*](https://www.intel.com/content/www/us/en/developer/articles/technical/innovation-of-ai-software-extension-tensorflow.html) (Oct 2022) +* [PyTorch* Inference Acceleration with Intel® Neural Compressor](https://www.intel.com/content/www/us/en/developer/articles/technical/pytorch-inference-with-intel-neural-compressor.html#gs.gnq0cj) (Oct 2022) +* Neural Coder, a new plug-in for Intel Neural Compressor was covered by [Twitter](https://intel.sharepoint.com/sites/iLit-MyLearning/Shared%20Documents/My%20Learning/Haihao/MSR/2022/o%09https:/twitter.com/IntelDevTools/status/1583629213697212416?s=20&t=f2dXY_g9-bCO-GQm-S6Btg), [LinkedIn](https://www.linkedin.com/posts/intel-software_oneapi-ai-deeplearning-activity-6989377309917007872-Dbzg?utm_source=share&utm_medium=member_desktop), and [Intel Developer Zone](https://mp.weixin.qq.com/s/LL-4eD-R0YagFgODM23oQA) from Intel, and [Twitter](https://twitter.com/IntelDevTools/status/1583629213697212416/retweets) and [LinkedIn](https://www.linkedin.com/feed/update/urn:li:share:6990377841435574272/) from Hugging Face. (Oct 2022) +* Intel Neural Compressor successfully landed on [GCP](https://console.cloud.google.com/marketplace/product/bitnami-launchpad/inc-tensorflow-intel?project=verdant-sensor-286207), [AWS](https://aws.amazon.com/marketplace/pp/prodview-yjyh2xmggbmga#pdp-support), and [Azure](https://azuremarketplace.microsoft.com/en-us/marketplace/apps/bitnami.inc-tensorflow-intel) marketplace. (Oct 2022) +* [Neural Coder (Intel Neural Compressor Plug-in): One-Click, No-Code Solution (Pat's Keynote IntelON 2022)](https://twitter.com/i/status/1574909338203967497) (Sep 2022) +* [Alibaba Cloud and Intel Neural Compressor Deliver Better Productivity for PyTorch Users](https://medium.com/intel-analytics-software/alibaba-cloud-collaborates-with-intel-neural-compressor-for-better-productivity-and-performance-83cdb6500420) (Sep 2022) +* [Efficient Text Classification with Intel Neural Compressor](https://medium.com/intel-analytics-software/efficient-text-classification-with-intel-neural-compressor-4853296deeac) (Sep 2022) + +> View our [full publication list](./publication_list.html). + +## Additional Content + +* [Release Information](./releases_info.html) +* [Contribution Guidelines](./contributions.html) +* [Legal Information](./legal_information.html) +* [Security Policy](SECURITY.html) +* [Intel® Neural Compressor Website](https://intel.github.io/neural-compressor) + +## Hiring + +We are actively hiring. Send your resume to inc.maintainers@intel.com if you are interested in model compression techniques. diff --git a/docs/adaptor.md b/docs/source/io_doc/adaptor.md similarity index 100% rename from docs/adaptor.md rename to docs/source/io_doc/adaptor.md diff --git a/docs/source/io_doc/api-documentation/adaptor.rst b/docs/source/io_doc/api-documentation/adaptor.rst new file mode 100644 index 00000000000..83dc04c0181 --- /dev/null +++ b/docs/source/io_doc/api-documentation/adaptor.rst @@ -0,0 +1,9 @@ +Adaptor +########### + +The adaptor API information is available: + +.. toctree:: + :maxdepth: 1 + + diff --git a/docs/source/io_doc/api-documentation/api-introduction.md b/docs/source/io_doc/api-documentation/api-introduction.md new file mode 100644 index 00000000000..e5fc85f5d21 --- /dev/null +++ b/docs/source/io_doc/api-documentation/api-introduction.md @@ -0,0 +1,210 @@ +API Documentation +================= + +## Introduction + +Intel® Neural Compressor is an open-source Python library designed to help users quickly deploy low-precision inference solutions on popular deep learning (DL) frameworks such as TensorFlow*, PyTorch*, MXNet, and ONNX Runtime. It automatically optimizes low-precision recipes for deep learning models in order to achieve optimal product objectives, such as inference performance and memory usage, with expected accuracy criteria. + + +## User-facing APIs + +These APIs are intended to unify low-precision quantization interfaces cross multiple DL frameworks for the best out-of-the-box experiences. + +> **Note** +> +> Neural Compressor is continuously improving user-facing APIs to create a better user experience. + +> Two sets of user-facing APIs exist. One is the default one supported from Neural Compressor v1.0 for backwards compatibility. The other set consists of new APIs in +the `neural_compressor.experimental` package. + +> We recommend that you use the APIs located in neural_compressor.experimental. All examples have been updated to use the experimental APIs. + +The major differences between the default user-facing APIs and the experimental APIs are: + +1. The experimental APIs abstract the `neural_compressor.experimental.common.Model` concept to cover those cases whose weight and graph files are stored separately. +2. The experimental APIs unify the calling style of the `Quantization`, `Pruning`, and `Benchmark` classes by setting model, calibration dataloader, evaluation dataloader, and metric through class attributes rather than passing them as function inputs. +3. The experimental APIs refine Neural Compressor built-in transforms/datasets/metrics by unifying the APIs cross different framework backends. + +## Experimental user-facing APIs + +Experimental user-facing APIs consist of the following components: + +### Quantization-related APIs + +```python +# neural_compressor.experimental.Quantization +class Quantization(object): + def __init__(self, conf_fname_or_obj): + ... + + def __call__(self): + ... + + @property + def calib_dataloader(self): + ... + + @property + def eval_dataloader(self): + ... + + @property + def model(self): + ... + + @property + def metric(self): + ... + + @property + def postprocess(self, user_postprocess): + ... + + @property + def q_func(self): + ... + + @property + def eval_func(self): + ... + +``` +The `conf_fname_or_obj` parameter used in the class initialization is the path to the user yaml configuration file or Quantization_Conf class. This yaml file is used to control the entire tuning behavior on the model. + +**Neural Compressor User YAML Syntax** + +> Intel® Neural Compressor provides template yaml files for [Post-Training Quantization](../neural_compressor/template/ptq.yaml), [Quantization-Aware Training](../neural_compressor/template/qat.yaml), and [Pruning](../neural_compressor/template/pruning.yaml) scenarios. Refer to these template files to understand the meaning of each field. + +> Note that most fields in the yaml templates are optional. View the [HelloWorld Yaml](../examples/helloworld/tf_example2/conf.yaml) example for reference. + +```python +# Typical Launcher code +from neural_compressor.experimental import Quantization, common + +# optional if Neural Compressor built-in dataset could be used as model input in yaml +class dataset(object): + def __init__(self, *args): + ... + + def __getitem__(self, idx): + # return single sample and label tuple without collate. label should be 0 for label-free case + ... + + def len(self): + ... + +# optional if Neural Compressor built-in metric could be used to do accuracy evaluation on model output in yaml +class custom_metric(object): + def __init__(self): + ... + + def update(self, predict, label): + # metric update per mini-batch + ... + + def result(self): + # final metric calculation invoked only once after all mini-batch are evaluated + # return a scalar to neural_compressor for accuracy-driven tuning. + # by default the scalar is higher-is-better. if not, set tuning.accuracy_criterion.higher_is_better to false in yaml. + ... + +quantizer = Quantization(conf.yaml) +quantizer.model = '/path/to/model' +# below two lines are optional if Neural Compressor built-in dataset is used as model calibration input in yaml +cal_dl = dataset('/path/to/calibration/dataset') +quantizer.calib_dataloader = common.DataLoader(cal_dl, batch_size=32) +# below two lines are optional if Neural Compressor built-in dataset is used as model evaluation input in yaml +dl = dataset('/path/to/evaluation/dataset') +quantizer.eval_dataloader = common.DataLoader(dl, batch_size=32) +# optional if Neural Compressor built-in metric could be used to do accuracy evaluation in yaml +quantizer.metric = common.Metric(custom_metric) +q_model = quantizer.fit() +q_model.save('/path/to/output/dir') +``` + +`model` attribute in `Quantization` class is an abstraction of model formats across different frameworks. Neural Compressor supports passing the path of `keras model`, `frozen pb`, `checkpoint`, `saved model`, `torch.nn.model`, `mxnet.symbol.Symbol`, `gluon.HybirdBlock`, and `onnx model` to instantiate a `neural_compressor.experimental.` class and set to `quantizer.model`. + +`calib_dataloader` and `eval_dataloader` attribute in `Quantization` class is used to set up a calibration dataloader by code. It is optional to set if the user sets corresponding fields in yaml. + +`metric` attribute in `Quantization` class is used to set up a custom metric by code. It is optional to set if user finds Neural Compressor built-in metric could be used with their model and sets corresponding fields in yaml. + +`postprocess` attribute in `Quantization` class is not necessary in most of the use cases. It is only needed when the user wants to use the built-in metric but the model output can not directly be handled by Neural Compressor built-in metrics. In this case, the user can register a transform to convert the model output to the expected one required by the built-in metric. + +`q_func` attribute in `Quantization` class is only for `Quantization Aware Training` case, in which the user needs to register a function that takes `model` as the input parameter and executes the entire training process with self-contained training hyper-parameters. + +`eval_func` attribute in `Quantization` class is reserved for special cases. If the user had an evaluation function when train a model, the user must implement a `calib_dataloader` and leave `eval_dataloader` as None. Then, modify this evaluation function to take `model` as the input parameter and return a higher-is-better scaler. In some scenarios, it may reduce development effort. + + +### Pruning-related APIs (POC) + +```python +class Pruning(object): + def __init__(self, conf_fname_or_obj): + ... + + def on_epoch_begin(self, epoch): + ... + + def on_step_begin(self, batch_id): + ... + + def on_step_end(self): + ... + + def on_epoch_end(self): + ... + + def __call__(self): + ... + + @property + def model(self): + ... + + @property + def q_func(self): + ... + +``` + +This API is used to do sparsity pruning. Currently, it is a Proof of Concept; Neural Compressor only supports `magnitude pruning` on PyTorch. + +To learn how to use this API, refer to the [pruning document](../pruning.md). + +### Benchmarking-related APIs +```python +class Benchmark(object): + def __init__(self, conf_fname_or_obj): + ... + + def __call__(self): + ... + + @property + def model(self): + ... + + @property + def metric(self): + ... + + @property + def b_dataloader(self): + ... + + @property + def postprocess(self, user_postprocess): + ... +``` + +This API is used to measure model performance and accuracy. + +To learn how to use this API, refer to the [benchmarking document](../docs/benchmark.md). + +## Default user-facing APIs + +The default user-facing APIs exist for backwards compatibility from the v1.0 release. Refer to [v1.1 API](https://github.com/intel/neural-compressor/blob/v1.1/docs/introduction.md) to understand how the default user-facing APIs work. + +View the [HelloWorld example](/examples/helloworld/tf_example6) that uses default user-facing APIs for user reference. + +Full examples using default user-facing APIs can be found [here](https://github.com/intel/neural-compressor/tree/v1.1/examples). diff --git a/api-documentation/apis.rst b/docs/source/io_doc/api-documentation/apis.rst old mode 100755 new mode 100644 similarity index 59% rename from api-documentation/apis.rst rename to docs/source/io_doc/api-documentation/apis.rst index a9bac5c00e4..921dc1beb34 --- a/api-documentation/apis.rst +++ b/docs/source/io_doc/api-documentation/apis.rst @@ -6,5 +6,8 @@ The following API information is available: .. toctree:: :maxdepth: 1 - api-reference - ../docs/api-introduction.md \ No newline at end of file + component + common + strategy + adaptor + pythonic diff --git a/docs/source/io_doc/api-documentation/common.rst b/docs/source/io_doc/api-documentation/common.rst new file mode 100644 index 00000000000..285c9136026 --- /dev/null +++ b/docs/source/io_doc/api-documentation/common.rst @@ -0,0 +1,15 @@ +Common +########### + +The common API information is available: + +.. toctree:: + :maxdepth: 1 + + common/data + common/metric + common/model + common/criterion + common/benchmark + common/optimizer + diff --git a/docs/source/io_doc/api-documentation/common/benchmark.rst b/docs/source/io_doc/api-documentation/common/benchmark.rst new file mode 100644 index 00000000000..6f8288a6a46 --- /dev/null +++ b/docs/source/io_doc/api-documentation/common/benchmark.rst @@ -0,0 +1,7 @@ +.. _quantization-api + +Benchmark +############ + +.. .. automodule:: neural_compressor.quantization +.. :members: \ No newline at end of file diff --git a/docs/source/io_doc/api-documentation/common/bleu.rst b/docs/source/io_doc/api-documentation/common/bleu.rst new file mode 100644 index 00000000000..7b4c04997e4 --- /dev/null +++ b/docs/source/io_doc/api-documentation/common/bleu.rst @@ -0,0 +1,61 @@ +BLEU +==================================================== + +.. py:module:: neural_compressor.experimental.metric.bleu + + +Module Contents +--------------- + +Classes +~~~~~~~ + +.. autoapisummary:: + + neural_compressor.experimental.metric.bleu.BLEU + +.. py:class:: BLEU + + Bases: :py:obj:`object` + + Computes the BLEU (Bilingual Evaluation Understudy) score. + + BLEU is an algorithm for evaluating the quality of text which has + been machine-translated from one natural language to another. + This implementent approximate the BLEU score since we do not + glue word pieces or decode the ids and tokenize the output. + By default, we use ngram order of 4 and use brevity penalty. + Also, this does not have beam search. + + .. attribute:: predictions + + List of translations to score. + + .. attribute:: labels + + List of the reference corresponding to the prediction result. + + .. py:method:: reset() -> None + + Clear the predictions and labels in the cache. + + + .. py:method:: update(prediction: Sequence[str], label: Sequence[str]) -> None + + Add the prediction and label. + + :param prediction: The prediction result. + :param label: The reference corresponding to the prediction result. + + :raises ValueError: An error occurred when the length of the prediction + :raises and label are different.: + + + .. py:method:: result() -> float + + Compute the BLEU score. + + :returns: The approximate BLEU score. + :rtype: bleu_score + + diff --git a/docs/source/io_doc/api-documentation/common/criterion.rst b/docs/source/io_doc/api-documentation/common/criterion.rst new file mode 100644 index 00000000000..1fbc402b195 --- /dev/null +++ b/docs/source/io_doc/api-documentation/common/criterion.rst @@ -0,0 +1,211 @@ +Criterion +========================================================= + +.. py:module:: neural_compressor.experimental.common.criterion + +Module Contents +--------------- + +Classes +~~~~~~~ + +.. autoapisummary:: + + neural_compressor.experimental.common.criterion.PyTorchKnowledgeDistillationLoss + neural_compressor.experimental.common.criterion.PyTorchKnowledgeDistillationLossWrapper + neural_compressor.experimental.common.criterion.TensorflowKnowledgeDistillationLoss + neural_compressor.experimental.common.criterion.TensorflowKnowledgeDistillationLossWrapper + neural_compressor.experimental.common.criterion.TensorflowKnowledgeDistillationLossExternal + + +.. py:class:: PyTorchKnowledgeDistillationLoss(temperature=1.0, loss_types=['CE', 'CE'], loss_weights=[0.5, 0.5], student_model=None, teacher_model=None) + + Bases: :py:obj:`KnowledgeDistillationLoss` + + The PyTorchKnowledgeDistillationLoss class inherits from KnowledgeDistillationLoss. + + .. py:method:: SoftCrossEntropy(logits, targets) + + Return SoftCrossEntropy. + + :param logits: output logits + :type logits: tensor + :param targets: ground truth label + :type targets: tensor + + :returns: SoftCrossEntropy + :rtype: tensor + + + .. py:method:: KullbackLeiblerDivergence(logits, targets) + + Return KullbackLeiblerDivergence. + + :param logits: output logits + :type logits: tensor + :param targets: ground truth label + :type targets: tensor + + :returns: KullbackLeiblerDivergence + :rtype: tensor + + + .. py:method:: teacher_model_forward(input, teacher_model=None, device=None) + + Teacher model forward. + + :param input: input data + :type input: tensor + :param teacher_model: teacher model. Defaults to None. + :type teacher_model: torch.nn.model, optional + :param device: device. Defaults to None. + :type device: torch.device, optional + + :returns: output + :rtype: tensor + + + .. py:method:: teacher_student_loss_cal(student_outputs, teacher_outputs) + + Calculate loss between student model and teacher model. + + :param student_outputs: student outputs + :type student_outputs: tensor + :param teacher_outputs: teacher outputs + :type teacher_outputs: tensor + + :returns: loss + :rtype: tensor + + + .. py:method:: student_targets_loss_cal(student_outputs, targets) + + Calculate loss of student model. + + :param student_outputs: student outputs + :type student_outputs: tensor + :param targets: groud truth label + :type targets: tensor + + :returns: loss + :rtype: tensor + + + +.. py:class:: PyTorchKnowledgeDistillationLossWrapper(param_dict) + + Bases: :py:obj:`object` + + PyTorchKnowledgeDistillationLossWrapper wraps PyTorchKnowledgeDistillationLoss. + + .. py:method:: _param_check() + + + .. py:method:: __call__(**kwargs) + + Return PyTorchKnowledgeDistillationLoss, param dict. + + :returns: PyTorchKnowledgeDistillationLoss + param dict (dict): param dict + :rtype: PyTorchKnowledgeDistillationLoss (class) + + + +.. py:class:: TensorflowKnowledgeDistillationLoss(temperature=1.0, loss_types=['CE', 'CE'], loss_weights=[0.5, 0.5], student_model=None, teacher_model=None) + + Bases: :py:obj:`KnowledgeDistillationLoss` + + The TensorflowKnowledgeDistillationLoss class inherits from KnowledgeDistillationLoss. + + .. py:method:: SoftCrossEntropy(targets, logits) + + Return SoftCrossEntropy. + + :param logits: output logits + :type logits: tensor + :param targets: ground truth label + :type targets: tensor + + :returns: SoftCrossEntropy + :rtype: tensor + + + .. py:method:: teacher_model_forward(input, teacher_model=None) + + Teacher model forward. + + :param input: input data + :type input: tensor + :param teacher_model: teacher model. Defaults to None. + :type teacher_model: optional + :param device: device. Defaults to None. + :type device: torch.device, optional + + :returns: output + :rtype: tensor + + + .. py:method:: teacher_student_loss_cal(student_outputs, teacher_outputs) + + Calculate loss between student model and teacher model. + + :param student_outputs: student outputs + :type student_outputs: tensor + :param teacher_outputs: teacher outputs + :type teacher_outputs: tensor + + :returns: loss + :rtype: tensor + + + .. py:method:: student_targets_loss_cal(student_outputs, targets) + + Calculate loss of student model. + + :param student_outputs: student outputs + :type student_outputs: tensor + :param targets: groud truth label + :type targets: tensor + + :returns: loss + :rtype: tensor + + + .. py:method:: __call__(student_outputs, targets) + + Return loss of student model. + + :param student_outputs: student outputs + :type student_outputs: tensor + :param targets: groud truth label + :type targets: tensor + + :returns: loss + :rtype: tensor + + + +.. py:class:: TensorflowKnowledgeDistillationLossWrapper(param_dict) + + Bases: :py:obj:`object` + + TensorflowKnowledgeDistillationLossWrapper wraps TensorflowKnowledgeDistillationLoss. + + .. py:method:: _param_check() + + + .. py:method:: __call__(**kwargs) + + Return TensorflowKnowledgeDistillationLoss, param dict. + + :returns: TensorflowKnowledgeDistillationLoss + param dict (dict): param dict + :rtype: class + + + +.. py:class:: TensorflowKnowledgeDistillationLossExternal(temperature=1.0, loss_types=['CE', 'CE'], loss_weights=[0.5, 0.5], student_model=None, teacher_model=None) + + Bases: :py:obj:`KnowledgeDistillationLoss` + + TensorflowKnowledgeDistillationLossExternal inherits from KnowledgeDistillationLoss. diff --git a/docs/source/io_doc/api-documentation/common/data.rst b/docs/source/io_doc/api-documentation/common/data.rst new file mode 100644 index 00000000000..9f045f42a5c --- /dev/null +++ b/docs/source/io_doc/api-documentation/common/data.rst @@ -0,0 +1,11 @@ +Data +########### + +The data API information is available: + +.. toctree:: + :maxdepth: 1 + + data/datasets.rst + data/dataloader.rst + data/transforms.rst \ No newline at end of file diff --git a/docs/source/io_doc/api-documentation/common/data/basic_nas.rst b/docs/source/io_doc/api-documentation/common/data/basic_nas.rst new file mode 100644 index 00000000000..4e6785d5e7f --- /dev/null +++ b/docs/source/io_doc/api-documentation/common/data/basic_nas.rst @@ -0,0 +1,13 @@ +.. _data-api + +basic_nas +########## + +.. .. automodule:: neural_compressor.objective +.. :members: + +.. .. autoclass:: neural_compressor.objective.Measurer +.. :members: + +.. .. autoclass:: neural_compressor.objective.FootprintMeasure +.. :members: \ No newline at end of file diff --git a/docs/source/io_doc/api-documentation/common/data/dataloader.rst b/docs/source/io_doc/api-documentation/common/data/dataloader.rst new file mode 100644 index 00000000000..5c87629ccfd --- /dev/null +++ b/docs/source/io_doc/api-documentation/common/data/dataloader.rst @@ -0,0 +1,19 @@ +Dataloader +============== + +BaseDataLoader +--------------- + +.. autoapisummary:: + + neural_compressor.experimental.data.dataloaders.base_dataloader.BaseDataLoader + +dataloaders +------------ +.. autoapisummary:: + + neural_compressor.experimental.data.dataloaders.default_dataloader + neural_compressor.experimental.data.dataloaders.mxnet_dataloader + neural_compressor.experimental.data.dataloaders.onnxrt_dataloader + neural_compressor.experimental.data.dataloaders.pytorch_dataloader + neural_compressor.experimental.data.dataloaders.tensorflow_dataloader \ No newline at end of file diff --git a/docs/source/io_doc/api-documentation/common/data/datasets.rst b/docs/source/io_doc/api-documentation/common/data/datasets.rst new file mode 100644 index 00000000000..f86f41c0040 --- /dev/null +++ b/docs/source/io_doc/api-documentation/common/data/datasets.rst @@ -0,0 +1,12 @@ +Datasets +============== + +.. autoapisummary:: + + neural_compressor.experimental.data.datasets.bert_dataset + neural_compressor.experimental.data.datasets.coco_dataset + neural_compressor.experimental.data.datasets.dataset + neural_compressor.experimental.data.datasets.dummy_dataset + neural_compressor.experimental.data.datasets.imagenet_dataset + neural_compressor.experimental.data.datasets.dummy_dataset_v2 + neural_compressor.experimental.data.datasets.style_transfer_dataset \ No newline at end of file diff --git a/docs/source/io_doc/api-documentation/common/data/transforms.rst b/docs/source/io_doc/api-documentation/common/data/transforms.rst new file mode 100644 index 00000000000..7e452b8910c --- /dev/null +++ b/docs/source/io_doc/api-documentation/common/data/transforms.rst @@ -0,0 +1,1562 @@ +transform +================================================================== + +.. py:module:: neural_compressor.experimental.data.transforms.transform + +.. autoapi-nested-parse:: + + Neural Compressor built-in Transforms on multiple framework backends. + + + +Module Contents +--------------- + +Classes +~~~~~~~ + +.. autoapisummary:: + + neural_compressor.experimental.data.transforms.transform.Transforms + neural_compressor.experimental.data.transforms.transform.TensorflowTransforms + neural_compressor.experimental.data.transforms.transform.MXNetTransforms + neural_compressor.experimental.data.transforms.transform.PyTorchTransforms + neural_compressor.experimental.data.transforms.transform.ONNXRTQLTransforms + neural_compressor.experimental.data.transforms.transform.ONNXRTITTransforms + neural_compressor.experimental.data.transforms.transform.TRANSFORMS + neural_compressor.experimental.data.transforms.transform.BaseTransform + neural_compressor.experimental.data.transforms.transform.TensorflowWrapFunction + neural_compressor.experimental.data.transforms.transform.TensorflowTransform + neural_compressor.experimental.data.transforms.transform.PytorchMxnetWrapFunction + neural_compressor.experimental.data.transforms.transform.PytorchMxnetTransform + neural_compressor.experimental.data.transforms.transform.ComposeTransform + neural_compressor.experimental.data.transforms.transform.CropToBoundingBox + neural_compressor.experimental.data.transforms.transform.MXNetCropToBoundingBox + neural_compressor.experimental.data.transforms.transform.ONNXRTCropToBoundingBox + neural_compressor.experimental.data.transforms.transform.TensorflowCropToBoundingBox + neural_compressor.experimental.data.transforms.transform.ResizeWithRatio + neural_compressor.experimental.data.transforms.transform.TensorflowResizeWithRatio + neural_compressor.experimental.data.transforms.transform.Transpose + neural_compressor.experimental.data.transforms.transform.TensorflowTranspose + neural_compressor.experimental.data.transforms.transform.MXNetTranspose + neural_compressor.experimental.data.transforms.transform.PyTorchTranspose + neural_compressor.experimental.data.transforms.transform.RandomVerticalFlip + neural_compressor.experimental.data.transforms.transform.TensorflowRandomVerticalFlip + neural_compressor.experimental.data.transforms.transform.RandomHorizontalFlip + neural_compressor.experimental.data.transforms.transform.TensorflowRandomHorizontalFlip + neural_compressor.experimental.data.transforms.transform.ToArray + neural_compressor.experimental.data.transforms.transform.CastTFTransform + neural_compressor.experimental.data.transforms.transform.CastONNXTransform + neural_compressor.experimental.data.transforms.transform.CastPyTorchTransform + neural_compressor.experimental.data.transforms.transform.CenterCropTFTransform + neural_compressor.experimental.data.transforms.transform.PaddedCenterCropTransform + neural_compressor.experimental.data.transforms.transform.ResizeTFTransform + neural_compressor.experimental.data.transforms.transform.ResizePytorchTransform + neural_compressor.experimental.data.transforms.transform.RandomCropTFTransform + neural_compressor.experimental.data.transforms.transform.RandomResizedCropPytorchTransform + neural_compressor.experimental.data.transforms.transform.RandomResizedCropMXNetTransform + neural_compressor.experimental.data.transforms.transform.RandomResizedCropTFTransform + neural_compressor.experimental.data.transforms.transform.NormalizeTFTransform + neural_compressor.experimental.data.transforms.transform.RescaleKerasPretrainTransform + neural_compressor.experimental.data.transforms.transform.RescaleTFTransform + neural_compressor.experimental.data.transforms.transform.RescaleTransform + neural_compressor.experimental.data.transforms.transform.AlignImageChannelTransform + neural_compressor.experimental.data.transforms.transform.PyTorchAlignImageChannel + neural_compressor.experimental.data.transforms.transform.ToNDArrayTransform + neural_compressor.experimental.data.transforms.transform.ResizeMXNetTransform + neural_compressor.experimental.data.transforms.transform.ResizeTransform + neural_compressor.experimental.data.transforms.transform.CropResizeTFTransform + neural_compressor.experimental.data.transforms.transform.PyTorchCropResizeTransform + neural_compressor.experimental.data.transforms.transform.MXNetCropResizeTransform + neural_compressor.experimental.data.transforms.transform.CropResizeTransform + neural_compressor.experimental.data.transforms.transform.CenterCropTransform + neural_compressor.experimental.data.transforms.transform.MXNetNormalizeTransform + neural_compressor.experimental.data.transforms.transform.PyTorchNormalizeTransform + neural_compressor.experimental.data.transforms.transform.NormalizeTransform + neural_compressor.experimental.data.transforms.transform.RandomCropTransform + neural_compressor.experimental.data.transforms.transform.RandomResizedCropTransform + neural_compressor.experimental.data.transforms.transform.SquadExample + neural_compressor.experimental.data.transforms.transform.InputFeatures + neural_compressor.experimental.data.transforms.transform.CollectTransform + neural_compressor.experimental.data.transforms.transform.TFSquadV1PostTransform + neural_compressor.experimental.data.transforms.transform.TFModelZooCollectTransform + neural_compressor.experimental.data.transforms.transform.TFSquadV1ModelZooPostTransform + neural_compressor.experimental.data.transforms.transform.ParseDecodeVocTransform + + + +Functions +~~~~~~~~~ + +.. autoapisummary:: + + neural_compressor.experimental.data.transforms.transform.transform_registry + neural_compressor.experimental.data.transforms.transform.get_torchvision_map + neural_compressor.experimental.data.transforms.transform._compute_softmax + neural_compressor.experimental.data.transforms.transform._get_best_indexes + neural_compressor.experimental.data.transforms.transform.get_final_text + neural_compressor.experimental.data.transforms.transform.read_squad_examples + neural_compressor.experimental.data.transforms.transform._check_is_max_context + neural_compressor.experimental.data.transforms.transform.convert_examples_to_features + + + +Attributes +~~~~~~~~~~ + +.. autoapisummary:: + + neural_compressor.experimental.data.transforms.transform.torchvision + neural_compressor.experimental.data.transforms.transform.torch + neural_compressor.experimental.data.transforms.transform.tf + neural_compressor.experimental.data.transforms.transform.mx + neural_compressor.experimental.data.transforms.transform.cv2 + neural_compressor.experimental.data.transforms.transform.framework_transforms + neural_compressor.experimental.data.transforms.transform.TENSORFLOW_TRANSFORMS + neural_compressor.experimental.data.transforms.transform.TENSORFLOW_ITEX_TRANSFORMS + neural_compressor.experimental.data.transforms.transform.MXNET_TRANSFORMS + neural_compressor.experimental.data.transforms.transform.PYTORCH_TRANSFORMS + neural_compressor.experimental.data.transforms.transform.ONNXRT_QL_TRANSFORMS + neural_compressor.experimental.data.transforms.transform.ONNXRT_IT_TRANSFORMS + neural_compressor.experimental.data.transforms.transform.registry_transforms + neural_compressor.experimental.data.transforms.transform.interpolation_map + neural_compressor.experimental.data.transforms.transform.interpolation_pytorch_map + neural_compressor.experimental.data.transforms.transform.interpolation_mxnet_map + neural_compressor.experimental.data.transforms.transform.np_dtype_map + + +.. py:data:: torchvision + + + + +.. py:data:: torch + + + + +.. py:data:: tf + + + + +.. py:data:: mx + + + + +.. py:data:: cv2 + + + + +.. py:class:: Transforms(process, concat_general=True) + + Bases: :py:obj:`object` + + INC supports built-in preprocessing, postprocessing and general methods on different framework backends. + + Transforms base class provides the abstract methods. + Users can also register their own Transforms classes by inheriting this base class. + + .. py:method:: _get_preprocess() + :abstractmethod: + + Abstract method to get preprocessing method. + + + .. py:method:: _get_postprocess() + :abstractmethod: + + Abstract method to get postprocess method. + + + .. py:method:: _get_general() + :abstractmethod: + + Abstract method to get general method. + + + +.. py:class:: TensorflowTransforms(process, concat_general=True) + + Bases: :py:obj:`Transforms` + + Tensorflow Transforms subclass. + + .. py:method:: _get_preprocess() + + Tensorflow get preprocess method. + + :returns: a dict including all the registered preprocess methods + :rtype: preprocess + + + .. py:method:: _get_postprocess() + + Tensorflow get postprocess method. + + :returns: a dict including all the registered postprocess methods + :rtype: postprocess + + + .. py:method:: _get_general() + + Tensorflow get general method. + + :returns: a dict including all the registered general methods + :rtype: general + + + +.. py:class:: MXNetTransforms(process, concat_general=True) + + Bases: :py:obj:`Transforms` + + Mxnet Transforms subclass. + + .. py:method:: _get_preprocess() + + Mxnet get preprocess method. + + :returns: a dict including all the registered preprocess methods + :rtype: preprocess + + + .. py:method:: _get_postprocess() + + Mxnet get postprocess method. + + :returns: a dict including all the registered postprocess methods + :rtype: postprocess + + + .. py:method:: _get_general() + + Mxnet get general method. + + :returns: a dict including all the registered general methods + :rtype: general + + + +.. py:class:: PyTorchTransforms(process, concat_general=True) + + Bases: :py:obj:`Transforms` + + Pytorch Transforms subclass. + + .. py:method:: _get_preprocess() + + Pytorch get preprocessing method. + + :returns: a dict including all the registered preprocess methods + :rtype: preprocess + + + .. py:method:: _get_postprocess() + + Pytorch get postprocess method. + + :returns: a dict including all the registered postprocess methods + :rtype: postprocess + + + .. py:method:: _get_general() + + Pytorch get general method. + + :returns: a dict including all the registered general methods + :rtype: general + + + +.. py:class:: ONNXRTQLTransforms(process, concat_general=True) + + Bases: :py:obj:`Transforms` + + Onnxrt_qlinearops Transforms subclass. + + .. py:method:: _get_preprocess() + + Onnxrt_qlinearops get preprocessing method. + + :returns: a dict including all the registered preprocess methods + :rtype: preprocess + + + .. py:method:: _get_postprocess() + + Onnxrt_qlinearops get postprocess method. + + :returns: a dict including all the registered postprocess methods + :rtype: postprocess + + + .. py:method:: _get_general() + + Onnxrt_qlinearops get general method. + + :returns: a dict including all the registered general methods + :rtype: general + + + +.. py:class:: ONNXRTITTransforms(process, concat_general=True) + + Bases: :py:obj:`Transforms` + + Onnxrt_integerops Transforms subclass. + + .. py:method:: _get_preprocess() + + Onnxrt_integerops get preprocessing method. + + :returns: a dict including all the registered preprocess methods + :rtype: preprocess + + + .. py:method:: _get_postprocess() + + Onnxrt_integerops get postprocess method. + + :returns: a dict including all the registered postprocess methods + :rtype: postprocess + + + .. py:method:: _get_general() + + Onnxrt_integerops get general method. + + :returns: a dict including all the registered general methods + :rtype: general + + + +.. py:data:: framework_transforms + + + + +.. py:data:: TENSORFLOW_TRANSFORMS + + + + +.. py:data:: TENSORFLOW_ITEX_TRANSFORMS + + + + +.. py:data:: MXNET_TRANSFORMS + + + + +.. py:data:: PYTORCH_TRANSFORMS + + + + +.. py:data:: ONNXRT_QL_TRANSFORMS + + + + +.. py:data:: ONNXRT_IT_TRANSFORMS + + + + +.. py:data:: registry_transforms + + + + +.. py:class:: TRANSFORMS(framework, process) + + Bases: :py:obj:`object` + + Transforms collection class. + + Provide register method to register new Transforms + and provide __getitem__ method to get Transforms according to Transforms type. + + .. py:method:: __getitem__(transform_type) + + Get Transform according to Transforms type. + + :param transform_type: the value can be preprocess, postprocess or general + :type transform_type: str + + :returns: the registered Transforms + :rtype: Transforms + + + .. py:method:: register(name, transform_cls) + + Register new Transform according to Transforms type. + + :param name: process name + :type name: str + :param transform_cls: process function wrapper class + :type transform_cls: class + + + +.. py:function:: transform_registry(transform_type, process, framework) + + Class decorator used to register all transform subclasses. + + :param transform_type: Transform registration name + :type transform_type: str + :param process: support 3 process including 'preprocess', 'postprocess', 'general' + :type process: str + :param framework: support 4 framework including 'tensorflow', 'pytorch', 'mxnet', 'onnxrt' + :type framework: str + :param cls: The class of register. + :type cls: class + + :returns: The class of register. + :rtype: cls + + +.. py:class:: BaseTransform + + Bases: :py:obj:`object` + + The base class for transform. + + .. py:method:: __call__(*args, **kwargs) + :abstractmethod: + + __call__ method is needed when write user specific transform. + + + +.. py:class:: TensorflowWrapFunction(transform_func) + + Bases: :py:obj:`object` + + Tensorflow wrapper function class. + + .. py:method:: __call__(**kwargs) + + __call__ method. + + :returns: TensorflowTransform class + + + +.. py:class:: TensorflowTransform(transform_func, **kwargs) + + Bases: :py:obj:`BaseTransform` + + Tensorflow transform class, the subclass of BaseTransform. + + .. py:method:: __call__(sample) + + __call__ method. + + :returns: a tuple of image and lable which get from tensorflow tranform processing + + + +.. py:class:: PytorchMxnetWrapFunction(transform_func) + + Bases: :py:obj:`object` + + Pytorch and MXNet wrapper function class. + + .. py:method:: __call__(**args) + + __call__ method. + + :returns: PytorchMxnetTransform class + + + +.. py:class:: PytorchMxnetTransform(transform_func) + + Bases: :py:obj:`BaseTransform` + + Pytorch and Mxnet transform class, the subclass of BaseTransform. + + .. py:method:: __call__(sample) + + __call__ method. + + :returns: a tuple of image and lable which get from pytorch or mxnet tranform processing + + + +.. py:data:: interpolation_map + + + + +.. py:data:: interpolation_pytorch_map + + + + +.. py:data:: interpolation_mxnet_map + + + + +.. py:function:: get_torchvision_map(interpolation) + + Get torchvision interpolation map. + + +.. py:class:: ComposeTransform(transform_list) + + Bases: :py:obj:`BaseTransform` + + Composes several transforms together. + + :param transform_list: list of transforms to compose + :type transform_list: list of Transform objects + + :returns: tuple of processed image and label + :rtype: sample (tuple) + + .. py:method:: __call__(sample) + + Call transforms in transform_list. + + + +.. py:class:: CropToBoundingBox(offset_height, offset_width, target_height, target_width) + + Bases: :py:obj:`BaseTransform` + + Crops an image to a specified bounding box. + + :param offset_height: Vertical coordinate of the top-left corner of the result in the input + :type offset_height: int + :param offset_width: Horizontal coordinate of the top-left corner of the result in the input + :type offset_width: int + :param target_height: Height of the result + :type target_height: int + :param target_width: Width of the result + :type target_width: int + + :returns: tuple of processed image and label + + .. py:method:: __call__(sample) + + Call torchvision.transforms.functional.crop. + + + +.. py:class:: MXNetCropToBoundingBox(offset_height, offset_width, target_height, target_width) + + Bases: :py:obj:`CropToBoundingBox` + + Crops an image to a specified bounding box. + + :param offset_height: Vertical coordinate of the top-left corner of the result in the input + :type offset_height: int + :param offset_width: Horizontal coordinate of the top-left corner of the result in the input + :type offset_width: int + :param target_height: Height of the result + :type target_height: int + :param target_width: Width of the result + :type target_width: int + + :returns: tuple of processed image and label + + .. py:method:: __call__(sample) + + Call mx.image.fixed_crop. + + + +.. py:class:: ONNXRTCropToBoundingBox(offset_height, offset_width, target_height, target_width) + + Bases: :py:obj:`CropToBoundingBox` + + Crops an image to a specified bounding box. + + :param offset_height: Vertical coordinate of the top-left corner of the result in the input + :type offset_height: int + :param offset_width: Horizontal coordinate of the top-left corner of the result in the input + :type offset_width: int + :param target_height: Height of the result + :type target_height: int + :param target_width: Width of the result + :type target_width: int + + :returns: tuple of processed image and label + + .. py:method:: __call__(sample) + + Crop the image in sample. + + + +.. py:class:: TensorflowCropToBoundingBox(offset_height, offset_width, target_height, target_width) + + Bases: :py:obj:`CropToBoundingBox` + + Crops an image to a specified bounding box. + + :param offset_height: Vertical coordinate of the top-left corner of the result in the input + :type offset_height: int + :param offset_width: Horizontal coordinate of the top-left corner of the result in the input + :type offset_width: int + :param target_height: Height of the result + :type target_height: int + :param target_width: Width of the result + :type target_width: int + + :returns: tuple of processed image and label + + .. py:method:: __call__(sample) + + Crop the image in sample. + + + +.. py:class:: ResizeWithRatio(min_dim=800, max_dim=1365, padding=False, constant_value=0) + + Bases: :py:obj:`BaseTransform` + + Resize image with aspect ratio and pad it to max shape(optional). + + If the image is padded, the label will be processed at the same time. + The input image should be np.array. + + :param min_dim: Resizes the image such that its smaller dimension == min_dim + :type min_dim: int, default=800 + :param max_dim: Ensures that the image longest side doesn't exceed this value + :type max_dim: int, default=1365 + :param padding: If true, pads image with zeros so its size is max_dim x max_dim + :type padding: bool, default=False + + :returns: tuple of processed image and label + + .. py:method:: __call__(sample) + + Resize the image with ratio in sample. + + + +.. py:class:: TensorflowResizeWithRatio(min_dim=800, max_dim=1365, padding=False, constant_value=0) + + Bases: :py:obj:`BaseTransform` + + Resize image with aspect ratio and pad it to max shape(optional). + + If the image is padded, the label will be processed at the same time. + The input image should be np.array or tf.Tensor. + + :param min_dim: Resizes the image such that its smaller dimension == min_dim + :type min_dim: int, default=800 + :param max_dim: Ensures that the image longest side doesn't exceed this value + :type max_dim: int, default=1365 + :param padding: If true, pads image with zeros so its size is max_dim x max_dim + :type padding: bool, default=False + + :returns: tuple of processed image and label + + .. py:method:: __call__(sample) + + Resize the image with ratio in sample. + + + +.. py:class:: Transpose(perm) + + Bases: :py:obj:`BaseTransform` + + Transpose image according to perm. + + :param perm: A permutation of the dimensions of input image + :type perm: list + + :returns: tuple of processed image and label + + .. py:method:: __call__(sample) + + Transpose the image according to perm in sample. + + + +.. py:class:: TensorflowTranspose(perm) + + Bases: :py:obj:`Transpose` + + Transpose image according to perm. + + :param perm: A permutation of the dimensions of input image + :type perm: list + + :returns: tuple of processed image and label + + .. py:method:: __call__(sample) + + Transpose the image according to perm in sample. + + + +.. py:class:: MXNetTranspose(perm) + + Bases: :py:obj:`Transpose` + + Transpose image according to perm. + + :param perm: A permutation of the dimensions of input image + :type perm: list + + :returns: tuple of processed image and label + + .. py:method:: __call__(sample) + + Transpose the image according to perm in sample. + + + +.. py:class:: PyTorchTranspose(perm) + + Bases: :py:obj:`Transpose` + + Transpose image according to perm. + + :param perm: A permutation of the dimensions of input image + :type perm: list + + :returns: tuple of processed image and label + + .. py:method:: __call__(sample) + + Transpose the image according to perm in sample. + + + +.. py:class:: RandomVerticalFlip + + Bases: :py:obj:`BaseTransform` + + Vertically flip the given image randomly. + + :returns: tuple of processed image and label + + .. py:method:: __call__(sample) + + Vertically flip the image in sample. + + + +.. py:class:: TensorflowRandomVerticalFlip + + Bases: :py:obj:`BaseTransform` + + Vertically flip the given image randomly. + + :returns: tuple of processed image and label + + .. py:method:: __call__(sample) + + Vertically flip the image in sample. + + + +.. py:class:: RandomHorizontalFlip + + Bases: :py:obj:`BaseTransform` + + Horizontally flip the given image randomly. + + :returns: tuple of processed image and label + + .. py:method:: __call__(sample) + + Horizontally flip the image in sample. + + + +.. py:class:: TensorflowRandomHorizontalFlip + + Bases: :py:obj:`BaseTransform` + + Horizontally flip the given image randomly. + + :returns: tuple of processed image and label + + .. py:method:: __call__(sample) + + Horizontally flip the image in sample. + + + +.. py:class:: ToArray + + Bases: :py:obj:`BaseTransform` + + Convert PIL Image or NDArray to numpy array. + + :returns: tuple of processed image and label + + .. py:method:: __call__(sample) + + Convert image in sample to numpy array. + + + +.. py:data:: np_dtype_map + + + + +.. py:class:: CastTFTransform(dtype='float32') + + Bases: :py:obj:`BaseTransform` + + Convert image to given dtype. + + :param dtype: A dtype to convert image to + :type dtype: str, default='float32' + + :returns: tuple of processed image and label + + .. py:method:: __call__(sample) + + Convert image in sample to given dtype. + + + +.. py:class:: CastONNXTransform(dtype='float32') + + Bases: :py:obj:`BaseTransform` + + Convert image to given dtype. + + :param dtype: A dtype to convert image to + :type dtype: str, default='float32' + + :returns: tuple of processed image and label + + .. py:method:: __call__(sample) + + Convert image in sample to given dtype. + + + +.. py:class:: CastPyTorchTransform(dtype='float32') + + Bases: :py:obj:`BaseTransform` + + Convert image to given dtype. + + :param dtype: A dtype to convert image to + :type dtype: str, default='float32' + + :returns: tuple of processed image and label + + .. py:method:: __call__(sample) + + Convert image in sample to given dtype. + + + +.. py:class:: CenterCropTFTransform(size) + + Bases: :py:obj:`BaseTransform` + + Crops the given image at the center to the given size. + + :param size: Size of the result + :type size: list or int + + :returns: tuple of processed image and label + + .. py:method:: __call__(sample) + + Crops image in sample to the given size. + + + +.. py:class:: PaddedCenterCropTransform(size, crop_padding=0) + + Bases: :py:obj:`BaseTransform` + + Crops the given image at the center to the given size with padding. + + :param size: Size of the result + :type size: list or int + :param crop_padding: crop padding number + :type crop_padding: int + + :returns: tuple of processed image and label + + .. py:method:: __call__(sample) + + Crops image in sample to the given size with padding. + + + +.. py:class:: ResizeTFTransform(size, interpolation='bilinear') + + Bases: :py:obj:`BaseTransform` + + Resize the input image to the given size. + + :param size: Size of the result + :type size: list or int + :param interpolation: Desired interpolation type, + support 'bilinear', 'nearest', 'bicubic' + :type interpolation: str, default='bilinear' + + :returns: tuple of processed image and label + + .. py:method:: __call__(sample) + + Resize the input image in sample to the given size. + + + +.. py:class:: ResizePytorchTransform(size, interpolation='bilinear') + + Bases: :py:obj:`BaseTransform` + + Resize the input image to the given size. + + :param size: Size of the result + :type size: list or int + :param interpolation: Desired interpolation type, + support 'bilinear', 'nearest', 'bicubic' + :type interpolation: str, default='bilinear' + + :returns: tuple of processed image and label + + .. py:method:: __call__(sample) + + Resize the input image in sample to the given size. + + + +.. py:class:: RandomCropTFTransform(size) + + Bases: :py:obj:`BaseTransform` + + Crop the image at a random location to the given size. + + :param size: Size of the result + :type size: list or tuple or int + + :returns: tuple of processed image and label + + .. py:method:: __call__(sample) + + Crop the image in sample to the given size. + + + +.. py:class:: RandomResizedCropPytorchTransform(size, scale=(0.08, 1.0), ratio=(3.0 / 4.0, 4.0 / 3.0), interpolation='bilinear') + + Bases: :py:obj:`BaseTransform` + + Crop the given image to random size and aspect ratio. + + :param size: Size of the result + :type size: list or int + :param scale: range of size of the origin size cropped + :type scale: tuple or list, default=(0.08, 1.0) + :param ratio: range of aspect ratio of the origin aspect ratio cropped + :type ratio: tuple or list, default=(3. / 4., 4. / 3.) + :param interpolation: Desired interpolation type, support 'bilinear', 'nearest', 'bicubic' + :type interpolation: str, default='bilinear' + + :returns: tuple of processed image and label + + .. py:method:: __call__(sample) + + Crop the image in sample to the random size. + + + +.. py:class:: RandomResizedCropMXNetTransform(size, scale=(0.08, 1.0), ratio=(3.0 / 4.0, 4.0 / 3.0), interpolation='bilinear') + + Bases: :py:obj:`BaseTransform` + + Crop the given image to random size and aspect ratio. + + :param size: Size of the result + :type size: list or int + :param scale: range of size of the origin size cropped + :type scale: tuple or list, default=(0.08, 1.0) + :param ratio: range of aspect ratio of the origin aspect ratio cropped + :type ratio: tuple or list, default=(3. / 4., 4. / 3.) + :param interpolation: Desired interpolation type, support 'bilinear', 'nearest', 'bicubic' + :type interpolation: str, default='bilinear' + + :returns: tuple of processed image and label + + .. py:method:: __call__(sample) + + Crop the image in sample to the random size. + + + +.. py:class:: RandomResizedCropTFTransform(size, scale=(0.08, 1.0), ratio=(3.0 / 4.0, 4.0 / 3.0), interpolation='bilinear') + + Bases: :py:obj:`BaseTransform` + + Crop the given image to random size and aspect ratio. + + :param size: Size of the result + :type size: list or int + :param scale: range of size of the origin size cropped + :type scale: tuple or list, default=(0.08, 1.0) + :param ratio: range of aspect ratio of the origin aspect ratio cropped + :type ratio: tuple or list, default=(3. / 4., 4. / 3.) + :param interpolation: Desired interpolation type, support 'bilinear', 'nearest' + :type interpolation: str, default='bilinear' + + :returns: tuple of processed image and label + + .. py:method:: get_params(image, scale, ratio) + + Get the image prameters: position, height and width. + + + .. py:method:: __call__(sample) + + Crop the image in sample to the random size. + + + +.. py:class:: NormalizeTFTransform(mean=[0.0], std=[1.0], rescale=None) + + Bases: :py:obj:`BaseTransform` + + Normalize a image with mean and standard deviation. + + :param mean: means for each channel, if len(mean)=1, mean will be broadcasted to each channel, + otherwise its length should be same with the length of image shape + :type mean: list, default=[0.0] + :param std: stds for each channel, if len(std)=1, std will be broadcasted to each channel, + otherwise its length should be same with the length of image shape + :type std: list, default=[1.0] + + :returns: tuple of processed image and label + + .. py:method:: __call__(sample) + + Normalize the image in sample. + + + +.. py:class:: RescaleKerasPretrainTransform(rescale=None) + + Bases: :py:obj:`BaseTransform` + + Scale the values of image to [0,1]. + + :returns: tuple of processed image and label + + .. py:method:: __call__(sample) + + Scale the values of the image in sample. + + + +.. py:class:: RescaleTFTransform + + Bases: :py:obj:`BaseTransform` + + Scale the values of image to [0,1]. + + :returns: tuple of processed image and label + + .. py:method:: __call__(sample) + + Scale the values of the image in sample. + + + +.. py:class:: RescaleTransform + + Bases: :py:obj:`BaseTransform` + + Scale the values of image to [0,1]. + + :returns: tuple of processed image and label + + .. py:method:: __call__(sample) + + Scale the values of the image in sample. + + + +.. py:class:: AlignImageChannelTransform(dim=3) + + Bases: :py:obj:`BaseTransform` + + Align image channel, now just support [H,W]->[H,W,dim], [H,W,4]->[H,W,3] and [H,W,3]->[H,W]. + + Input image must be np.ndarray. + + :returns: tuple of processed image and label + + .. py:method:: __call__(sample) + + Align channel of the image in sample. + + + +.. py:class:: PyTorchAlignImageChannel(dim=3) + + Bases: :py:obj:`BaseTransform` + + Align image channel, now just support [H,W,4]->[H,W,3] and [H,W,3]->[H,W]. + + Input image must be PIL Image. + + :returns: tuple of processed image and label + + .. py:method:: __call__(sample) + + Align channel of the image in sample. + + + +.. py:class:: ToNDArrayTransform + + Bases: :py:obj:`BaseTransform` + + Convert np.array to NDArray. + + :returns: tuple of processed image and label + + .. py:method:: __call__(sample) + + Convert np.array of the image in sample. + + + +.. py:class:: ResizeMXNetTransform(size, interpolation='bilinear') + + Bases: :py:obj:`BaseTransform` + + Resize the input image to the given size. + + :param size: Size of the result + :type size: list or int + :param interpolation: Desired interpolation type, + support 'bilinear', 'nearest', 'bicubic' + :type interpolation: str, default='bilinear' + + :returns: tuple of processed image and label + + .. py:method:: __call__(sample) + + Resize the input image in sample to the given size. + + + +.. py:class:: ResizeTransform(size, interpolation='bilinear') + + Bases: :py:obj:`BaseTransform` + + Resize the input image to the given size. + + :param size: Size of the result + :type size: list or int + :param interpolation: Desired interpolation type, + :type interpolation: str, default='bilinear' + :param support 'bilinear': + :param 'nearest': + :param 'bicubic'.: + + :returns: tuple of processed image and label + + .. py:method:: __call__(sample) + + Resize the input image in sample to the given size. + + + +.. py:class:: CropResizeTFTransform(x, y, width, height, size, interpolation='bilinear') + + Bases: :py:obj:`BaseTransform` + + Crop the input image with given location and resize it. + + :param x: Left boundary of the cropping area + :type x: int + :param y: Top boundary of the cropping area + :type y: int + :param width: Width of the cropping area + :type width: int + :param height: Height of the cropping area + :type height: int + :param size: resize to new size after cropping + :type size: list or int + :param interpolation: Desired interpolation type, + support 'bilinear', 'nearest', 'bicubic' + :type interpolation: str, default='bilinear' + + :returns: tuple of processed image and label + + .. py:method:: __call__(sample) + + Resize the input image in sample with given location. + + + +.. py:class:: PyTorchCropResizeTransform(x, y, width, height, size, interpolation='bilinear') + + Bases: :py:obj:`BaseTransform` + + Crop the input image with given location and resize it. + + :param x: Left boundary of the cropping area + :type x: int + :param y: Top boundary of the cropping area + :type y: int + :param width: Width of the cropping area + :type width: int + :param height: Height of the cropping area + :type height: int + :param size: resize to new size after cropping + :type size: list or int + :param interpolation: Desired interpolation type, + support 'bilinear', 'nearest', 'bicubic' + :type interpolation: str, default='bilinear' + + :returns: tuple of processed image and label + + .. py:method:: __call__(sample) + + Resize the input image in sample with given location. + + + +.. py:class:: MXNetCropResizeTransform(x, y, width, height, size, interpolation='bilinear') + + Bases: :py:obj:`BaseTransform` + + Crop the input image with given location and resize it. + + :param x: Left boundary of the cropping area + :type x: int + :param y: Top boundary of the cropping area + :type y: int + :param width: Width of the cropping area + :type width: int + :param height: Height of the cropping area + :type height: int + :param size: resize to new size after cropping + :type size: list or int + :param interpolation: Desired interpolation type, + support 'bilinear', 'nearest', 'bicubic' + :type interpolation: str, default='bilinear' + + :returns: tuple of processed image and label + + .. py:method:: __call__(sample) + + Resize the input image in sample with given location. + + + +.. py:class:: CropResizeTransform(x, y, width, height, size, interpolation='bilinear') + + Bases: :py:obj:`BaseTransform` + + Crop the input image with given location and resize it. + + :param x: Left boundary of the cropping area + :type x: int + :param y: Top boundary of the cropping area + :type y: int + :param width: Width of the cropping area + :type width: int + :param height: Height of the cropping area + :type height: int + :param size: resize to new size after cropping + :type size: list or int + :param interpolation: Desired interpolation type, + support 'bilinear', 'nearest', 'bicubic' + :type interpolation: str, default='bilinear' + + :returns: tuple of processed image and label + + .. py:method:: __call__(sample) + + Crop the input image in sample with given location. + + + +.. py:class:: CenterCropTransform(size) + + Bases: :py:obj:`BaseTransform` + + Crops the given image at the center to the given size. + + :param size: Size of the result + :type size: list or int + + :returns: tuple of processed image and label + + .. py:method:: __call__(sample) + + Crop the input image in sample at the center to the given size. + + + +.. py:class:: MXNetNormalizeTransform(mean=[0.0], std=[1.0]) + + Bases: :py:obj:`BaseTransform` + + Normalize a image with mean and standard deviation. + + :param mean: means for each channel, if len(mean)=1, mean will be broadcasted to each channel, + otherwise its length should be same with the length of image shape + :type mean: list, default=[0.0] + :param std: stds for each channel, if len(std)=1, std will be broadcasted to each channel, + otherwise its length should be same with the length of image shape + :type std: list, default=[1.0] + + :returns: tuple of processed image and label + + .. py:method:: __call__(sample) + + Normalize the image in sample. + + + +.. py:class:: PyTorchNormalizeTransform(mean=[0.0], std=[1.0]) + + Bases: :py:obj:`MXNetNormalizeTransform` + + Normalize a image with mean and standard deviation. + + :param mean: means for each channel, if len(mean)=1, mean will be broadcasted to each channel, + otherwise its length should be same with the length of image shape + :type mean: list, default=[0.0] + :param std: stds for each channel, if len(std)=1, std will be broadcasted to each channel, + otherwise its length should be same with the length of image shape + :type std: list, default=[1.0] + + :returns: tuple of processed image and label + + .. py:method:: __call__(sample) + + Normalize the image in sample. + + + +.. py:class:: NormalizeTransform(mean=[0.0], std=[1.0]) + + Bases: :py:obj:`BaseTransform` + + Normalize a image with mean and standard deviation. + + :param mean: means for each channel, if len(mean)=1, mean will be broadcasted to each channel, + otherwise its length should be same with the length of image shape + :type mean: list, default=[0.0] + :param std: stds for each channel, if len(std)=1, std will be broadcasted to each channel, + otherwise its length should be same with the length of image shape + :type std: list, default=[1.0] + + :returns: tuple of processed image and label + + .. py:method:: __call__(sample) + + Normalize the image in sample. + + + +.. py:class:: RandomCropTransform(size) + + Bases: :py:obj:`BaseTransform` + + Crop the image at a random location to the given size. + + :param size: Size of the result + :type size: list or tuple or int + + :returns: tuple of processed image and label + + .. py:method:: __call__(sample) + + Crop the image in sample to the given size. + + + +.. py:class:: RandomResizedCropTransform(size, scale=(0.08, 1.0), ratio=(3.0 / 4.0, 4.0 / 3.0), interpolation='bilinear') + + Bases: :py:obj:`BaseTransform` + + Crop the given image to random size and aspect ratio. + + :param size: Size of the result + :type size: list or int + :param scale: range of size of the origin size cropped + :type scale: tuple or list, default=(0.08, 1.0) + :param ratio: range of aspect ratio of the origin aspect ratio cropped + :type ratio: tuple or list, default=(3. / 4., 4. / 3.) + :param interpolation: Desired interpolation type, support 'bilinear', 'nearest' + :type interpolation: str, default='bilinear' + + :returns: tuple of processed image and label + + .. py:method:: get_params(image, scale, ratio) + + Get the image prameters: position, height and width. + + + .. py:method:: __call__(sample) + + Crop the image in sample to random size. + + + +.. py:function:: _compute_softmax(scores) + + Compute softmax probability over raw logits. + + +.. py:function:: _get_best_indexes(logits, n_best_size) + + Get the n-best logits from a list. + + +.. py:function:: get_final_text(pred_text, orig_text, do_lower_case) + + Project the tokenized prediction back to the original text. + + +.. py:class:: SquadExample(qas_id, question_text, doc_tokens, orig_answer_text=None, start_position=None, end_position=None, is_impossible=False) + + Bases: :py:obj:`object` + + A single training/test example for simple sequence classification. + + For examples without an answer, the start and end position are -1. + + +.. py:class:: InputFeatures(unique_id, example_index, doc_span_index, tokens, token_to_orig_map, token_is_max_context, input_ids, input_mask, segment_ids, start_position=None, end_position=None, is_impossible=None) + + Bases: :py:obj:`object` + + A single set of features of data. + + +.. py:function:: read_squad_examples(input_file) + + Read a SQuAD json file into a list of SquadExample. + + +.. py:function:: _check_is_max_context(doc_spans, cur_span_index, position) + + Check if this is the 'max context' doc span for the token. + + +.. py:function:: convert_examples_to_features(examples, tokenizer, max_seq_length, doc_stride, max_query_length, output_fn) + + Load a data file into a list of `InputBatch`s. + + +.. py:class:: CollectTransform(length=10833) + + Bases: :py:obj:`BaseTransform` + + Postprocess the predictions, collect data. + + .. py:method:: __call__(sample) + + Collect postprocess data. + + + +.. py:class:: TFSquadV1PostTransform(label_file, vocab_file, n_best_size=20, max_seq_length=384, max_query_length=64, max_answer_length=30, do_lower_case=True, doc_stride=128) + + Bases: :py:obj:`BaseTransform` + + Postprocess the predictions of bert on SQuAD. + + :param label_file: path of label file + :type label_file: str + :param vocab_file: path of vocabulary file + :type vocab_file: str + :param n_best_size: The total number of n-best predictions to generate in nbest_predictions.json + :type n_best_size: int, default=20 + :param max_seq_length: The maximum total input sequence length after WordPiece tokenization. + Sequences longer than this will be truncated, shorter than this will be padded + :type max_seq_length: int, default=384 + :param max_query_length: The maximum number of tokens for the question. + Questions longer than this will be truncated to this length + :type max_query_length: int, default=64 + :param max_answer_length: The maximum length of an answer that can be generated. This is needed because + the start and end predictions are not conditioned on one another + :type max_answer_length: int, default=30 + :param do_lower_case: Whether to lower case the input text. + Should be True for uncased models and False for cased models + :type do_lower_case: bool, default=True + :param doc_stride: When splitting up a long document into chunks, + how much stride to take between chunks + :type doc_stride: int, default=128 + + :returns: tuple of processed prediction and label + + .. py:method:: process_result(results) + + Get the processed results. + + + .. py:method:: get_postprocess_result(sample) + + Get the post processed results. + + + .. py:method:: __call__(sample) + + Call the get_postprocess_result. + + + +.. py:class:: TFModelZooCollectTransform(length=10833) + + Bases: :py:obj:`CollectTransform` + + Postprocess the predictions of model zoo, collect data. + + .. py:method:: __call__(sample) + + Collect postprocess data. + + + +.. py:class:: TFSquadV1ModelZooPostTransform(label_file, vocab_file, n_best_size=20, max_seq_length=384, max_query_length=64, max_answer_length=30, do_lower_case=True, doc_stride=128) + + Bases: :py:obj:`TFSquadV1PostTransform` + + Postprocess the predictions of bert on SQuADV1.1. + + See class TFSquadV1PostTransform for more details + + .. py:method:: __call__(sample) + + Collect data and get postprocess results. + + + +.. py:class:: ParseDecodeVocTransform + + Bases: :py:obj:`BaseTransform` + + Parse features in Example proto. + + :returns: tuple of parsed image and labels + + .. py:method:: __call__(sample) + + Parse decode voc. + + diff --git a/docs/source/io_doc/api-documentation/common/metric.rst b/docs/source/io_doc/api-documentation/common/metric.rst new file mode 100644 index 00000000000..7e7919d2938 --- /dev/null +++ b/docs/source/io_doc/api-documentation/common/metric.rst @@ -0,0 +1,12 @@ +Metric +########### + +The Metric API information is available: + +.. toctree:: + :maxdepth: 1 + + + metrics + bleu + diff --git a/docs/source/io_doc/api-documentation/common/metrics.rst b/docs/source/io_doc/api-documentation/common/metrics.rst new file mode 100644 index 00000000000..0ff2d234203 --- /dev/null +++ b/docs/source/io_doc/api-documentation/common/metrics.rst @@ -0,0 +1,622 @@ +Metric +====================================================== + +.. py:module:: neural_compressor.experimental.metric.metric + +Module Contents +--------------- + +Classes +~~~~~~~ + +.. autoapisummary:: + + neural_compressor.experimental.metric.metric.BaseMetric + neural_compressor.experimental.metric.metric.F1 + neural_compressor.experimental.metric.metric.Accuracy + neural_compressor.experimental.metric.metric.PyTorchLoss + neural_compressor.experimental.metric.metric.Loss + neural_compressor.experimental.metric.metric.MAE + neural_compressor.experimental.metric.metric.RMSE + neural_compressor.experimental.metric.metric.MSE + neural_compressor.experimental.metric.metric.TensorflowTopK + neural_compressor.experimental.metric.metric.GeneralTopK + neural_compressor.experimental.metric.metric.COCOmAPv2 + neural_compressor.experimental.metric.metric.TensorflowMAP + neural_compressor.experimental.metric.metric.TensorflowCOCOMAP + neural_compressor.experimental.metric.metric.TensorflowVOCMAP + neural_compressor.experimental.metric.metric.SquadF1 + neural_compressor.experimental.metric.metric.mIOU + neural_compressor.experimental.metric.metric.ONNXRTGLUE + neural_compressor.experimental.metric.metric.ROC + + + + + +.. py:class:: BaseMetric(metric, single_output=False, hvd=None) + + Bases: :py:obj:`object` + + The base class of Metric. + + .. py:method:: __call__(*args, **kwargs) + + Evaluate the model predictions, and the reference. + + :returns: The class itself. + + + .. py:method:: update(preds, labels=None, sample_weight=None) + :abstractmethod: + + Update the state that need to be evaluated. + + :param preds: The prediction result. + :param labels: The reference. Defaults to None. + :param sample_weight: The sampling weight. Defaults to None. + + :raises NotImplementedError: The method should be implemented by subclass. + + + .. py:method:: reset() + :abstractmethod: + + Clear the predictions and labels. + + :raises NotImplementedError: The method should be implemented by subclass. + + + .. py:method:: result() + :abstractmethod: + + Evaluate the difference between predictions and labels. + + :raises NotImplementedError: The method should be implemented by subclass. + + + .. py:method:: metric() + :property: + + Return its metric class. + + :returns: The metric class. + + + .. py:method:: hvd() + :property: + + Return its hvd class. + + :returns: The hvd class. + + + +.. py:class:: F1 + + Bases: :py:obj:`BaseMetric` + + F1 score of a binary classification problem. + + The F1 score is the harmonic mean of the precision and recall. + It can be computed with the equation: + F1 = 2 * (precision * recall) / (precision + recall) + + .. py:method:: update(preds, labels) + + Add the predictions and labels. + + :param preds: The predictions. + :param labels: The labels corresponding to the predictions. + + + .. py:method:: reset() + + Clear the predictions and labels. + + + .. py:method:: result() + + Compute the F1 score. + + + + + + + + +.. py:class:: Accuracy + + Bases: :py:obj:`BaseMetric` + + The Accuracy for the classification tasks. + + The accuracy score is the proportion of the total number of predictions + that were correct classified. + + .. attribute:: pred_list + + List of prediction to score. + + .. attribute:: label_list + + List of labels to score. + + .. attribute:: sample + + The total number of samples. + + .. py:method:: update(preds, labels, sample_weight=None) + + Add the predictions and labels. + + :param preds: The predictions. + :param labels: The labels corresponding to the predictions. + :param sample_weight: The sample weight. + + + .. py:method:: reset() + + Clear the predictions and labels. + + + .. py:method:: result() + + Compute the accuracy. + + + +.. py:class:: PyTorchLoss + + A dummy PyTorch Metric. + + A dummy metric that computes the average of predictions and prints it directly. + + .. py:method:: reset() + + Reset the number of samples and total cases to zero. + + + .. py:method:: update(output) + + Add the predictions. + + :param output: The predictions. + + + .. py:method:: compute() + + Compute the average of predictions. + + :raises ValueError: There must have at least one example. + + :returns: The dummy loss. + + + +.. py:class:: Loss + + Bases: :py:obj:`BaseMetric` + + A dummy Metric. + + A dummy metric that computes the average of predictions and prints it directly. + + .. attribute:: sample + + The number of samples. + + .. attribute:: sum + + The sum of prediction. + + .. py:method:: update(preds, labels, sample_weight=None) + + Add the predictions and labels. + + :param preds: The predictions. + :param labels: The labels corresponding to the predictions. + :param sample_weight: The sample weight. + + + .. py:method:: reset() + + Reset the number of samples and total cases to zero. + + + .. py:method:: result() + + Compute the average of predictions. + + :returns: The dummy loss. + + + +.. py:class:: MAE(compare_label=True) + + Bases: :py:obj:`BaseMetric` + + Computes Mean Absolute Error (MAE) loss. + + Mean Absolute Error (MAE) is the mean of the magnitude of + difference between the predicted and actual numeric values. + + .. attribute:: pred_list + + List of prediction to score. + + .. attribute:: label_list + + List of references corresponding to the prediction result. + + .. attribute:: compare_label + + Whether to compare label. False if there are no + labels and will use FP32 preds as labels. + + :type: bool + + .. py:method:: update(preds, labels, sample_weight=None) + + Add the predictions and labels. + + :param preds: The predictions. + :param labels: The labels corresponding to the predictions. + :param sample_weight: The sample weight. + + + .. py:method:: reset() + + Clear the predictions and labels. + + + .. py:method:: result() + + Compute the MAE score. + + :returns: The MAE score. + + + +.. py:class:: RMSE(compare_label=True) + + Bases: :py:obj:`BaseMetric` + + Computes Root Mean Squared Error (RMSE) loss. + + .. attribute:: mse + + The instance of MSE Metric. + + .. py:method:: update(preds, labels, sample_weight=None) + + Add the predictions and labels. + + :param preds: The predictions. + :param labels: The labels corresponding to the predictions. + :param sample_weight: The sample weight. + + + .. py:method:: reset() + + Clear the predictions and labels. + + + .. py:method:: result() + + Compute the RMSE score. + + :returns: The RMSE score. + + + +.. py:class:: MSE(compare_label=True) + + Bases: :py:obj:`BaseMetric` + + Computes Mean Squared Error (MSE) loss. + + Mean Squared Error(MSE) represents the average of the squares of errors. + For example, the average squared difference between the estimated values + and the actual values. + + .. attribute:: pred_list + + List of prediction to score. + + .. attribute:: label_list + + List of references corresponding to the prediction result. + + .. attribute:: compare_label + + Whether to compare label. False if there are no labels + and will use FP32 preds as labels. + + :type: bool + + .. py:method:: update(preds, labels, sample_weight=None) + + Add the predictions and labels. + + :param preds: The predictions. + :param labels: The labels corresponding to the predictions. + :param sample_weight: The sample weight. + + + .. py:method:: reset() + + Clear the predictions and labels. + + + .. py:method:: result() + + Compute the MSE score. + + :returns: The MSE score. + + + +.. py:class:: TensorflowTopK(k=1) + + Bases: :py:obj:`BaseMetric` + + Compute Top-k Accuracy classification score for Tensorflow model. + + This metric computes the number of times where the correct label is among + the top k labels predicted. + + .. attribute:: k + + The number of most likely outcomes considered to find the correct label. + + :type: int + + .. attribute:: num_correct + + The number of predictions that were correct classified. + + .. attribute:: num_sample + + The total number of predictions. + + .. py:method:: update(preds, labels, sample_weight=None) + + Add the predictions and labels. + + :param preds: The predictions. + :param labels: The labels corresponding to the predictions. + :param sample_weight: The sample weight. + + + .. py:method:: reset() + + Reset the number of samples and correct predictions. + + + .. py:method:: result() + + Compute the top-k score. + + :returns: The top-k score. + + + +.. py:class:: GeneralTopK(k=1) + + Bases: :py:obj:`BaseMetric` + + Compute Top-k Accuracy classification score. + + This metric computes the number of times where the correct label is among + the top k labels predicted. + + .. attribute:: k + + The number of most likely outcomes considered to find the correct label. + + :type: int + + .. attribute:: num_correct + + The number of predictions that were correct classified. + + .. attribute:: num_sample + + The total number of predictions. + + .. py:method:: update(preds, labels, sample_weight=None) + + Add the predictions and labels. + + :param preds: The predictions. + :param labels: The labels corresponding to the predictions. + :param sample_weight: The sample weight. + + + .. py:method:: reset() + + Reset the number of samples and correct predictions. + + + .. py:method:: result() + + Compute the top-k score. + + :returns: The top-k score. + + + +.. py:class:: COCOmAPv2(anno_path=None, iou_thrs='0.5:0.05:0.95', map_points=101, map_key='DetectionBoxes_Precision/mAP', output_index_mapping={'num_detections': -1, 'boxes': 0, 'scores': 1, 'classes': 2}) + + Bases: :py:obj:`BaseMetric` + + Compute mean average precision of the detection task. + + .. py:method:: update(predicts, labels, sample_weight=None) + + Add the predictions and labels. + + :param predicts: The predictions. + :param labels: The labels corresponding to the predictions. + :param sample_weight: The sample weight. Defaults to None. + + + .. py:method:: reset() + + Reset the prediction and labels. + + + .. py:method:: result() + + Compute mean average precision. + + :returns: The mean average precision score. + + + +.. py:class:: TensorflowMAP(anno_path=None, iou_thrs=0.5, map_points=0, map_key='DetectionBoxes_Precision/mAP') + + Bases: :py:obj:`BaseMetric` + + Computes mean average precision. + + .. py:method:: update(predicts, labels, sample_weight=None) + + Add the predictions and labels. + + :param predicts: The predictions. + :param labels: The labels corresponding to the predictions. + :param sample_weight: The sample weight. + + + .. py:method:: reset() + + Reset the prediction and labels. + + + .. py:method:: result() + + Compute mean average precision. + + :returns: The mean average precision score. + + + +.. py:class:: TensorflowCOCOMAP(anno_path=None, iou_thrs=None, map_points=None, map_key='DetectionBoxes_Precision/mAP') + + Bases: :py:obj:`TensorflowMAP` + + Computes mean average precision using algorithm in COCO. + + +.. py:class:: TensorflowVOCMAP(anno_path=None, iou_thrs=None, map_points=None, map_key='DetectionBoxes_Precision/mAP') + + Bases: :py:obj:`TensorflowMAP` + + Computes mean average precision using algorithm in VOC. + + +.. py:class:: SquadF1 + + Bases: :py:obj:`BaseMetric` + + Evaluate for v1.1 of the SQuAD dataset. + + .. py:method:: update(preds, labels, sample_weight=None) + + Add the predictions and labels. + + :param preds: The predictions. + :param labels: The labels corresponding to the predictions. + :param sample_weight: The sample weight. + + + .. py:method:: reset() + + Reset the score list. + + + .. py:method:: result() + + Compute F1 score. + + + +.. py:class:: mIOU(num_classes=21) + + Bases: :py:obj:`BaseMetric` + + Compute the mean IOU(Intersection over Union) score. + + .. py:method:: update(preds, labels) + + Add the predictions and labels. + + :param preds: The predictions. + :param labels: The labels corresponding to the predictions. + + + .. py:method:: reset() + + Reset the hist. + + + .. py:method:: result() + + Compute mean IOU. + + :returns: The mean IOU score. + + + +.. py:class:: ONNXRTGLUE(task='mrpc') + + Bases: :py:obj:`BaseMetric` + + Compute the GLUE score. + + .. py:method:: update(preds, labels) + + Add the predictions and labels. + + :param preds: The predictions. + :param labels: The labels corresponding to the predictions. + + + .. py:method:: reset() + + Reset the prediction and labels. + + + .. py:method:: result() + + Compute the GLUE score. + + + +.. py:class:: ROC(task='dlrm') + + Bases: :py:obj:`BaseMetric` + + Computes ROC score. + + .. py:method:: update(preds, labels) + + Add the predictions and labels. + + :param preds: The predictions. + :param labels: The labels corresponding to the predictions. + + + .. py:method:: reset() + + Reset the prediction and labels. + + + .. py:method:: result() + + Compute the ROC score. + + diff --git a/docs/source/io_doc/api-documentation/common/model.rst b/docs/source/io_doc/api-documentation/common/model.rst new file mode 100644 index 00000000000..3f55747449f --- /dev/null +++ b/docs/source/io_doc/api-documentation/common/model.rst @@ -0,0 +1,7 @@ +.. _quantization-api + +Model +############ + +.. .. automodule:: neural_compressor.quantization +.. :members: \ No newline at end of file diff --git a/docs/source/io_doc/api-documentation/common/optimizer.rst b/docs/source/io_doc/api-documentation/common/optimizer.rst new file mode 100644 index 00000000000..655522a8c3a --- /dev/null +++ b/docs/source/io_doc/api-documentation/common/optimizer.rst @@ -0,0 +1,203 @@ +Optimizer +========================================================= + +.. py:module:: neural_compressor.experimental.common.optimizer + +.. autoapi-nested-parse:: + + Intel Neural Compressor built-in Optimizers on multiple framework backends. + + +Module Contents +--------------- + +Classes +~~~~~~~ + +.. autoapisummary:: + + neural_compressor.experimental.common.optimizer.TensorflowOptimizers + neural_compressor.experimental.common.optimizer.PyTorchOptimizers + neural_compressor.experimental.common.optimizer.Optimizers + neural_compressor.experimental.common.optimizer.TensorFlowSGD + neural_compressor.experimental.common.optimizer.TensorFlowAdamW + neural_compressor.experimental.common.optimizer.TensorFlowAdam + neural_compressor.experimental.common.optimizer.PyTorchSGD + + + +Functions +~~~~~~~~~ + +.. autoapisummary:: + + neural_compressor.experimental.common.optimizer.optimizer_registry + + + +Attributes +~~~~~~~~~~ + +.. autoapisummary:: + + neural_compressor.experimental.common.optimizer.torch + neural_compressor.experimental.common.optimizer.tf + neural_compressor.experimental.common.optimizer.tfa + neural_compressor.experimental.common.optimizer.framework_optimizers + neural_compressor.experimental.common.optimizer.TENSORFLOW_OPTIMIZERS + neural_compressor.experimental.common.optimizer.PYTORCH_OPTIMIZERS + neural_compressor.experimental.common.optimizer.registry_optimizers + + +.. py:data:: torch + + + + +.. py:data:: tf + + + + +.. py:data:: tfa + + + + +.. py:class:: TensorflowOptimizers + + Bases: :py:obj:`object` + + Class to get all registered TensorFlow Optimizers once only. + + +.. py:class:: PyTorchOptimizers + + Bases: :py:obj:`object` + + Class to get all registered PyTorch Optimizers once only. + + +.. py:data:: framework_optimizers + + + + +.. py:data:: TENSORFLOW_OPTIMIZERS + + + + +.. py:data:: PYTORCH_OPTIMIZERS + + + + +.. py:data:: registry_optimizers + + + + +.. py:class:: Optimizers(framework) + + Bases: :py:obj:`object` + + Main entry to get the specific type of optimizer. + + .. py:method:: __getitem__(optimizer_type) + + Return the specific type of optimizer object according to the given optimizer_type. + + + .. py:method:: register(name, optimizer_cls) + + Allow registration of non-built-in optimizers. + + + +.. py:function:: optimizer_registry(optimizer_type, framework) + + Class decorator used to register all Optimizer subclasses. + + Cross framework optimizer is supported by add param as framework='tensorflow, pytorch' + + :param optimizer_type: The string of supported criterion. + :type optimizer_type: str + :param framework: The string of supported framework. + :type framework: str + + :returns: The class of register. + :rtype: cls + + +.. py:class:: TensorFlowSGD(param_dict) + + Bases: :py:obj:`object` + + TensorFlow keras SGD optimizer. + + :param param_dict: The dict of parameters setting by user for SGD optimizer + :type param_dict: dict + + .. py:method:: _mapping() + + + .. py:method:: __call__(**kwargs) + + Call `TensorFlowSGD` object. + + + +.. py:class:: TensorFlowAdamW(param_dict) + + Bases: :py:obj:`object` + + tensorflow_addons AdamW optimizer. + + :param param_dict: The dict of parameters setting by user for AdamW optimizer + :type param_dict: dict + + .. py:method:: _mapping() + + + .. py:method:: __call__(**kwargs) + + Call `TensorFlowAdamW` object. + + + +.. py:class:: TensorFlowAdam(param_dict) + + Bases: :py:obj:`object` + + tensorflow Adam optimizer. + + :param param_dict: The dict of parameters setting by user for Adam optimizer + :type param_dict: dict + + .. py:method:: _mapping() + + + .. py:method:: __call__(**kwargs) + + Call `TensorFlowAdam` object. + + + +.. py:class:: PyTorchSGD(param_dict) + + Bases: :py:obj:`object` + + PyTorch SGD optimizer. + + :param param_dict: The dict of parameters setting by user for SGD optimizer + :type param_dict: dict + + .. py:method:: _mapping() + + + .. py:method:: __call__(**kwargs) + + Call `PyTorchSGD` object. + + diff --git a/docs/source/io_doc/api-documentation/component.rst b/docs/source/io_doc/api-documentation/component.rst new file mode 100644 index 00000000000..825a3d19588 --- /dev/null +++ b/docs/source/io_doc/api-documentation/component.rst @@ -0,0 +1,15 @@ +Component(old API) +#################### + +The component API information is available: + +.. toctree:: + :maxdepth: 1 + + component/Quantization + component/Pruning + component/Distillation + component/Scheduler + component/MixedPrecision + component/ModelConversion + component/Nas \ No newline at end of file diff --git a/docs/source/io_doc/api-documentation/component/Benchmark.rst b/docs/source/io_doc/api-documentation/component/Benchmark.rst new file mode 100644 index 00000000000..d831ccef703 --- /dev/null +++ b/docs/source/io_doc/api-documentation/component/Benchmark.rst @@ -0,0 +1,13 @@ +.. _data-api + +Benchmark +######### + +.. .. automodule:: neural_compressor.objective +.. :members: + +.. .. autoclass:: neural_compressor.objective.Measurer +.. :members: + +.. .. autoclass:: neural_compressor.objective.FootprintMeasure +.. :members: \ No newline at end of file diff --git a/docs/source/io_doc/api-documentation/component/Distillation.rst b/docs/source/io_doc/api-documentation/component/Distillation.rst new file mode 100644 index 00000000000..0a453401d5e --- /dev/null +++ b/docs/source/io_doc/api-documentation/component/Distillation.rst @@ -0,0 +1,13 @@ +.. _data-api + +Distillation +############# + +.. .. automodule:: neural_compressor.objective +.. :members: + +.. .. autoclass:: neural_compressor.objective.Measurer +.. :members: + +.. .. autoclass:: neural_compressor.objective.FootprintMeasure +.. :members: \ No newline at end of file diff --git a/docs/source/io_doc/api-documentation/component/MixedPrecision.rst b/docs/source/io_doc/api-documentation/component/MixedPrecision.rst new file mode 100644 index 00000000000..9341c8fd24e --- /dev/null +++ b/docs/source/io_doc/api-documentation/component/MixedPrecision.rst @@ -0,0 +1,13 @@ +.. _data-api + +MixedPrecision +############### + +.. .. automodule:: neural_compressor.objective +.. :members: + +.. .. autoclass:: neural_compressor.objective.Measurer +.. :members: + +.. .. autoclass:: neural_compressor.objective.FootprintMeasure +.. :members: \ No newline at end of file diff --git a/docs/source/io_doc/api-documentation/component/ModelConversion.rst b/docs/source/io_doc/api-documentation/component/ModelConversion.rst new file mode 100644 index 00000000000..03552e260df --- /dev/null +++ b/docs/source/io_doc/api-documentation/component/ModelConversion.rst @@ -0,0 +1,13 @@ +.. _data-api + +ModelConversion +################ + +.. .. automodule:: neural_compressor.objective +.. :members: + +.. .. autoclass:: neural_compressor.objective.Measurer +.. :members: + +.. .. autoclass:: neural_compressor.objective.FootprintMeasure +.. :members: \ No newline at end of file diff --git a/docs/source/io_doc/api-documentation/component/Nas.rst b/docs/source/io_doc/api-documentation/component/Nas.rst new file mode 100644 index 00000000000..0488db0aae3 --- /dev/null +++ b/docs/source/io_doc/api-documentation/component/Nas.rst @@ -0,0 +1,120 @@ +NAS +============================================ + +.. py:module:: neural_compressor.experimental.nas + + +.. autoapisummary:: + + neural_compressor.experimental.nas.basic_nas + neural_compressor.experimental.nas.dynas + + +Package Contents +---------------- + +Classes +~~~~~~~ + +.. autoapisummary:: + + neural_compressor.experimental.nas.BasicNAS + neural_compressor.experimental.nas.DyNAS + neural_compressor.experimental.nas.NAS + + + + +.. py:class:: BasicNAS(conf_fname_or_obj, search_space=None, model_builder=None) + + Bases: :py:obj:`neural_compressor.experimental.nas.nas.NASBase`, :py:obj:`neural_compressor.experimental.component.Component` + + :param conf_fname: The path to the YAML configuration file. + :type conf_fname: string + :param search_space: A dictionary for defining the search space. + :type search_space: dict + :param model_builder: A function to build model instance with the specified + model architecture parameters. + :type model_builder: function obj + + .. py:method:: execute() + + Initialize the dataloader and train/eval functions from yaml config. + Component base class provides default function to initialize dataloaders and functions + from user config. And for derived classes(Pruning, Quantization, etc.), an override + function is required. + + + .. py:method:: estimate(model) + + Estimate performance of the model. + Depends on specific NAS algorithm. Here we use train and evaluate. + + :returns: Evaluated metrics of the model. + + + .. py:method:: init_by_cfg(conf_fname_or_obj) + + + .. py:method:: pre_process() + + Initialize the dataloader and train/eval functions from yaml config. + Component base class provides default function to initialize dataloaders and functions + from user config. And for derived classes(Pruning, Quantization, etc.), an override + function is required. + + + .. py:method:: __repr__() + + Return repr(self). + + + +.. py:class:: DyNAS(conf_fname_or_obj) + + Bases: :py:obj:`neural_compressor.experimental.nas.nas.NASBase` + + :param conf_fname_or_obj: The path to the YAML configuration file or the object of NASConfig. + :type conf_fname_or_obj: string or obj + + .. py:method:: estimate(individual) + + Estimate performance of the model. Depends on specific NAS algorithm. + + :returns: Evaluated metrics of the model. + + + .. py:method:: init_for_search() + + + .. py:method:: search() + + NAS search process. + + :returns: Best model architecture found in search process. + + + .. py:method:: select_model_arch() + + Propose architecture of the model based on search algorithm for next search iteration. + + :returns: Model architecture description. + + + .. py:method:: create_acc_predictor() + + + .. py:method:: create_macs_predictor() + + + .. py:method:: create_latency_predictor() + + + .. py:method:: init_cfg(conf_fname_or_obj) + + + +.. py:class:: NAS + + Bases: :py:obj:`object` + diff --git a/docs/source/io_doc/api-documentation/component/Pruning.rst b/docs/source/io_doc/api-documentation/component/Pruning.rst new file mode 100644 index 00000000000..a49e019381a --- /dev/null +++ b/docs/source/io_doc/api-documentation/component/Pruning.rst @@ -0,0 +1,13 @@ +.. _data-api + +Pruning +######### + +.. .. automodule:: neural_compressor.objective +.. :members: + +.. .. autoclass:: neural_compressor.objective.Measurer +.. :members: + +.. .. autoclass:: neural_compressor.objective.FootprintMeasure +.. :members: \ No newline at end of file diff --git a/docs/source/io_doc/api-documentation/component/Quantization.rst b/docs/source/io_doc/api-documentation/component/Quantization.rst new file mode 100644 index 00000000000..6a2c5cfc985 --- /dev/null +++ b/docs/source/io_doc/api-documentation/component/Quantization.rst @@ -0,0 +1,13 @@ +.. _data-api + +Quantization +############# + +.. .. automodule:: neural_compressor.objective +.. :members: + +.. .. autoclass:: neural_compressor.objective.Measurer +.. :members: + +.. .. autoclass:: neural_compressor.objective.FootprintMeasure +.. :members: \ No newline at end of file diff --git a/docs/source/io_doc/api-documentation/component/Scheduler.rst b/docs/source/io_doc/api-documentation/component/Scheduler.rst new file mode 100644 index 00000000000..0c9942106ae --- /dev/null +++ b/docs/source/io_doc/api-documentation/component/Scheduler.rst @@ -0,0 +1,13 @@ +.. _data-api + +scheduler +########## + +.. .. automodule:: neural_compressor.objective +.. :members: + +.. .. autoclass:: neural_compressor.objective.Measurer +.. :members: + +.. .. autoclass:: neural_compressor.objective.FootprintMeasure +.. :members: \ No newline at end of file diff --git a/docs/source/io_doc/api-documentation/pythonic.rst b/docs/source/io_doc/api-documentation/pythonic.rst new file mode 100644 index 00000000000..77514870ff7 --- /dev/null +++ b/docs/source/io_doc/api-documentation/pythonic.rst @@ -0,0 +1,8 @@ +Pythonic +########### + +The Pythonic API information is available: + +.. toctree:: + :maxdepth: 1 + diff --git a/docs/source/io_doc/api-documentation/strategy.rst b/docs/source/io_doc/api-documentation/strategy.rst new file mode 100644 index 00000000000..db96e96607d --- /dev/null +++ b/docs/source/io_doc/api-documentation/strategy.rst @@ -0,0 +1,9 @@ +strategy +########### + +The strategy API information is available: + +.. toctree:: + :maxdepth: 1 + + diff --git a/docs/api-introduction.md b/docs/source/io_doc/api-introduction.md similarity index 100% rename from docs/api-introduction.md rename to docs/source/io_doc/api-introduction.md diff --git a/docs/backend_quant.md b/docs/source/io_doc/backend_quant.md similarity index 100% rename from docs/backend_quant.md rename to docs/source/io_doc/backend_quant.md diff --git a/docs/bench.md b/docs/source/io_doc/bench.md similarity index 100% rename from docs/bench.md rename to docs/source/io_doc/bench.md diff --git a/docs/benchmark.md b/docs/source/io_doc/benchmark.md similarity index 100% rename from docs/benchmark.md rename to docs/source/io_doc/benchmark.md diff --git a/docs/contributions.md b/docs/source/io_doc/contributions.md similarity index 100% rename from docs/contributions.md rename to docs/source/io_doc/contributions.md diff --git a/docs/dataloader.md b/docs/source/io_doc/dataloader.md similarity index 100% rename from docs/dataloader.md rename to docs/source/io_doc/dataloader.md diff --git a/docs/dataset.md b/docs/source/io_doc/dataset.md similarity index 100% rename from docs/dataset.md rename to docs/source/io_doc/dataset.md diff --git a/docs/design.md b/docs/source/io_doc/design.md similarity index 100% rename from docs/design.md rename to docs/source/io_doc/design.md diff --git a/docs/distillation.md b/docs/source/io_doc/distillation.md similarity index 98% rename from docs/distillation.md rename to docs/source/io_doc/distillation.md index b5f363a5a67..1c4f248fcd4 100644 --- a/docs/distillation.md +++ b/docs/source/io_doc/distillation.md @@ -1,138 +1,138 @@ -Distillation -============ - -1. [Introduction](#introduction) - - 1.1. [Knowledge Distillation](#knowledge-distillation) - - 1.2. [Intermediate Layer Knowledge Distillation](#intermediate-layer-knowledge-distillation) - - 1.3. [Self Distillation](#self-distillation) - -2. [Distillation Support Matrix](#distillation-support-matrix) -3. [Get Started with Distillation API ](#get-started-with-distillation-api) -4. [Examples](#examples) - -## Introduction - -Distillation is one of popular approaches of network compression, which transfers knowledge from a large model to a smaller one without loss of validity. As smaller models are less expensive to evaluate, they can be deployed on less powerful hardware (such as a mobile device). Graph shown below is the workflow of the distillation, the teacher model will take the same input that feed into the student model to produce the output that contains knowledge of the teacher model to instruct the student model. -
- -Architecture - -Intel® Neural Compressor supports Knowledge Distillation and Intermediate Layer Knowledge Distillation algorithms. - -### Knowledge Distillation -Knowledge distillation is proposed in [Distilling the Knowledge in a Neural Network](https://arxiv.org/abs/1503.02531). It leverages the logits (the input of softmax in the classification tasks) of teacher and student model to minimize the the difference between their predicted class distributions, this can be done by minimizing the below loss function. - -$$L_{KD} = D(z_t, z_s)$$ - -Where $D$ is a distance measurement, e.g. Euclidean distance and Kullback–Leibler divergence, $z_t$ and $z_s$ are the logits of teacher and student model, or predicted distributions from softmax of the logits in case the distance is measured in terms of distribution. - -### Intermediate Layer Knowledge Distillation - -There are more information contained in the teacher model beside its logits, for example, the output features of the teacher model's intermediate layers often been used to guide the student model, as in [Patient Knowledge Distillation for BERT Model Compression](https://arxiv.org/pdf/1908.09355) and [MobileBERT: a Compact Task-Agnostic BERT for Resource-Limited Devices](https://arxiv.org/abs/2004.02984). The general loss function for this approach can be summarized as follow. - -$$L_{KD} = \sum\limits_i D(T_t^{n_i}(F_t^{n_i}), T_s^{m_i}(F_s^{m_i}))$$ - -Where $D$ is a distance measurement as before, $F_t^{n_i}$ the output feature of the $n_i$'s layer of the teacher model, $F_s^{m_i}$ the output feature of the $m_i$'s layer of the student model. Since the dimensions of $F_t^{n_i}$ and $F_s^{m_i}$ are usually different, the transformations $T_t^{n_i}$ and $T_s^{m_i}$ are needed to match dimensions of the two features. Specifically, the transformation can take the forms like identity, linear transformation, 1X1 convolution etc. - -### Self Distillation - -Self-distillation ia a one-stage training method where the teacher model and student models can be trained together. It attaches several attention modules and shallow classifiers at different depths of neural networks and distills knowledge from the deepest classifier to the shallower classifiers. Different from the conventional knowledge distillation methods where the knowledge of the teacher model is transferred to another student model, self-distillation can be considered as knowledge transfer in the same model, from the deeper layers to the shallower layers. -The additional classifiers in self-distillation allow the neural network to work in a dynamic manner, which leads to a much higher acceleration. -
- -Architecture - -Architecture from paper [Self-Distillation: Towards Efficient and Compact Neural Networks](https://ieeexplore.ieee.org/document/9381661) - -## Distillation Support Matrix - -|Distillation Algorithm |PyTorch |TensorFlow | -|------------------------------------------------|:--------:|:---------:| -|Knowledge Distillation |✔ |✔ | -|Intermediate Layer Knowledge Distillation |✔ |Will be supported| -|Self Distillation |✔ |✖ | - -## Get Started with Distillation API - -Simplest launcher code if training behavior is defined in user-defined yaml. - -```python -from neural_compressor.experimental import Distillation, common -distiller = Distillation('/path/to/user/yaml') -distiller.student_model = student_model -distiller.teacher_model = teacher_model -model = distiller.fit() -``` -Distillation class also support DistillationConf class as it's argument. - -```python -from neural_compressor.experimental import Distillation, common -from neural_compressor.conf.config import DistillationConf -conf = DistillationConf('/path/to/user/yaml') -distiller = Distillation(conf) -distiller.student_model = student_model -distiller.teacher_model = teacher_model -model = distiller.fit() -``` - -User can pass the customized training/evaluation functions to `Distillation` for flexible scenarios. In this case, distillation process can be done by pre-defined hooks in Neural Compressor. User needs to put those hooks inside the training function. - -Neural Compressor defines several hooks for user pass - -``` -on_train_begin() : Hook executed before training begins -on_after_compute_loss(input, student_output, student_loss) : Hook executed after each batch inference of student model -on_epoch_end() : Hook executed at each epoch end -``` - -Following section shows how to use hooks in user pass-in training function which is part of example from BlendCNN distillation: - -```python -def train_func(model): - distiller.on_train_begin() - for nepoch in range(epochs): - model.train() - cnt = 0 - loss_sum = 0. - iter_bar = tqdm(train_dataloader, desc='Iter (loss=X.XXX)') - for batch in iter_bar: - teacher_logits, input_ids, segment_ids, input_mask, target = batch - cnt += 1 - output = model(input_ids, segment_ids, input_mask) - loss = criterion(output, target) - loss = distiller.on_after_compute_loss( - {'input_ids':input_ids, 'segment_ids':segment_ids, 'input_mask':input_mask}, - output, - loss, - teacher_logits) - optimizer.zero_grad() - loss.backward() - optimizer.step() - if cnt >= iters: - break - print('Average Loss: {}'.format(loss_sum / cnt)) - distiller.on_epoch_end() -... -``` - -In this case, the launcher code is like the following: - -```python -from neural_compressor.experimental import Distillation, common -from neural_compressor.experimental.common.criterion import PyTorchKnowledgeDistillationLoss -distiller = Distillation(args.config) -distiller.student_model = model -distiller.teacher_model = teacher -distiller.criterion = PyTorchKnowledgeDistillationLoss() -distiller.train_func = train_func -model = distiller.fit() -``` - -## Examples - -[Distillation Examples](../examples/README.md#distillation) -
-[Distillation Examples Results](./validated_model_list.md#validated-knowledge-distillation-examples) +Distillation +============ + +1. [Introduction](#introduction) + + 1.1. [Knowledge Distillation](#knowledge-distillation) + + 1.2. [Intermediate Layer Knowledge Distillation](#intermediate-layer-knowledge-distillation) + + 1.3. [Self Distillation](#self-distillation) + +2. [Distillation Support Matrix](#distillation-support-matrix) +3. [Get Started with Distillation API ](#get-started-with-distillation-api) +4. [Examples](#examples) + +## Introduction + +Distillation is one of popular approaches of network compression, which transfers knowledge from a large model to a smaller one without loss of validity. As smaller models are less expensive to evaluate, they can be deployed on less powerful hardware (such as a mobile device). Graph shown below is the workflow of the distillation, the teacher model will take the same input that feed into the student model to produce the output that contains knowledge of the teacher model to instruct the student model. +
+ +Architecture + +Intel® Neural Compressor supports Knowledge Distillation and Intermediate Layer Knowledge Distillation algorithms. + +### Knowledge Distillation +Knowledge distillation is proposed in [Distilling the Knowledge in a Neural Network](https://arxiv.org/abs/1503.02531). It leverages the logits (the input of softmax in the classification tasks) of teacher and student model to minimize the the difference between their predicted class distributions, this can be done by minimizing the below loss function. + +$$L_{KD} = D(z_t, z_s)$$ + +Where $D$ is a distance measurement, e.g. Euclidean distance and Kullback–Leibler divergence, $z_t$ and $z_s$ are the logits of teacher and student model, or predicted distributions from softmax of the logits in case the distance is measured in terms of distribution. + +### Intermediate Layer Knowledge Distillation + +There are more information contained in the teacher model beside its logits, for example, the output features of the teacher model's intermediate layers often been used to guide the student model, as in [Patient Knowledge Distillation for BERT Model Compression](https://arxiv.org/pdf/1908.09355) and [MobileBERT: a Compact Task-Agnostic BERT for Resource-Limited Devices](https://arxiv.org/abs/2004.02984). The general loss function for this approach can be summarized as follow. + +$$L_{KD} = \sum\limits_i D(T_t^{n_i}(F_t^{n_i}), T_s^{m_i}(F_s^{m_i}))$$ + +Where $D$ is a distance measurement as before, $F_t^{n_i}$ the output feature of the $n_i$'s layer of the teacher model, $F_s^{m_i}$ the output feature of the $m_i$'s layer of the student model. Since the dimensions of $F_t^{n_i}$ and $F_s^{m_i}$ are usually different, the transformations $T_t^{n_i}$ and $T_s^{m_i}$ are needed to match dimensions of the two features. Specifically, the transformation can take the forms like identity, linear transformation, 1X1 convolution etc. + +### Self Distillation + +Self-distillation ia a one-stage training method where the teacher model and student models can be trained together. It attaches several attention modules and shallow classifiers at different depths of neural networks and distills knowledge from the deepest classifier to the shallower classifiers. Different from the conventional knowledge distillation methods where the knowledge of the teacher model is transferred to another student model, self-distillation can be considered as knowledge transfer in the same model, from the deeper layers to the shallower layers. +The additional classifiers in self-distillation allow the neural network to work in a dynamic manner, which leads to a much higher acceleration. +
+ +Architecture + +Architecture from paper [Self-Distillation: Towards Efficient and Compact Neural Networks](https://ieeexplore.ieee.org/document/9381661) + +## Distillation Support Matrix + +|Distillation Algorithm |PyTorch |TensorFlow | +|------------------------------------------------|:--------:|:---------:| +|Knowledge Distillation |✔ |✔ | +|Intermediate Layer Knowledge Distillation |✔ |Will be supported| +|Self Distillation |✔ |✖ | + +## Get Started with Distillation API + +Simplest launcher code if training behavior is defined in user-defined yaml. + +```python +from neural_compressor.experimental import Distillation, common +distiller = Distillation('/path/to/user/yaml') +distiller.student_model = student_model +distiller.teacher_model = teacher_model +model = distiller.fit() +``` +Distillation class also support DistillationConf class as it's argument. + +```python +from neural_compressor.experimental import Distillation, common +from neural_compressor.conf.config import DistillationConf +conf = DistillationConf('/path/to/user/yaml') +distiller = Distillation(conf) +distiller.student_model = student_model +distiller.teacher_model = teacher_model +model = distiller.fit() +``` + +User can pass the customized training/evaluation functions to `Distillation` for flexible scenarios. In this case, distillation process can be done by pre-defined hooks in Neural Compressor. User needs to put those hooks inside the training function. + +Neural Compressor defines several hooks for user pass + +``` +on_train_begin() : Hook executed before training begins +on_after_compute_loss(input, student_output, student_loss) : Hook executed after each batch inference of student model +on_epoch_end() : Hook executed at each epoch end +``` + +Following section shows how to use hooks in user pass-in training function which is part of example from BlendCNN distillation: + +```python +def train_func(model): + distiller.on_train_begin() + for nepoch in range(epochs): + model.train() + cnt = 0 + loss_sum = 0. + iter_bar = tqdm(train_dataloader, desc='Iter (loss=X.XXX)') + for batch in iter_bar: + teacher_logits, input_ids, segment_ids, input_mask, target = batch + cnt += 1 + output = model(input_ids, segment_ids, input_mask) + loss = criterion(output, target) + loss = distiller.on_after_compute_loss( + {'input_ids':input_ids, 'segment_ids':segment_ids, 'input_mask':input_mask}, + output, + loss, + teacher_logits) + optimizer.zero_grad() + loss.backward() + optimizer.step() + if cnt >= iters: + break + print('Average Loss: {}'.format(loss_sum / cnt)) + distiller.on_epoch_end() +... +``` + +In this case, the launcher code is like the following: + +```python +from neural_compressor.experimental import Distillation, common +from neural_compressor.experimental.common.criterion import PyTorchKnowledgeDistillationLoss +distiller = Distillation(args.config) +distiller.student_model = model +distiller.teacher_model = teacher +distiller.criterion = PyTorchKnowledgeDistillationLoss() +distiller.train_func = train_func +model = distiller.fit() +``` + +## Examples + +[Distillation Examples](../examples/README.md#distillation) +
+[Distillation Examples Results](./validated_model_list.md#validated-knowledge-distillation-examples) diff --git a/docs/distillation_quantization.md b/docs/source/io_doc/distillation_quantization.md similarity index 100% rename from docs/distillation_quantization.md rename to docs/source/io_doc/distillation_quantization.md diff --git a/docs/distributed.md b/docs/source/io_doc/distributed.md similarity index 100% rename from docs/distributed.md rename to docs/source/io_doc/distributed.md diff --git a/docs/doclist.rst b/docs/source/io_doc/doclist.rst similarity index 100% rename from docs/doclist.rst rename to docs/source/io_doc/doclist.rst diff --git a/docs/dynamic_quantization.md b/docs/source/io_doc/dynamic_quantization.md similarity index 100% rename from docs/dynamic_quantization.md rename to docs/source/io_doc/dynamic_quantization.md diff --git a/docs/examples_readme.md b/docs/source/io_doc/examples_readme.md similarity index 100% rename from docs/examples_readme.md rename to docs/source/io_doc/examples_readme.md diff --git a/docs/faq.md b/docs/source/io_doc/faq.md similarity index 100% rename from docs/faq.md rename to docs/source/io_doc/faq.md diff --git a/docs/getting_started.md b/docs/source/io_doc/getting_started.md similarity index 100% rename from docs/getting_started.md rename to docs/source/io_doc/getting_started.md diff --git a/docs/graph_optimization.md b/docs/source/io_doc/graph_optimization.md similarity index 100% rename from docs/graph_optimization.md rename to docs/source/io_doc/graph_optimization.md diff --git a/docs/incompatible_changes.md b/docs/source/io_doc/incompatible_changes.md similarity index 100% rename from docs/incompatible_changes.md rename to docs/source/io_doc/incompatible_changes.md diff --git a/docs/infrastructure.md b/docs/source/io_doc/infrastructure.md similarity index 100% rename from docs/infrastructure.md rename to docs/source/io_doc/infrastructure.md diff --git a/docs/installation_guide.md b/docs/source/io_doc/installation_guide.md similarity index 100% rename from docs/installation_guide.md rename to docs/source/io_doc/installation_guide.md diff --git a/docs/legal_information.md b/docs/source/io_doc/legal_information.md similarity index 100% rename from docs/legal_information.md rename to docs/source/io_doc/legal_information.md diff --git a/docs/metric.md b/docs/source/io_doc/metric.md similarity index 100% rename from docs/metric.md rename to docs/source/io_doc/metric.md diff --git a/docs/mixed_precision.md b/docs/source/io_doc/mixed_precision.md similarity index 100% rename from docs/mixed_precision.md rename to docs/source/io_doc/mixed_precision.md diff --git a/docs/model.md b/docs/source/io_doc/model.md similarity index 100% rename from docs/model.md rename to docs/source/io_doc/model.md diff --git a/docs/model_conversion.md b/docs/source/io_doc/model_conversion.md similarity index 100% rename from docs/model_conversion.md rename to docs/source/io_doc/model_conversion.md diff --git a/docs/objective.md b/docs/source/io_doc/objective.md similarity index 100% rename from docs/objective.md rename to docs/source/io_doc/objective.md diff --git a/docs/orchestration.md b/docs/source/io_doc/orchestration.md old mode 100755 new mode 100644 similarity index 97% rename from docs/orchestration.md rename to docs/source/io_doc/orchestration.md index 9de1d46172b..fb7e4fa4099 --- a/docs/orchestration.md +++ b/docs/source/io_doc/orchestration.md @@ -1,112 +1,112 @@ -Optimization Orchestration -============ - -1. [Introduction](#introduction) - - 1.1. [One-shot](#one-shot) - - 1.2. [Multi-shot](#multi-shot) - -2. [Orchestration Support Matrix](#orchestration-support-matrix) -3. [Get Started with Orchestration API ](#get-started-with-orchestration-api) -4. [Examples](#examples) - -## Introduction - -Orchestration is the combination of multiple optimization techniques, either applied simultaneously (one-shot) or sequentially (multi-shot). Intel Neural Compressor supports arbitrary meaningful combinations of supported optimization methods under one-shot or multi-shot, such as pruning during quantization-aware training, or pruning and then post-training quantization, pruning and then distillation and then quantization. - -### One-shot -Since quantization-aware training, pruning and distillation all leverage training process for optimization, we can achieve the goal of optimization through one shot training with arbitrary meaningful combinations of these methods, which often gain more benefits in terms of performance and accuracy than just one compression technique applied, and usually are as efficient as applying just one compression technique. The three possible combinations are shown below. -- Pruning during quantization-aware training -- Distillation with pattern lock pruning -- Distillation with pattern lock pruning and quantization-aware training - -### Multi-shot -Of course, besides one-shot, we also support separate execution of each optimization process. -- Pruning and then post-training quantization -- Distillation and then post-training quantization -- Distillation, then pruning and post-training quantization - -## Orchestration Support Matrix - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
OrchestrationCombinationsSupported
One-shotPruning + Quantization Aware Training✔
Distillation + Quantization Aware Training✔
Distillation + Pruning✔
Distillation + Pruning + Quantization Aware Training✔
Multi-shotPruning then Quantization✔
Distillation then Quantization✔
Distillation then Pruning✔
Distillation then Pruning then Quantization✔
- -## Get Started with Orchestration API - -Neural Compressor defines `Scheduler` class to automatically pipeline execute model optimization with one shot or multiple shots way. - -User instantiates model optimization components, such as quantization, pruning, distillation, separately. After that, user could append -those separate optimization objects into scheduler's pipeline, the scheduler API executes them one by one. - -In following example it executes the pruning and then post-training quantization with two-shot way. - -```python -from neural_compressor.experimental import Quantization, Pruning, Scheduler -prune = Pruning(prune_conf) -quantizer = Quantization(post_training_quantization_conf) -scheduler = Scheduler() -scheduler.model = model -scheduler.append(prune) -scheduler.append(quantizer) -opt_model = scheduler.fit() -``` - -If user wants to execute the pruning and quantization-aware training with one-shot way, the code is like below. - -```python -from neural_compressor.experimental import Quantization, Pruning, Scheduler -prune = Pruning(prune_conf) -quantizer = Quantization(quantization_aware_training_conf) -scheduler = Scheduler() -scheduler.model = model -combination = scheduler.combine(prune, quantizer) -scheduler.append(combination) -opt_model = scheduler.fit() -``` - -## Examples - -[Orchestration Examples](../examples/README.md#orchestration) +Optimization Orchestration +============ + +1. [Introduction](#introduction) + + 1.1. [One-shot](#one-shot) + + 1.2. [Multi-shot](#multi-shot) + +2. [Orchestration Support Matrix](#orchestration-support-matrix) +3. [Get Started with Orchestration API ](#get-started-with-orchestration-api) +4. [Examples](#examples) + +## Introduction + +Orchestration is the combination of multiple optimization techniques, either applied simultaneously (one-shot) or sequentially (multi-shot). Intel Neural Compressor supports arbitrary meaningful combinations of supported optimization methods under one-shot or multi-shot, such as pruning during quantization-aware training, or pruning and then post-training quantization, pruning and then distillation and then quantization. + +### One-shot +Since quantization-aware training, pruning and distillation all leverage training process for optimization, we can achieve the goal of optimization through one shot training with arbitrary meaningful combinations of these methods, which often gain more benefits in terms of performance and accuracy than just one compression technique applied, and usually are as efficient as applying just one compression technique. The three possible combinations are shown below. +- Pruning during quantization-aware training +- Distillation with pattern lock pruning +- Distillation with pattern lock pruning and quantization-aware training + +### Multi-shot +Of course, besides one-shot, we also support separate execution of each optimization process. +- Pruning and then post-training quantization +- Distillation and then post-training quantization +- Distillation, then pruning and post-training quantization + +## Orchestration Support Matrix + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
OrchestrationCombinationsSupported
One-shotPruning + Quantization Aware Training✔
Distillation + Quantization Aware Training✔
Distillation + Pruning✔
Distillation + Pruning + Quantization Aware Training✔
Multi-shotPruning then Quantization✔
Distillation then Quantization✔
Distillation then Pruning✔
Distillation then Pruning then Quantization✔
+ +## Get Started with Orchestration API + +Neural Compressor defines `Scheduler` class to automatically pipeline execute model optimization with one shot or multiple shots way. + +User instantiates model optimization components, such as quantization, pruning, distillation, separately. After that, user could append +those separate optimization objects into scheduler's pipeline, the scheduler API executes them one by one. + +In following example it executes the pruning and then post-training quantization with two-shot way. + +```python +from neural_compressor.experimental import Quantization, Pruning, Scheduler +prune = Pruning(prune_conf) +quantizer = Quantization(post_training_quantization_conf) +scheduler = Scheduler() +scheduler.model = model +scheduler.append(prune) +scheduler.append(quantizer) +opt_model = scheduler.fit() +``` + +If user wants to execute the pruning and quantization-aware training with one-shot way, the code is like below. + +```python +from neural_compressor.experimental import Quantization, Pruning, Scheduler +prune = Pruning(prune_conf) +quantizer = Quantization(quantization_aware_training_conf) +scheduler = Scheduler() +scheduler.model = model +combination = scheduler.combine(prune, quantizer) +scheduler.append(combination) +opt_model = scheduler.fit() +``` + +## Examples + +[Orchestration Examples](../examples/README.md#orchestration) diff --git a/docs/platform_configuration.md b/docs/source/io_doc/platform_configuration.md similarity index 100% rename from docs/platform_configuration.md rename to docs/source/io_doc/platform_configuration.md diff --git a/docs/pruning.md b/docs/source/io_doc/pruning.md old mode 100755 new mode 100644 similarity index 97% rename from docs/pruning.md rename to docs/source/io_doc/pruning.md index 1cf9f570642..95f06d81f01 --- a/docs/pruning.md +++ b/docs/source/io_doc/pruning.md @@ -1,234 +1,234 @@ -Pruning -============ - -1. [Introduction](#introduction) - - 1.1. [Neural Network Pruning](#neural-network-pruning) - - 1.2. [Pruning Patterns](#pruning-patterns) - - 1.3. [Pruning Criteria](#pruning-criteria) - - 1.4. [Pruning Schedule](#pruning-schedule) - -2. [Pruning Support Matrix](#pruning-support-matrix) - -3. [Get Started With Pruning API](#get-started-with-pruning-api) - -4. [Examples](#examples) - -## Introduction - -### Neural Network Pruning -Neural network pruning (briefly known as pruning or sparsity) is one of the most promising model compression techniques. It removes the least important parameters in the network and achieves compact architectures with minimal accuracy drop and maximal inference acceleration. As current state-of-the-art models have increasingly more parameters, pruning plays a crucial role in enabling them to run on devices whose memory footprints and computing resources are limited. - - - pruning intro - - - -### Pruning Patterns - -Pruning patterns defines the rules of pruned weights' arrangements in space. - - - Sparsity Pattern - - - -- Unstructured Pruning - -Unstructured pruning means finding and removing the less salient connection in the model where the nonzero patterns are irregular and could be anywhere in the matrix. - -- 2in4 Pruning - -NVIDIA proposed [2:4 sparsity](https://developer.nvidia.com/blog/accelerating-inference-with-sparsity-using-ampere-and-tensorrt/) (or known as "2in4 sparsity") in Ampere architecture, for every 4 continuous elements in a matrix, two of them are zero and others are non-zero. - -- Structured Pruning - -Structured pruning means finding parameters in groups, deleting entire blocks, filters, or channels according to some pruning criterions. In general, structured pruning leads to lower accuracy due to restrictive structure than unstructured pruning; However, it can accelerate the model execution significantly because it can fit hardware design better. - -Different from 2:4 sparsity above, we propose the block-wise structured sparsity patterns that we are able to demonstrate the performance benefits on existing Intel hardwares even without the support of hardware sparsity. A block-wise sparsity pattern with block size ```S``` means the contiguous ```S``` elements in this block are all zero values. - -For a typical GEMM, the weight dimension is ```IC``` x ```OC```, where ```IC``` is the number of input channels and ```OC``` is the number of output channels. Note that sometimes ```IC``` is also called dimension ```K```, and ```OC``` is called dimension ```N```. The sparsity dimension is on ```OC``` (or ```N```). - -For a typical Convolution, the weight dimension is ```OC x IC x KH x KW```, where ```OC``` is the number of output channels, ```IC``` is the number of input channels, and ```KH``` and ```KW``` is the kernel height and weight. The sparsity dimension is also on ```OC```. - -Here is a figure showing a matrix with ```IC``` = 32 and ```OC``` = 16 dimension, and a block-wise sparsity pattern with block size 4 on ```OC``` dimension. - - - block sparsity Pattern - - -### Pruning Criteria - -Pruning criteria defines the rules of which weights are least important to be pruned, in order to maintain the model's original accuracy. Most popular criteria examine weights' absolute value and their corresponding gradients. - -- Magnitude - - The algorithm prunes the weight by the lowest absolute value at each layer with given sparsity target. - -- Gradient sensitivity - - The algorithm prunes the head, intermediate layers, and hidden states in NLP model according to importance score calculated by following the paper [FastFormers](https://arxiv.org/abs/2010.13382). - -- Group Lasso - - The algorithm uses Group lasso regularization to prune entire rows, columns or blocks of parameters that result in a smaller dense network. - -- Pattern Lock - - The algorithm locks the sparsity pattern in fine tune phase by freezing those zero values of weight tensor during weight update of training. - -- SNIP - - The algorithm prunes the dense model at its initialization, by analyzing the weights' effect to the loss function when they are masked. Please refer to the original [paper](https://arxiv.org/abs/1810.02340) for details - -- SNIP with momentum - - The algorithm improves original SNIP algorithms and introduces weights' score maps which updates in a momentum way.\ - In the following formula, $n$ is the pruning step and $W$ and $G$ are model's weights and gradients respectively. - $$Score_{n} = 1.0 \times Score_{n-1} + 0.9 \times |W_{n} \times G_{n}|$$ - -### Pruning Schedule - -Pruning schedule defines the way the model reach the target sparsity (the ratio of pruned weights). - -- One-shot Pruning - - One-shot pruning means the model is pruned to its target sparsity with one single step. This pruning method often works at model's initialization step. It can easily cause accuracy drop, but save much training time. - -- Iterative Pruning - - Iterative pruning means the model is gradually pruned to its target sparsity during a training process. The pruning process contains several pruning steps, and each step raises model's sparsity to a higher value. In the final pruning step, the model reaches target sparsity and the pruning process ends. - -## Pruning Support Matrix - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
Pruning TypePruning GranularityPruning AlgorithmFramework
Unstructured PruningElement-wiseMagnitudePyTorch, TensorFlow
Pattern LockPyTorch
SNIP with momentumPyTorch
Structured PruningFilter/Channel-wiseGradient SensitivityPyTorch
SNIP with momentumPyTorch
Block-wiseGroup LassoPyTorch
SNIP with momentumPyTorch
Element-wisePattern LockPyTorch
SNIP with momentumPyTorch
- -## Get Started with Pruning API - -Neural Compressor `Pruning` API is defined under `neural_compressor.experimental.Pruning`, which takes a user defined yaml file as input. Below is the launcher code of applying the API to execute a pruning process. - -```python -from neural_compressor.experimental import Pruning -prune = Pruning('/path/to/user/pruning/yaml') -prune.model = model -model = prune.fit() -``` - -Users can pass the customized training/evaluation functions to `Pruning` for flexible scenarios. In this case, pruning process can be done by pre-defined hooks in Neural Compressor. Users need to put those hooks inside the training function. - -Neural Compressor defines several hooks for users to use: - -``` -on_epoch_begin(epoch) : Hook executed at each epoch beginning -on_step_begin(batch) : Hook executed at each batch beginning -on_step_end() : Hook executed at each batch end -on_epoch_end() : Hook executed at each epoch end -on_before_optimizer_step() : Hook executed after gradients calculated and before backward -``` - -Following section shows how to use hooks in user pass-in training function which is part of example from BERT training: - -```python -def pruning_func(model): - for epoch in range(int(args.num_train_epochs)): - pbar = ProgressBar(n_total=len(train_dataloader), desc='Training') - model.train() - prune.on_epoch_begin(epoch) - for step, batch in enumerate(train_dataloader): - prune.on_step_begin(step) - batch = tuple(t.to(args.device) for t in batch) - inputs = {'input_ids': batch[0], - 'attention_mask': batch[1], - 'labels': batch[3]} - #inputs['token_type_ids'] = batch[2] - outputs = model(**inputs) - loss = outputs[0] # model outputs are always tuple in transformers (see doc) - - if args.n_gpu > 1: - loss = loss.mean() # mean() to average on multi-gpu parallel training - if args.gradient_accumulation_steps > 1: - loss = loss / args.gradient_accumulation_steps - - loss.backward() - torch.nn.utils.clip_grad_norm_(model.parameters(), args.max_grad_norm) - - if (step + 1) % args.gradient_accumulation_steps == 0: - prune.on_before_optimizer_step() - optimizer.step() - scheduler.step() # Update learning rate schedule - model.zero_grad() - - prune.on_step_end() -... -``` -In this case, the launcher code is like the following: - -```python -from neural_compressor.experimental import Pruning, common -prune = Pruning(args.config) -prune.model = model -prune.train_func = pruning_func -model = prune.fit() -``` - -## Examples - -We validate the sparsity on typical models across different domains (including CV, NLP, and Recommendation System). [Validated pruning examples](../docs/validated_model_list.md#validated-pruning-examples) shows the sparsity pattern, sparsity ratio, and accuracy of sparse and dense (Reference) model for each model. - -Please refer to pruning examples([TensorFlow](../examples/README.md#Pruning), [PyTorch](../examples/README.md#Pruning-1)) for more information. +Pruning +============ + +1. [Introduction](#introduction) + + 1.1. [Neural Network Pruning](#neural-network-pruning) + + 1.2. [Pruning Patterns](#pruning-patterns) + + 1.3. [Pruning Criteria](#pruning-criteria) + + 1.4. [Pruning Schedule](#pruning-schedule) + +2. [Pruning Support Matrix](#pruning-support-matrix) + +3. [Get Started With Pruning API](#get-started-with-pruning-api) + +4. [Examples](#examples) + +## Introduction + +### Neural Network Pruning +Neural network pruning (briefly known as pruning or sparsity) is one of the most promising model compression techniques. It removes the least important parameters in the network and achieves compact architectures with minimal accuracy drop and maximal inference acceleration. As current state-of-the-art models have increasingly more parameters, pruning plays a crucial role in enabling them to run on devices whose memory footprints and computing resources are limited. + + + pruning intro + + + +### Pruning Patterns + +Pruning patterns defines the rules of pruned weights' arrangements in space. + + + Sparsity Pattern + + + +- Unstructured Pruning + +Unstructured pruning means finding and removing the less salient connection in the model where the nonzero patterns are irregular and could be anywhere in the matrix. + +- 2in4 Pruning + +NVIDIA proposed [2:4 sparsity](https://developer.nvidia.com/blog/accelerating-inference-with-sparsity-using-ampere-and-tensorrt/) (or known as "2in4 sparsity") in Ampere architecture, for every 4 continuous elements in a matrix, two of them are zero and others are non-zero. + +- Structured Pruning + +Structured pruning means finding parameters in groups, deleting entire blocks, filters, or channels according to some pruning criterions. In general, structured pruning leads to lower accuracy due to restrictive structure than unstructured pruning; However, it can accelerate the model execution significantly because it can fit hardware design better. + +Different from 2:4 sparsity above, we propose the block-wise structured sparsity patterns that we are able to demonstrate the performance benefits on existing Intel hardwares even without the support of hardware sparsity. A block-wise sparsity pattern with block size ```S``` means the contiguous ```S``` elements in this block are all zero values. + +For a typical GEMM, the weight dimension is ```IC``` x ```OC```, where ```IC``` is the number of input channels and ```OC``` is the number of output channels. Note that sometimes ```IC``` is also called dimension ```K```, and ```OC``` is called dimension ```N```. The sparsity dimension is on ```OC``` (or ```N```). + +For a typical Convolution, the weight dimension is ```OC x IC x KH x KW```, where ```OC``` is the number of output channels, ```IC``` is the number of input channels, and ```KH``` and ```KW``` is the kernel height and weight. The sparsity dimension is also on ```OC```. + +Here is a figure showing a matrix with ```IC``` = 32 and ```OC``` = 16 dimension, and a block-wise sparsity pattern with block size 4 on ```OC``` dimension. + + + block sparsity Pattern + + +### Pruning Criteria + +Pruning criteria defines the rules of which weights are least important to be pruned, in order to maintain the model's original accuracy. Most popular criteria examine weights' absolute value and their corresponding gradients. + +- Magnitude + + The algorithm prunes the weight by the lowest absolute value at each layer with given sparsity target. + +- Gradient sensitivity + + The algorithm prunes the head, intermediate layers, and hidden states in NLP model according to importance score calculated by following the paper [FastFormers](https://arxiv.org/abs/2010.13382). + +- Group Lasso + + The algorithm uses Group lasso regularization to prune entire rows, columns or blocks of parameters that result in a smaller dense network. + +- Pattern Lock + + The algorithm locks the sparsity pattern in fine tune phase by freezing those zero values of weight tensor during weight update of training. + +- SNIP + + The algorithm prunes the dense model at its initialization, by analyzing the weights' effect to the loss function when they are masked. Please refer to the original [paper](https://arxiv.org/abs/1810.02340) for details + +- SNIP with momentum + + The algorithm improves original SNIP algorithms and introduces weights' score maps which updates in a momentum way.\ + In the following formula, $n$ is the pruning step and $W$ and $G$ are model's weights and gradients respectively. + $$Score_{n} = 1.0 \times Score_{n-1} + 0.9 \times |W_{n} \times G_{n}|$$ + +### Pruning Schedule + +Pruning schedule defines the way the model reach the target sparsity (the ratio of pruned weights). + +- One-shot Pruning + + One-shot pruning means the model is pruned to its target sparsity with one single step. This pruning method often works at model's initialization step. It can easily cause accuracy drop, but save much training time. + +- Iterative Pruning + + Iterative pruning means the model is gradually pruned to its target sparsity during a training process. The pruning process contains several pruning steps, and each step raises model's sparsity to a higher value. In the final pruning step, the model reaches target sparsity and the pruning process ends. + +## Pruning Support Matrix + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Pruning TypePruning GranularityPruning AlgorithmFramework
Unstructured PruningElement-wiseMagnitudePyTorch, TensorFlow
Pattern LockPyTorch
SNIP with momentumPyTorch
Structured PruningFilter/Channel-wiseGradient SensitivityPyTorch
SNIP with momentumPyTorch
Block-wiseGroup LassoPyTorch
SNIP with momentumPyTorch
Element-wisePattern LockPyTorch
SNIP with momentumPyTorch
+ +## Get Started with Pruning API + +Neural Compressor `Pruning` API is defined under `neural_compressor.experimental.Pruning`, which takes a user defined yaml file as input. Below is the launcher code of applying the API to execute a pruning process. + +```python +from neural_compressor.experimental import Pruning +prune = Pruning('/path/to/user/pruning/yaml') +prune.model = model +model = prune.fit() +``` + +Users can pass the customized training/evaluation functions to `Pruning` for flexible scenarios. In this case, pruning process can be done by pre-defined hooks in Neural Compressor. Users need to put those hooks inside the training function. + +Neural Compressor defines several hooks for users to use: + +``` +on_epoch_begin(epoch) : Hook executed at each epoch beginning +on_step_begin(batch) : Hook executed at each batch beginning +on_step_end() : Hook executed at each batch end +on_epoch_end() : Hook executed at each epoch end +on_before_optimizer_step() : Hook executed after gradients calculated and before backward +``` + +Following section shows how to use hooks in user pass-in training function which is part of example from BERT training: + +```python +def pruning_func(model): + for epoch in range(int(args.num_train_epochs)): + pbar = ProgressBar(n_total=len(train_dataloader), desc='Training') + model.train() + prune.on_epoch_begin(epoch) + for step, batch in enumerate(train_dataloader): + prune.on_step_begin(step) + batch = tuple(t.to(args.device) for t in batch) + inputs = {'input_ids': batch[0], + 'attention_mask': batch[1], + 'labels': batch[3]} + #inputs['token_type_ids'] = batch[2] + outputs = model(**inputs) + loss = outputs[0] # model outputs are always tuple in transformers (see doc) + + if args.n_gpu > 1: + loss = loss.mean() # mean() to average on multi-gpu parallel training + if args.gradient_accumulation_steps > 1: + loss = loss / args.gradient_accumulation_steps + + loss.backward() + torch.nn.utils.clip_grad_norm_(model.parameters(), args.max_grad_norm) + + if (step + 1) % args.gradient_accumulation_steps == 0: + prune.on_before_optimizer_step() + optimizer.step() + scheduler.step() # Update learning rate schedule + model.zero_grad() + + prune.on_step_end() +... +``` +In this case, the launcher code is like the following: + +```python +from neural_compressor.experimental import Pruning, common +prune = Pruning(args.config) +prune.model = model +prune.train_func = pruning_func +model = prune.fit() +``` + +## Examples + +We validate the sparsity on typical models across different domains (including CV, NLP, and Recommendation System). [Validated pruning examples](../docs/validated_model_list.md#validated-pruning-examples) shows the sparsity pattern, sparsity ratio, and accuracy of sparse and dense (Reference) model for each model. + +Please refer to pruning examples([TensorFlow](../examples/README.md#Pruning), [PyTorch](../examples/README.md#Pruning-1)) for more information. diff --git a/docs/publication_list.md b/docs/source/io_doc/publication_list.md similarity index 100% rename from docs/publication_list.md rename to docs/source/io_doc/publication_list.md diff --git a/docs/quantization_mixed_precision.md b/docs/source/io_doc/quantization_mixed_precision.md similarity index 97% rename from docs/quantization_mixed_precision.md rename to docs/source/io_doc/quantization_mixed_precision.md index 728c854da5c..e4b800616b8 100644 --- a/docs/quantization_mixed_precision.md +++ b/docs/source/io_doc/quantization_mixed_precision.md @@ -1,59 +1,59 @@ -### Turn ON Auto Mixed Precision during Quantization - -BF16 conversion during quantization is default OFF. To force enable it, users need to turn on use_bf16 by pythonic config: - -```python -from neural_compressor import config -from neural_compressor.experimental import Quantization - -config.quantization.use_bf16 = True -quantizer = Quantization(config) -``` - -### Tensorflow - -Intel has worked with the TensorFlow development team to enhance TensorFlow to include bfloat16 data support for CPUs. For more information about BF16 in TensorFlow, please read [Accelerating AI performance on 3rd Gen Intel® Xeon® Scalable processors with TensorFlow and Bfloat16](https://blog.tensorflow.org/2020/06/accelerating-ai-performance-on-3rd-gen-processors-with-tensorflow-bfloat16.html). - -- BF16 conversion during quantization in TensorFlow - - -
- Architecture -
- - - -- Three steps - -1. Convert to a `FP32 + INT8` mixed precision Graph - - In this steps, TF adaptor will regard all fallback datatype as `FP32`. According to the per op datatype in tuning config passed by strategy, TF adaptor will generate a `FP32 + INT8` mixed precision graph. - -2. Convert to a `BF16 + FP32 + INT8` mixed precision Graph - - In this phase, adaptor will convert some `FP32` ops to `BF16` according to `bf16_ops` list in tuning config. - -3. Optimize the `BF16 + FP32 + INT8` mixed precision Graph - - After the mixed precision graph generated, there are still some optimization need to be applied to improved the performance, for example `Cast + Cast` and so on. The `BF16Convert` transformer also apply a depth-first method to make it possible to take the ops use `BF16` which can support `BF16` datatype to reduce the insertion of `Cast` op. - -### PyTorch - -Intel has also worked with the PyTorch development team to enhance PyTorch to include bfloat16 data support for CPUs. - -- BF16 conversion during quantization in PyTorch - - -
- Architecture -
- - -- Two steps -1. Convert to a `FP32 + INT8` mixed precision Graph or Module - - In this steps, PT adaptor will combine the `INT8` ops and all fallback ops to `FP32 + INT8` mixed precision Graph or Module no matter in Eager mode or Fx Graph mode. - -2. Convert to a `BF16 + FP32 + INT8` mixed precision Graph or Module - - In this phase, adaptor will according to `BF16` op list from strategy tune config to wrapper the `FP32` module with `BF16Wrapper` to realize the `BF16 + FP32 + INT8` mixed precision Graph or Module. adaptor will do retrace the `GraphModule` again if using Fx Graph mode. +### Turn ON Auto Mixed Precision during Quantization + +BF16 conversion during quantization is default OFF. To force enable it, users need to turn on use_bf16 by pythonic config: + +```python +from neural_compressor import config +from neural_compressor.experimental import Quantization + +config.quantization.use_bf16 = True +quantizer = Quantization(config) +``` + +### Tensorflow + +Intel has worked with the TensorFlow development team to enhance TensorFlow to include bfloat16 data support for CPUs. For more information about BF16 in TensorFlow, please read [Accelerating AI performance on 3rd Gen Intel® Xeon® Scalable processors with TensorFlow and Bfloat16](https://blog.tensorflow.org/2020/06/accelerating-ai-performance-on-3rd-gen-processors-with-tensorflow-bfloat16.html). + +- BF16 conversion during quantization in TensorFlow + + +
+ Architecture +
+ + + +- Three steps + +1. Convert to a `FP32 + INT8` mixed precision Graph + + In this steps, TF adaptor will regard all fallback datatype as `FP32`. According to the per op datatype in tuning config passed by strategy, TF adaptor will generate a `FP32 + INT8` mixed precision graph. + +2. Convert to a `BF16 + FP32 + INT8` mixed precision Graph + + In this phase, adaptor will convert some `FP32` ops to `BF16` according to `bf16_ops` list in tuning config. + +3. Optimize the `BF16 + FP32 + INT8` mixed precision Graph + + After the mixed precision graph generated, there are still some optimization need to be applied to improved the performance, for example `Cast + Cast` and so on. The `BF16Convert` transformer also apply a depth-first method to make it possible to take the ops use `BF16` which can support `BF16` datatype to reduce the insertion of `Cast` op. + +### PyTorch + +Intel has also worked with the PyTorch development team to enhance PyTorch to include bfloat16 data support for CPUs. + +- BF16 conversion during quantization in PyTorch + + +
+ Architecture +
+ + +- Two steps +1. Convert to a `FP32 + INT8` mixed precision Graph or Module + + In this steps, PT adaptor will combine the `INT8` ops and all fallback ops to `FP32 + INT8` mixed precision Graph or Module no matter in Eager mode or Fx Graph mode. + +2. Convert to a `BF16 + FP32 + INT8` mixed precision Graph or Module + + In this phase, adaptor will according to `BF16` op list from strategy tune config to wrapper the `FP32` module with `BF16Wrapper` to realize the `BF16 + FP32 + INT8` mixed precision Graph or Module. adaptor will do retrace the `GraphModule` again if using Fx Graph mode. diff --git a/docs/reference_examples.md b/docs/source/io_doc/reference_examples.md similarity index 100% rename from docs/reference_examples.md rename to docs/source/io_doc/reference_examples.md diff --git a/docs/releases_info.md b/docs/source/io_doc/releases_info.md similarity index 100% rename from docs/releases_info.md rename to docs/source/io_doc/releases_info.md diff --git a/docs/sigopt_strategy.md b/docs/source/io_doc/sigopt_strategy.md similarity index 100% rename from docs/sigopt_strategy.md rename to docs/source/io_doc/sigopt_strategy.md diff --git a/docs/tensorboard.md b/docs/source/io_doc/tensorboard.md similarity index 100% rename from docs/tensorboard.md rename to docs/source/io_doc/tensorboard.md diff --git a/docs/transform.md b/docs/source/io_doc/transform.md similarity index 100% rename from docs/transform.md rename to docs/source/io_doc/transform.md diff --git a/docs/tuning_strategies.md b/docs/source/io_doc/tuning_strategies.md similarity index 100% rename from docs/tuning_strategies.md rename to docs/source/io_doc/tuning_strategies.md diff --git a/docs/validated_model_list.md b/docs/source/io_doc/validated_model_list.md similarity index 100% rename from docs/validated_model_list.md rename to docs/source/io_doc/validated_model_list.md diff --git a/docs/welcome.md b/docs/source/io_doc/welcome.md similarity index 100% rename from docs/welcome.md rename to docs/source/io_doc/welcome.md diff --git a/sphinx-requirements.txt b/docs/sphinx-requirements.txt old mode 100755 new mode 100644 similarity index 100% rename from sphinx-requirements.txt rename to docs/sphinx-requirements.txt diff --git a/neural_compressor/experimental/data/datasets/bert_dataset.py b/neural_compressor/experimental/data/datasets/bert_dataset.py index 481757bac91..335fcd557ae 100644 --- a/neural_compressor/experimental/data/datasets/bert_dataset.py +++ b/neural_compressor/experimental/data/datasets/bert_dataset.py @@ -30,7 +30,8 @@ @dataset_registry(dataset_type="bert", framework="pytorch", dataset_format='') class PytorchBertDataset(Dataset): - """Dataset used for model Bert. + """Not displayed. + Dataset used for model Bert. This Dataset is to construct from the Bert TensorDataset and not a full implementation from yaml config. The original repo link is: https://github.com/huggingface/transformers. When you want use this Dataset, you should add it before you initialize your DataLoader. From 6dfbe765581c5a72db9ffe31306e39016ef403a5 Mon Sep 17 00:00:00 2001 From: "Yue, Wenjiao" Date: Mon, 21 Nov 2022 11:17:14 +0800 Subject: [PATCH 02/17] check build error --- .github/workflows/publish.yml | 1 + docs/Makefile | 13 ++----------- 2 files changed, 3 insertions(+), 11 deletions(-) diff --git a/.github/workflows/publish.yml b/.github/workflows/publish.yml index 66211ff1eb9..2789de6f53a 100644 --- a/.github/workflows/publish.yml +++ b/.github/workflows/publish.yml @@ -20,6 +20,7 @@ jobs: - name: Build the docs run: | export PATH="$HOME/.local/bin:$PATH/docs" + ls make html - name: Push the docs uses: peaceiris/actions-gh-pages@v3 diff --git a/docs/Makefile b/docs/Makefile index 9acd52f5f18..e3e1697e92b 100644 --- a/docs/Makefile +++ b/docs/Makefile @@ -32,19 +32,10 @@ html: # common svg cp -f "$(CODEIMGDIR)/imgs/common/code.svg" "$(CODEIMGDIR)/images/view-page-source-icon.svg" cp -f "$(CODEIMGDIR)/imgs/common/right.svg" "$(CODEIMGDIR)/images/chevron-right-orange.svg" - # README.md imgs - # README.md link cp "../neural_coder/extensions/screenshots/extmanager.png" "_build/html/_static/imgs/extmanager.png" - cp "$(IMGDIR)/INC_GUI.gif" "$(BUILDIMGDIR)/INC_GUI.gif" - cp "$(IMGDIR)/release_data.png" "$(BUILDIMGDIR)/release_data.png" - # cp docs/imgs/architecture.png "$(BUILDDIR)/html/docs/imgs/architecture.png" - # cp docs/imgs/workflow.png "$(BUILDDIR)/html/docs/imgs/workflow.png" - # cp docs/imgs/INC_GUI.gif "$(BUILDDIR)/html/docs/imgs/INC_GUI.gif" - # cp docs/imgs/release_data.png "$(BUILDDIR)/html/docs/imgs/release_data.png" - # cp "$(BUILDDIR)/html/README.html" "$(BUILDDIR)/html/README.html.tmp" - # sed 's/.md/.html/g' "$(BUILDDIR)/html/README.html.tmp" > "$(BUILDDIR)/html/README.html" - # rm -f "$(BUILDDIR)/html/README.html.tmp" + cp "$(IMGDIR)/INC_GUI.gif" "$(BUILDIMGDIR)/INC_GUI.gif" + cp "$(IMGDIR)/release_data.png" "$(BUILDIMGDIR)/release_data.png" # Catch-all target: route all unknown targets to Sphinx using the new From ff09b79d0b4826dc2a3cdb4a0b37ad642dac9666 Mon Sep 17 00:00:00 2001 From: "Yue, Wenjiao" Date: Mon, 21 Nov 2022 11:19:20 +0800 Subject: [PATCH 03/17] check build error --- .github/workflows/publish.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/publish.yml b/.github/workflows/publish.yml index 2789de6f53a..41d0ccbffd4 100644 --- a/.github/workflows/publish.yml +++ b/.github/workflows/publish.yml @@ -19,7 +19,7 @@ jobs: pip3 install --user -r docs/sphinx-requirements.txt - name: Build the docs run: | - export PATH="$HOME/.local/bin:$PATH/docs" + export PATH="$HOME/.local/bin:$PATH" ls make html - name: Push the docs From a5522480cc0686b68e94a2e7403681e11cb2eafc Mon Sep 17 00:00:00 2001 From: "Yue, Wenjiao" Date: Mon, 21 Nov 2022 11:20:36 +0800 Subject: [PATCH 04/17] modify build path --- .github/workflows/publish.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/publish.yml b/.github/workflows/publish.yml index 41d0ccbffd4..f3297bef2d8 100644 --- a/.github/workflows/publish.yml +++ b/.github/workflows/publish.yml @@ -20,7 +20,7 @@ jobs: - name: Build the docs run: | export PATH="$HOME/.local/bin:$PATH" - ls + cd docs/ make html - name: Push the docs uses: peaceiris/actions-gh-pages@v3 From a8d306d11d1cfea735b66488114c516dfb5c7c2f Mon Sep 17 00:00:00 2001 From: "Yue, Wenjiao" Date: Mon, 21 Nov 2022 11:25:09 +0800 Subject: [PATCH 05/17] modify sphinx-requirements --- docs/sphinx-requirements.txt | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/docs/sphinx-requirements.txt b/docs/sphinx-requirements.txt index 4c8b777989a..3d7fc7c1c45 100644 --- a/docs/sphinx-requirements.txt +++ b/docs/sphinx-requirements.txt @@ -1,5 +1,6 @@ sphinx==3.5.4 -sphinx-rtd-theme +pytorch_sphinx_theme recommonmark sphinx-markdown-tables -sphinx-md \ No newline at end of file +sphinx-md +sphinx-autoapi \ No newline at end of file From c0e17dd04e95fb1162c9c50d23d96fe69c3220c6 Mon Sep 17 00:00:00 2001 From: "Yue, Wenjiao" Date: Mon, 21 Nov 2022 15:30:31 +0800 Subject: [PATCH 06/17] modify IO catalog --- .../api-documentation/common/benchmark.rst | 10 +- .../api-documentation/common/criterion.rst | 210 +-- .../common/data/basic_nas.rst | 13 - .../common/data/dataloader.rst | 2 +- .../common/data/transforms.rst | 1562 +---------------- .../api-documentation/common/metric.rst | 16 +- .../api-documentation/common/metrics.rst | 622 ------- .../io_doc/api-documentation/common/model.rst | 9 +- .../api-documentation/common/optimizer.rst | 202 +-- .../api-documentation/component/Benchmark.rst | 13 - .../component/Distillation.rst | 13 +- .../component/MixedPrecision.rst | 13 +- .../component/ModelConversion.rst | 13 +- .../api-documentation/component/Nas.rst | 121 +- .../api-documentation/component/Pruning.rst | 14 +- .../component/Quantization.rst | 13 +- .../api-documentation/component/Scheduler.rst | 13 +- 17 files changed, 49 insertions(+), 2810 deletions(-) delete mode 100644 docs/source/io_doc/api-documentation/common/data/basic_nas.rst delete mode 100644 docs/source/io_doc/api-documentation/common/metrics.rst delete mode 100644 docs/source/io_doc/api-documentation/component/Benchmark.rst diff --git a/docs/source/io_doc/api-documentation/common/benchmark.rst b/docs/source/io_doc/api-documentation/common/benchmark.rst index 6f8288a6a46..0af068b64ba 100644 --- a/docs/source/io_doc/api-documentation/common/benchmark.rst +++ b/docs/source/io_doc/api-documentation/common/benchmark.rst @@ -1,7 +1,7 @@ -.. _quantization-api - Benchmark -############ +============== + +.. autoapisummary:: + + neural_compressor.experimental.benchmark -.. .. automodule:: neural_compressor.quantization -.. :members: \ No newline at end of file diff --git a/docs/source/io_doc/api-documentation/common/criterion.rst b/docs/source/io_doc/api-documentation/common/criterion.rst index 1fbc402b195..64d5c053dc6 100644 --- a/docs/source/io_doc/api-documentation/common/criterion.rst +++ b/docs/source/io_doc/api-documentation/common/criterion.rst @@ -1,211 +1,5 @@ Criterion -========================================================= - -.. py:module:: neural_compressor.experimental.common.criterion - -Module Contents ---------------- - -Classes -~~~~~~~ +============== .. autoapisummary:: - - neural_compressor.experimental.common.criterion.PyTorchKnowledgeDistillationLoss - neural_compressor.experimental.common.criterion.PyTorchKnowledgeDistillationLossWrapper - neural_compressor.experimental.common.criterion.TensorflowKnowledgeDistillationLoss - neural_compressor.experimental.common.criterion.TensorflowKnowledgeDistillationLossWrapper - neural_compressor.experimental.common.criterion.TensorflowKnowledgeDistillationLossExternal - - -.. py:class:: PyTorchKnowledgeDistillationLoss(temperature=1.0, loss_types=['CE', 'CE'], loss_weights=[0.5, 0.5], student_model=None, teacher_model=None) - - Bases: :py:obj:`KnowledgeDistillationLoss` - - The PyTorchKnowledgeDistillationLoss class inherits from KnowledgeDistillationLoss. - - .. py:method:: SoftCrossEntropy(logits, targets) - - Return SoftCrossEntropy. - - :param logits: output logits - :type logits: tensor - :param targets: ground truth label - :type targets: tensor - - :returns: SoftCrossEntropy - :rtype: tensor - - - .. py:method:: KullbackLeiblerDivergence(logits, targets) - - Return KullbackLeiblerDivergence. - - :param logits: output logits - :type logits: tensor - :param targets: ground truth label - :type targets: tensor - - :returns: KullbackLeiblerDivergence - :rtype: tensor - - - .. py:method:: teacher_model_forward(input, teacher_model=None, device=None) - - Teacher model forward. - - :param input: input data - :type input: tensor - :param teacher_model: teacher model. Defaults to None. - :type teacher_model: torch.nn.model, optional - :param device: device. Defaults to None. - :type device: torch.device, optional - - :returns: output - :rtype: tensor - - - .. py:method:: teacher_student_loss_cal(student_outputs, teacher_outputs) - - Calculate loss between student model and teacher model. - - :param student_outputs: student outputs - :type student_outputs: tensor - :param teacher_outputs: teacher outputs - :type teacher_outputs: tensor - - :returns: loss - :rtype: tensor - - - .. py:method:: student_targets_loss_cal(student_outputs, targets) - - Calculate loss of student model. - - :param student_outputs: student outputs - :type student_outputs: tensor - :param targets: groud truth label - :type targets: tensor - - :returns: loss - :rtype: tensor - - - -.. py:class:: PyTorchKnowledgeDistillationLossWrapper(param_dict) - - Bases: :py:obj:`object` - - PyTorchKnowledgeDistillationLossWrapper wraps PyTorchKnowledgeDistillationLoss. - - .. py:method:: _param_check() - - - .. py:method:: __call__(**kwargs) - - Return PyTorchKnowledgeDistillationLoss, param dict. - - :returns: PyTorchKnowledgeDistillationLoss - param dict (dict): param dict - :rtype: PyTorchKnowledgeDistillationLoss (class) - - - -.. py:class:: TensorflowKnowledgeDistillationLoss(temperature=1.0, loss_types=['CE', 'CE'], loss_weights=[0.5, 0.5], student_model=None, teacher_model=None) - - Bases: :py:obj:`KnowledgeDistillationLoss` - - The TensorflowKnowledgeDistillationLoss class inherits from KnowledgeDistillationLoss. - - .. py:method:: SoftCrossEntropy(targets, logits) - - Return SoftCrossEntropy. - - :param logits: output logits - :type logits: tensor - :param targets: ground truth label - :type targets: tensor - - :returns: SoftCrossEntropy - :rtype: tensor - - - .. py:method:: teacher_model_forward(input, teacher_model=None) - - Teacher model forward. - - :param input: input data - :type input: tensor - :param teacher_model: teacher model. Defaults to None. - :type teacher_model: optional - :param device: device. Defaults to None. - :type device: torch.device, optional - - :returns: output - :rtype: tensor - - - .. py:method:: teacher_student_loss_cal(student_outputs, teacher_outputs) - - Calculate loss between student model and teacher model. - - :param student_outputs: student outputs - :type student_outputs: tensor - :param teacher_outputs: teacher outputs - :type teacher_outputs: tensor - - :returns: loss - :rtype: tensor - - - .. py:method:: student_targets_loss_cal(student_outputs, targets) - - Calculate loss of student model. - - :param student_outputs: student outputs - :type student_outputs: tensor - :param targets: groud truth label - :type targets: tensor - - :returns: loss - :rtype: tensor - - - .. py:method:: __call__(student_outputs, targets) - - Return loss of student model. - - :param student_outputs: student outputs - :type student_outputs: tensor - :param targets: groud truth label - :type targets: tensor - - :returns: loss - :rtype: tensor - - - -.. py:class:: TensorflowKnowledgeDistillationLossWrapper(param_dict) - - Bases: :py:obj:`object` - - TensorflowKnowledgeDistillationLossWrapper wraps TensorflowKnowledgeDistillationLoss. - - .. py:method:: _param_check() - - - .. py:method:: __call__(**kwargs) - - Return TensorflowKnowledgeDistillationLoss, param dict. - - :returns: TensorflowKnowledgeDistillationLoss - param dict (dict): param dict - :rtype: class - - - -.. py:class:: TensorflowKnowledgeDistillationLossExternal(temperature=1.0, loss_types=['CE', 'CE'], loss_weights=[0.5, 0.5], student_model=None, teacher_model=None) - - Bases: :py:obj:`KnowledgeDistillationLoss` - - TensorflowKnowledgeDistillationLossExternal inherits from KnowledgeDistillationLoss. + neural_compressor.experimental.common.criterion diff --git a/docs/source/io_doc/api-documentation/common/data/basic_nas.rst b/docs/source/io_doc/api-documentation/common/data/basic_nas.rst deleted file mode 100644 index 4e6785d5e7f..00000000000 --- a/docs/source/io_doc/api-documentation/common/data/basic_nas.rst +++ /dev/null @@ -1,13 +0,0 @@ -.. _data-api - -basic_nas -########## - -.. .. automodule:: neural_compressor.objective -.. :members: - -.. .. autoclass:: neural_compressor.objective.Measurer -.. :members: - -.. .. autoclass:: neural_compressor.objective.FootprintMeasure -.. :members: \ No newline at end of file diff --git a/docs/source/io_doc/api-documentation/common/data/dataloader.rst b/docs/source/io_doc/api-documentation/common/data/dataloader.rst index 5c87629ccfd..38809581d3a 100644 --- a/docs/source/io_doc/api-documentation/common/data/dataloader.rst +++ b/docs/source/io_doc/api-documentation/common/data/dataloader.rst @@ -6,7 +6,7 @@ BaseDataLoader .. autoapisummary:: - neural_compressor.experimental.data.dataloaders.base_dataloader.BaseDataLoader + neural_compressor.experimental.data.dataloaders.base_dataloader dataloaders ------------ diff --git a/docs/source/io_doc/api-documentation/common/data/transforms.rst b/docs/source/io_doc/api-documentation/common/data/transforms.rst index 7e452b8910c..d9e63e1fc3e 100644 --- a/docs/source/io_doc/api-documentation/common/data/transforms.rst +++ b/docs/source/io_doc/api-documentation/common/data/transforms.rst @@ -1,1562 +1,6 @@ -transform -================================================================== - -.. py:module:: neural_compressor.experimental.data.transforms.transform - -.. autoapi-nested-parse:: - - Neural Compressor built-in Transforms on multiple framework backends. - - - -Module Contents ---------------- - -Classes -~~~~~~~ - -.. autoapisummary:: - - neural_compressor.experimental.data.transforms.transform.Transforms - neural_compressor.experimental.data.transforms.transform.TensorflowTransforms - neural_compressor.experimental.data.transforms.transform.MXNetTransforms - neural_compressor.experimental.data.transforms.transform.PyTorchTransforms - neural_compressor.experimental.data.transforms.transform.ONNXRTQLTransforms - neural_compressor.experimental.data.transforms.transform.ONNXRTITTransforms - neural_compressor.experimental.data.transforms.transform.TRANSFORMS - neural_compressor.experimental.data.transforms.transform.BaseTransform - neural_compressor.experimental.data.transforms.transform.TensorflowWrapFunction - neural_compressor.experimental.data.transforms.transform.TensorflowTransform - neural_compressor.experimental.data.transforms.transform.PytorchMxnetWrapFunction - neural_compressor.experimental.data.transforms.transform.PytorchMxnetTransform - neural_compressor.experimental.data.transforms.transform.ComposeTransform - neural_compressor.experimental.data.transforms.transform.CropToBoundingBox - neural_compressor.experimental.data.transforms.transform.MXNetCropToBoundingBox - neural_compressor.experimental.data.transforms.transform.ONNXRTCropToBoundingBox - neural_compressor.experimental.data.transforms.transform.TensorflowCropToBoundingBox - neural_compressor.experimental.data.transforms.transform.ResizeWithRatio - neural_compressor.experimental.data.transforms.transform.TensorflowResizeWithRatio - neural_compressor.experimental.data.transforms.transform.Transpose - neural_compressor.experimental.data.transforms.transform.TensorflowTranspose - neural_compressor.experimental.data.transforms.transform.MXNetTranspose - neural_compressor.experimental.data.transforms.transform.PyTorchTranspose - neural_compressor.experimental.data.transforms.transform.RandomVerticalFlip - neural_compressor.experimental.data.transforms.transform.TensorflowRandomVerticalFlip - neural_compressor.experimental.data.transforms.transform.RandomHorizontalFlip - neural_compressor.experimental.data.transforms.transform.TensorflowRandomHorizontalFlip - neural_compressor.experimental.data.transforms.transform.ToArray - neural_compressor.experimental.data.transforms.transform.CastTFTransform - neural_compressor.experimental.data.transforms.transform.CastONNXTransform - neural_compressor.experimental.data.transforms.transform.CastPyTorchTransform - neural_compressor.experimental.data.transforms.transform.CenterCropTFTransform - neural_compressor.experimental.data.transforms.transform.PaddedCenterCropTransform - neural_compressor.experimental.data.transforms.transform.ResizeTFTransform - neural_compressor.experimental.data.transforms.transform.ResizePytorchTransform - neural_compressor.experimental.data.transforms.transform.RandomCropTFTransform - neural_compressor.experimental.data.transforms.transform.RandomResizedCropPytorchTransform - neural_compressor.experimental.data.transforms.transform.RandomResizedCropMXNetTransform - neural_compressor.experimental.data.transforms.transform.RandomResizedCropTFTransform - neural_compressor.experimental.data.transforms.transform.NormalizeTFTransform - neural_compressor.experimental.data.transforms.transform.RescaleKerasPretrainTransform - neural_compressor.experimental.data.transforms.transform.RescaleTFTransform - neural_compressor.experimental.data.transforms.transform.RescaleTransform - neural_compressor.experimental.data.transforms.transform.AlignImageChannelTransform - neural_compressor.experimental.data.transforms.transform.PyTorchAlignImageChannel - neural_compressor.experimental.data.transforms.transform.ToNDArrayTransform - neural_compressor.experimental.data.transforms.transform.ResizeMXNetTransform - neural_compressor.experimental.data.transforms.transform.ResizeTransform - neural_compressor.experimental.data.transforms.transform.CropResizeTFTransform - neural_compressor.experimental.data.transforms.transform.PyTorchCropResizeTransform - neural_compressor.experimental.data.transforms.transform.MXNetCropResizeTransform - neural_compressor.experimental.data.transforms.transform.CropResizeTransform - neural_compressor.experimental.data.transforms.transform.CenterCropTransform - neural_compressor.experimental.data.transforms.transform.MXNetNormalizeTransform - neural_compressor.experimental.data.transforms.transform.PyTorchNormalizeTransform - neural_compressor.experimental.data.transforms.transform.NormalizeTransform - neural_compressor.experimental.data.transforms.transform.RandomCropTransform - neural_compressor.experimental.data.transforms.transform.RandomResizedCropTransform - neural_compressor.experimental.data.transforms.transform.SquadExample - neural_compressor.experimental.data.transforms.transform.InputFeatures - neural_compressor.experimental.data.transforms.transform.CollectTransform - neural_compressor.experimental.data.transforms.transform.TFSquadV1PostTransform - neural_compressor.experimental.data.transforms.transform.TFModelZooCollectTransform - neural_compressor.experimental.data.transforms.transform.TFSquadV1ModelZooPostTransform - neural_compressor.experimental.data.transforms.transform.ParseDecodeVocTransform - - - -Functions -~~~~~~~~~ - -.. autoapisummary:: - - neural_compressor.experimental.data.transforms.transform.transform_registry - neural_compressor.experimental.data.transforms.transform.get_torchvision_map - neural_compressor.experimental.data.transforms.transform._compute_softmax - neural_compressor.experimental.data.transforms.transform._get_best_indexes - neural_compressor.experimental.data.transforms.transform.get_final_text - neural_compressor.experimental.data.transforms.transform.read_squad_examples - neural_compressor.experimental.data.transforms.transform._check_is_max_context - neural_compressor.experimental.data.transforms.transform.convert_examples_to_features - - - -Attributes -~~~~~~~~~~ +Transforms +============== .. autoapisummary:: - neural_compressor.experimental.data.transforms.transform.torchvision - neural_compressor.experimental.data.transforms.transform.torch - neural_compressor.experimental.data.transforms.transform.tf - neural_compressor.experimental.data.transforms.transform.mx - neural_compressor.experimental.data.transforms.transform.cv2 - neural_compressor.experimental.data.transforms.transform.framework_transforms - neural_compressor.experimental.data.transforms.transform.TENSORFLOW_TRANSFORMS - neural_compressor.experimental.data.transforms.transform.TENSORFLOW_ITEX_TRANSFORMS - neural_compressor.experimental.data.transforms.transform.MXNET_TRANSFORMS - neural_compressor.experimental.data.transforms.transform.PYTORCH_TRANSFORMS - neural_compressor.experimental.data.transforms.transform.ONNXRT_QL_TRANSFORMS - neural_compressor.experimental.data.transforms.transform.ONNXRT_IT_TRANSFORMS - neural_compressor.experimental.data.transforms.transform.registry_transforms - neural_compressor.experimental.data.transforms.transform.interpolation_map - neural_compressor.experimental.data.transforms.transform.interpolation_pytorch_map - neural_compressor.experimental.data.transforms.transform.interpolation_mxnet_map - neural_compressor.experimental.data.transforms.transform.np_dtype_map - - -.. py:data:: torchvision - - - - -.. py:data:: torch - - - - -.. py:data:: tf - - - - -.. py:data:: mx - - - - -.. py:data:: cv2 - - - - -.. py:class:: Transforms(process, concat_general=True) - - Bases: :py:obj:`object` - - INC supports built-in preprocessing, postprocessing and general methods on different framework backends. - - Transforms base class provides the abstract methods. - Users can also register their own Transforms classes by inheriting this base class. - - .. py:method:: _get_preprocess() - :abstractmethod: - - Abstract method to get preprocessing method. - - - .. py:method:: _get_postprocess() - :abstractmethod: - - Abstract method to get postprocess method. - - - .. py:method:: _get_general() - :abstractmethod: - - Abstract method to get general method. - - - -.. py:class:: TensorflowTransforms(process, concat_general=True) - - Bases: :py:obj:`Transforms` - - Tensorflow Transforms subclass. - - .. py:method:: _get_preprocess() - - Tensorflow get preprocess method. - - :returns: a dict including all the registered preprocess methods - :rtype: preprocess - - - .. py:method:: _get_postprocess() - - Tensorflow get postprocess method. - - :returns: a dict including all the registered postprocess methods - :rtype: postprocess - - - .. py:method:: _get_general() - - Tensorflow get general method. - - :returns: a dict including all the registered general methods - :rtype: general - - - -.. py:class:: MXNetTransforms(process, concat_general=True) - - Bases: :py:obj:`Transforms` - - Mxnet Transforms subclass. - - .. py:method:: _get_preprocess() - - Mxnet get preprocess method. - - :returns: a dict including all the registered preprocess methods - :rtype: preprocess - - - .. py:method:: _get_postprocess() - - Mxnet get postprocess method. - - :returns: a dict including all the registered postprocess methods - :rtype: postprocess - - - .. py:method:: _get_general() - - Mxnet get general method. - - :returns: a dict including all the registered general methods - :rtype: general - - - -.. py:class:: PyTorchTransforms(process, concat_general=True) - - Bases: :py:obj:`Transforms` - - Pytorch Transforms subclass. - - .. py:method:: _get_preprocess() - - Pytorch get preprocessing method. - - :returns: a dict including all the registered preprocess methods - :rtype: preprocess - - - .. py:method:: _get_postprocess() - - Pytorch get postprocess method. - - :returns: a dict including all the registered postprocess methods - :rtype: postprocess - - - .. py:method:: _get_general() - - Pytorch get general method. - - :returns: a dict including all the registered general methods - :rtype: general - - - -.. py:class:: ONNXRTQLTransforms(process, concat_general=True) - - Bases: :py:obj:`Transforms` - - Onnxrt_qlinearops Transforms subclass. - - .. py:method:: _get_preprocess() - - Onnxrt_qlinearops get preprocessing method. - - :returns: a dict including all the registered preprocess methods - :rtype: preprocess - - - .. py:method:: _get_postprocess() - - Onnxrt_qlinearops get postprocess method. - - :returns: a dict including all the registered postprocess methods - :rtype: postprocess - - - .. py:method:: _get_general() - - Onnxrt_qlinearops get general method. - - :returns: a dict including all the registered general methods - :rtype: general - - - -.. py:class:: ONNXRTITTransforms(process, concat_general=True) - - Bases: :py:obj:`Transforms` - - Onnxrt_integerops Transforms subclass. - - .. py:method:: _get_preprocess() - - Onnxrt_integerops get preprocessing method. - - :returns: a dict including all the registered preprocess methods - :rtype: preprocess - - - .. py:method:: _get_postprocess() - - Onnxrt_integerops get postprocess method. - - :returns: a dict including all the registered postprocess methods - :rtype: postprocess - - - .. py:method:: _get_general() - - Onnxrt_integerops get general method. - - :returns: a dict including all the registered general methods - :rtype: general - - - -.. py:data:: framework_transforms - - - - -.. py:data:: TENSORFLOW_TRANSFORMS - - - - -.. py:data:: TENSORFLOW_ITEX_TRANSFORMS - - - - -.. py:data:: MXNET_TRANSFORMS - - - - -.. py:data:: PYTORCH_TRANSFORMS - - - - -.. py:data:: ONNXRT_QL_TRANSFORMS - - - - -.. py:data:: ONNXRT_IT_TRANSFORMS - - - - -.. py:data:: registry_transforms - - - - -.. py:class:: TRANSFORMS(framework, process) - - Bases: :py:obj:`object` - - Transforms collection class. - - Provide register method to register new Transforms - and provide __getitem__ method to get Transforms according to Transforms type. - - .. py:method:: __getitem__(transform_type) - - Get Transform according to Transforms type. - - :param transform_type: the value can be preprocess, postprocess or general - :type transform_type: str - - :returns: the registered Transforms - :rtype: Transforms - - - .. py:method:: register(name, transform_cls) - - Register new Transform according to Transforms type. - - :param name: process name - :type name: str - :param transform_cls: process function wrapper class - :type transform_cls: class - - - -.. py:function:: transform_registry(transform_type, process, framework) - - Class decorator used to register all transform subclasses. - - :param transform_type: Transform registration name - :type transform_type: str - :param process: support 3 process including 'preprocess', 'postprocess', 'general' - :type process: str - :param framework: support 4 framework including 'tensorflow', 'pytorch', 'mxnet', 'onnxrt' - :type framework: str - :param cls: The class of register. - :type cls: class - - :returns: The class of register. - :rtype: cls - - -.. py:class:: BaseTransform - - Bases: :py:obj:`object` - - The base class for transform. - - .. py:method:: __call__(*args, **kwargs) - :abstractmethod: - - __call__ method is needed when write user specific transform. - - - -.. py:class:: TensorflowWrapFunction(transform_func) - - Bases: :py:obj:`object` - - Tensorflow wrapper function class. - - .. py:method:: __call__(**kwargs) - - __call__ method. - - :returns: TensorflowTransform class - - - -.. py:class:: TensorflowTransform(transform_func, **kwargs) - - Bases: :py:obj:`BaseTransform` - - Tensorflow transform class, the subclass of BaseTransform. - - .. py:method:: __call__(sample) - - __call__ method. - - :returns: a tuple of image and lable which get from tensorflow tranform processing - - - -.. py:class:: PytorchMxnetWrapFunction(transform_func) - - Bases: :py:obj:`object` - - Pytorch and MXNet wrapper function class. - - .. py:method:: __call__(**args) - - __call__ method. - - :returns: PytorchMxnetTransform class - - - -.. py:class:: PytorchMxnetTransform(transform_func) - - Bases: :py:obj:`BaseTransform` - - Pytorch and Mxnet transform class, the subclass of BaseTransform. - - .. py:method:: __call__(sample) - - __call__ method. - - :returns: a tuple of image and lable which get from pytorch or mxnet tranform processing - - - -.. py:data:: interpolation_map - - - - -.. py:data:: interpolation_pytorch_map - - - - -.. py:data:: interpolation_mxnet_map - - - - -.. py:function:: get_torchvision_map(interpolation) - - Get torchvision interpolation map. - - -.. py:class:: ComposeTransform(transform_list) - - Bases: :py:obj:`BaseTransform` - - Composes several transforms together. - - :param transform_list: list of transforms to compose - :type transform_list: list of Transform objects - - :returns: tuple of processed image and label - :rtype: sample (tuple) - - .. py:method:: __call__(sample) - - Call transforms in transform_list. - - - -.. py:class:: CropToBoundingBox(offset_height, offset_width, target_height, target_width) - - Bases: :py:obj:`BaseTransform` - - Crops an image to a specified bounding box. - - :param offset_height: Vertical coordinate of the top-left corner of the result in the input - :type offset_height: int - :param offset_width: Horizontal coordinate of the top-left corner of the result in the input - :type offset_width: int - :param target_height: Height of the result - :type target_height: int - :param target_width: Width of the result - :type target_width: int - - :returns: tuple of processed image and label - - .. py:method:: __call__(sample) - - Call torchvision.transforms.functional.crop. - - - -.. py:class:: MXNetCropToBoundingBox(offset_height, offset_width, target_height, target_width) - - Bases: :py:obj:`CropToBoundingBox` - - Crops an image to a specified bounding box. - - :param offset_height: Vertical coordinate of the top-left corner of the result in the input - :type offset_height: int - :param offset_width: Horizontal coordinate of the top-left corner of the result in the input - :type offset_width: int - :param target_height: Height of the result - :type target_height: int - :param target_width: Width of the result - :type target_width: int - - :returns: tuple of processed image and label - - .. py:method:: __call__(sample) - - Call mx.image.fixed_crop. - - - -.. py:class:: ONNXRTCropToBoundingBox(offset_height, offset_width, target_height, target_width) - - Bases: :py:obj:`CropToBoundingBox` - - Crops an image to a specified bounding box. - - :param offset_height: Vertical coordinate of the top-left corner of the result in the input - :type offset_height: int - :param offset_width: Horizontal coordinate of the top-left corner of the result in the input - :type offset_width: int - :param target_height: Height of the result - :type target_height: int - :param target_width: Width of the result - :type target_width: int - - :returns: tuple of processed image and label - - .. py:method:: __call__(sample) - - Crop the image in sample. - - - -.. py:class:: TensorflowCropToBoundingBox(offset_height, offset_width, target_height, target_width) - - Bases: :py:obj:`CropToBoundingBox` - - Crops an image to a specified bounding box. - - :param offset_height: Vertical coordinate of the top-left corner of the result in the input - :type offset_height: int - :param offset_width: Horizontal coordinate of the top-left corner of the result in the input - :type offset_width: int - :param target_height: Height of the result - :type target_height: int - :param target_width: Width of the result - :type target_width: int - - :returns: tuple of processed image and label - - .. py:method:: __call__(sample) - - Crop the image in sample. - - - -.. py:class:: ResizeWithRatio(min_dim=800, max_dim=1365, padding=False, constant_value=0) - - Bases: :py:obj:`BaseTransform` - - Resize image with aspect ratio and pad it to max shape(optional). - - If the image is padded, the label will be processed at the same time. - The input image should be np.array. - - :param min_dim: Resizes the image such that its smaller dimension == min_dim - :type min_dim: int, default=800 - :param max_dim: Ensures that the image longest side doesn't exceed this value - :type max_dim: int, default=1365 - :param padding: If true, pads image with zeros so its size is max_dim x max_dim - :type padding: bool, default=False - - :returns: tuple of processed image and label - - .. py:method:: __call__(sample) - - Resize the image with ratio in sample. - - - -.. py:class:: TensorflowResizeWithRatio(min_dim=800, max_dim=1365, padding=False, constant_value=0) - - Bases: :py:obj:`BaseTransform` - - Resize image with aspect ratio and pad it to max shape(optional). - - If the image is padded, the label will be processed at the same time. - The input image should be np.array or tf.Tensor. - - :param min_dim: Resizes the image such that its smaller dimension == min_dim - :type min_dim: int, default=800 - :param max_dim: Ensures that the image longest side doesn't exceed this value - :type max_dim: int, default=1365 - :param padding: If true, pads image with zeros so its size is max_dim x max_dim - :type padding: bool, default=False - - :returns: tuple of processed image and label - - .. py:method:: __call__(sample) - - Resize the image with ratio in sample. - - - -.. py:class:: Transpose(perm) - - Bases: :py:obj:`BaseTransform` - - Transpose image according to perm. - - :param perm: A permutation of the dimensions of input image - :type perm: list - - :returns: tuple of processed image and label - - .. py:method:: __call__(sample) - - Transpose the image according to perm in sample. - - - -.. py:class:: TensorflowTranspose(perm) - - Bases: :py:obj:`Transpose` - - Transpose image according to perm. - - :param perm: A permutation of the dimensions of input image - :type perm: list - - :returns: tuple of processed image and label - - .. py:method:: __call__(sample) - - Transpose the image according to perm in sample. - - - -.. py:class:: MXNetTranspose(perm) - - Bases: :py:obj:`Transpose` - - Transpose image according to perm. - - :param perm: A permutation of the dimensions of input image - :type perm: list - - :returns: tuple of processed image and label - - .. py:method:: __call__(sample) - - Transpose the image according to perm in sample. - - - -.. py:class:: PyTorchTranspose(perm) - - Bases: :py:obj:`Transpose` - - Transpose image according to perm. - - :param perm: A permutation of the dimensions of input image - :type perm: list - - :returns: tuple of processed image and label - - .. py:method:: __call__(sample) - - Transpose the image according to perm in sample. - - - -.. py:class:: RandomVerticalFlip - - Bases: :py:obj:`BaseTransform` - - Vertically flip the given image randomly. - - :returns: tuple of processed image and label - - .. py:method:: __call__(sample) - - Vertically flip the image in sample. - - - -.. py:class:: TensorflowRandomVerticalFlip - - Bases: :py:obj:`BaseTransform` - - Vertically flip the given image randomly. - - :returns: tuple of processed image and label - - .. py:method:: __call__(sample) - - Vertically flip the image in sample. - - - -.. py:class:: RandomHorizontalFlip - - Bases: :py:obj:`BaseTransform` - - Horizontally flip the given image randomly. - - :returns: tuple of processed image and label - - .. py:method:: __call__(sample) - - Horizontally flip the image in sample. - - - -.. py:class:: TensorflowRandomHorizontalFlip - - Bases: :py:obj:`BaseTransform` - - Horizontally flip the given image randomly. - - :returns: tuple of processed image and label - - .. py:method:: __call__(sample) - - Horizontally flip the image in sample. - - - -.. py:class:: ToArray - - Bases: :py:obj:`BaseTransform` - - Convert PIL Image or NDArray to numpy array. - - :returns: tuple of processed image and label - - .. py:method:: __call__(sample) - - Convert image in sample to numpy array. - - - -.. py:data:: np_dtype_map - - - - -.. py:class:: CastTFTransform(dtype='float32') - - Bases: :py:obj:`BaseTransform` - - Convert image to given dtype. - - :param dtype: A dtype to convert image to - :type dtype: str, default='float32' - - :returns: tuple of processed image and label - - .. py:method:: __call__(sample) - - Convert image in sample to given dtype. - - - -.. py:class:: CastONNXTransform(dtype='float32') - - Bases: :py:obj:`BaseTransform` - - Convert image to given dtype. - - :param dtype: A dtype to convert image to - :type dtype: str, default='float32' - - :returns: tuple of processed image and label - - .. py:method:: __call__(sample) - - Convert image in sample to given dtype. - - - -.. py:class:: CastPyTorchTransform(dtype='float32') - - Bases: :py:obj:`BaseTransform` - - Convert image to given dtype. - - :param dtype: A dtype to convert image to - :type dtype: str, default='float32' - - :returns: tuple of processed image and label - - .. py:method:: __call__(sample) - - Convert image in sample to given dtype. - - - -.. py:class:: CenterCropTFTransform(size) - - Bases: :py:obj:`BaseTransform` - - Crops the given image at the center to the given size. - - :param size: Size of the result - :type size: list or int - - :returns: tuple of processed image and label - - .. py:method:: __call__(sample) - - Crops image in sample to the given size. - - - -.. py:class:: PaddedCenterCropTransform(size, crop_padding=0) - - Bases: :py:obj:`BaseTransform` - - Crops the given image at the center to the given size with padding. - - :param size: Size of the result - :type size: list or int - :param crop_padding: crop padding number - :type crop_padding: int - - :returns: tuple of processed image and label - - .. py:method:: __call__(sample) - - Crops image in sample to the given size with padding. - - - -.. py:class:: ResizeTFTransform(size, interpolation='bilinear') - - Bases: :py:obj:`BaseTransform` - - Resize the input image to the given size. - - :param size: Size of the result - :type size: list or int - :param interpolation: Desired interpolation type, - support 'bilinear', 'nearest', 'bicubic' - :type interpolation: str, default='bilinear' - - :returns: tuple of processed image and label - - .. py:method:: __call__(sample) - - Resize the input image in sample to the given size. - - - -.. py:class:: ResizePytorchTransform(size, interpolation='bilinear') - - Bases: :py:obj:`BaseTransform` - - Resize the input image to the given size. - - :param size: Size of the result - :type size: list or int - :param interpolation: Desired interpolation type, - support 'bilinear', 'nearest', 'bicubic' - :type interpolation: str, default='bilinear' - - :returns: tuple of processed image and label - - .. py:method:: __call__(sample) - - Resize the input image in sample to the given size. - - - -.. py:class:: RandomCropTFTransform(size) - - Bases: :py:obj:`BaseTransform` - - Crop the image at a random location to the given size. - - :param size: Size of the result - :type size: list or tuple or int - - :returns: tuple of processed image and label - - .. py:method:: __call__(sample) - - Crop the image in sample to the given size. - - - -.. py:class:: RandomResizedCropPytorchTransform(size, scale=(0.08, 1.0), ratio=(3.0 / 4.0, 4.0 / 3.0), interpolation='bilinear') - - Bases: :py:obj:`BaseTransform` - - Crop the given image to random size and aspect ratio. - - :param size: Size of the result - :type size: list or int - :param scale: range of size of the origin size cropped - :type scale: tuple or list, default=(0.08, 1.0) - :param ratio: range of aspect ratio of the origin aspect ratio cropped - :type ratio: tuple or list, default=(3. / 4., 4. / 3.) - :param interpolation: Desired interpolation type, support 'bilinear', 'nearest', 'bicubic' - :type interpolation: str, default='bilinear' - - :returns: tuple of processed image and label - - .. py:method:: __call__(sample) - - Crop the image in sample to the random size. - - - -.. py:class:: RandomResizedCropMXNetTransform(size, scale=(0.08, 1.0), ratio=(3.0 / 4.0, 4.0 / 3.0), interpolation='bilinear') - - Bases: :py:obj:`BaseTransform` - - Crop the given image to random size and aspect ratio. - - :param size: Size of the result - :type size: list or int - :param scale: range of size of the origin size cropped - :type scale: tuple or list, default=(0.08, 1.0) - :param ratio: range of aspect ratio of the origin aspect ratio cropped - :type ratio: tuple or list, default=(3. / 4., 4. / 3.) - :param interpolation: Desired interpolation type, support 'bilinear', 'nearest', 'bicubic' - :type interpolation: str, default='bilinear' - - :returns: tuple of processed image and label - - .. py:method:: __call__(sample) - - Crop the image in sample to the random size. - - - -.. py:class:: RandomResizedCropTFTransform(size, scale=(0.08, 1.0), ratio=(3.0 / 4.0, 4.0 / 3.0), interpolation='bilinear') - - Bases: :py:obj:`BaseTransform` - - Crop the given image to random size and aspect ratio. - - :param size: Size of the result - :type size: list or int - :param scale: range of size of the origin size cropped - :type scale: tuple or list, default=(0.08, 1.0) - :param ratio: range of aspect ratio of the origin aspect ratio cropped - :type ratio: tuple or list, default=(3. / 4., 4. / 3.) - :param interpolation: Desired interpolation type, support 'bilinear', 'nearest' - :type interpolation: str, default='bilinear' - - :returns: tuple of processed image and label - - .. py:method:: get_params(image, scale, ratio) - - Get the image prameters: position, height and width. - - - .. py:method:: __call__(sample) - - Crop the image in sample to the random size. - - - -.. py:class:: NormalizeTFTransform(mean=[0.0], std=[1.0], rescale=None) - - Bases: :py:obj:`BaseTransform` - - Normalize a image with mean and standard deviation. - - :param mean: means for each channel, if len(mean)=1, mean will be broadcasted to each channel, - otherwise its length should be same with the length of image shape - :type mean: list, default=[0.0] - :param std: stds for each channel, if len(std)=1, std will be broadcasted to each channel, - otherwise its length should be same with the length of image shape - :type std: list, default=[1.0] - - :returns: tuple of processed image and label - - .. py:method:: __call__(sample) - - Normalize the image in sample. - - - -.. py:class:: RescaleKerasPretrainTransform(rescale=None) - - Bases: :py:obj:`BaseTransform` - - Scale the values of image to [0,1]. - - :returns: tuple of processed image and label - - .. py:method:: __call__(sample) - - Scale the values of the image in sample. - - - -.. py:class:: RescaleTFTransform - - Bases: :py:obj:`BaseTransform` - - Scale the values of image to [0,1]. - - :returns: tuple of processed image and label - - .. py:method:: __call__(sample) - - Scale the values of the image in sample. - - - -.. py:class:: RescaleTransform - - Bases: :py:obj:`BaseTransform` - - Scale the values of image to [0,1]. - - :returns: tuple of processed image and label - - .. py:method:: __call__(sample) - - Scale the values of the image in sample. - - - -.. py:class:: AlignImageChannelTransform(dim=3) - - Bases: :py:obj:`BaseTransform` - - Align image channel, now just support [H,W]->[H,W,dim], [H,W,4]->[H,W,3] and [H,W,3]->[H,W]. - - Input image must be np.ndarray. - - :returns: tuple of processed image and label - - .. py:method:: __call__(sample) - - Align channel of the image in sample. - - - -.. py:class:: PyTorchAlignImageChannel(dim=3) - - Bases: :py:obj:`BaseTransform` - - Align image channel, now just support [H,W,4]->[H,W,3] and [H,W,3]->[H,W]. - - Input image must be PIL Image. - - :returns: tuple of processed image and label - - .. py:method:: __call__(sample) - - Align channel of the image in sample. - - - -.. py:class:: ToNDArrayTransform - - Bases: :py:obj:`BaseTransform` - - Convert np.array to NDArray. - - :returns: tuple of processed image and label - - .. py:method:: __call__(sample) - - Convert np.array of the image in sample. - - - -.. py:class:: ResizeMXNetTransform(size, interpolation='bilinear') - - Bases: :py:obj:`BaseTransform` - - Resize the input image to the given size. - - :param size: Size of the result - :type size: list or int - :param interpolation: Desired interpolation type, - support 'bilinear', 'nearest', 'bicubic' - :type interpolation: str, default='bilinear' - - :returns: tuple of processed image and label - - .. py:method:: __call__(sample) - - Resize the input image in sample to the given size. - - - -.. py:class:: ResizeTransform(size, interpolation='bilinear') - - Bases: :py:obj:`BaseTransform` - - Resize the input image to the given size. - - :param size: Size of the result - :type size: list or int - :param interpolation: Desired interpolation type, - :type interpolation: str, default='bilinear' - :param support 'bilinear': - :param 'nearest': - :param 'bicubic'.: - - :returns: tuple of processed image and label - - .. py:method:: __call__(sample) - - Resize the input image in sample to the given size. - - - -.. py:class:: CropResizeTFTransform(x, y, width, height, size, interpolation='bilinear') - - Bases: :py:obj:`BaseTransform` - - Crop the input image with given location and resize it. - - :param x: Left boundary of the cropping area - :type x: int - :param y: Top boundary of the cropping area - :type y: int - :param width: Width of the cropping area - :type width: int - :param height: Height of the cropping area - :type height: int - :param size: resize to new size after cropping - :type size: list or int - :param interpolation: Desired interpolation type, - support 'bilinear', 'nearest', 'bicubic' - :type interpolation: str, default='bilinear' - - :returns: tuple of processed image and label - - .. py:method:: __call__(sample) - - Resize the input image in sample with given location. - - - -.. py:class:: PyTorchCropResizeTransform(x, y, width, height, size, interpolation='bilinear') - - Bases: :py:obj:`BaseTransform` - - Crop the input image with given location and resize it. - - :param x: Left boundary of the cropping area - :type x: int - :param y: Top boundary of the cropping area - :type y: int - :param width: Width of the cropping area - :type width: int - :param height: Height of the cropping area - :type height: int - :param size: resize to new size after cropping - :type size: list or int - :param interpolation: Desired interpolation type, - support 'bilinear', 'nearest', 'bicubic' - :type interpolation: str, default='bilinear' - - :returns: tuple of processed image and label - - .. py:method:: __call__(sample) - - Resize the input image in sample with given location. - - - -.. py:class:: MXNetCropResizeTransform(x, y, width, height, size, interpolation='bilinear') - - Bases: :py:obj:`BaseTransform` - - Crop the input image with given location and resize it. - - :param x: Left boundary of the cropping area - :type x: int - :param y: Top boundary of the cropping area - :type y: int - :param width: Width of the cropping area - :type width: int - :param height: Height of the cropping area - :type height: int - :param size: resize to new size after cropping - :type size: list or int - :param interpolation: Desired interpolation type, - support 'bilinear', 'nearest', 'bicubic' - :type interpolation: str, default='bilinear' - - :returns: tuple of processed image and label - - .. py:method:: __call__(sample) - - Resize the input image in sample with given location. - - - -.. py:class:: CropResizeTransform(x, y, width, height, size, interpolation='bilinear') - - Bases: :py:obj:`BaseTransform` - - Crop the input image with given location and resize it. - - :param x: Left boundary of the cropping area - :type x: int - :param y: Top boundary of the cropping area - :type y: int - :param width: Width of the cropping area - :type width: int - :param height: Height of the cropping area - :type height: int - :param size: resize to new size after cropping - :type size: list or int - :param interpolation: Desired interpolation type, - support 'bilinear', 'nearest', 'bicubic' - :type interpolation: str, default='bilinear' - - :returns: tuple of processed image and label - - .. py:method:: __call__(sample) - - Crop the input image in sample with given location. - - - -.. py:class:: CenterCropTransform(size) - - Bases: :py:obj:`BaseTransform` - - Crops the given image at the center to the given size. - - :param size: Size of the result - :type size: list or int - - :returns: tuple of processed image and label - - .. py:method:: __call__(sample) - - Crop the input image in sample at the center to the given size. - - - -.. py:class:: MXNetNormalizeTransform(mean=[0.0], std=[1.0]) - - Bases: :py:obj:`BaseTransform` - - Normalize a image with mean and standard deviation. - - :param mean: means for each channel, if len(mean)=1, mean will be broadcasted to each channel, - otherwise its length should be same with the length of image shape - :type mean: list, default=[0.0] - :param std: stds for each channel, if len(std)=1, std will be broadcasted to each channel, - otherwise its length should be same with the length of image shape - :type std: list, default=[1.0] - - :returns: tuple of processed image and label - - .. py:method:: __call__(sample) - - Normalize the image in sample. - - - -.. py:class:: PyTorchNormalizeTransform(mean=[0.0], std=[1.0]) - - Bases: :py:obj:`MXNetNormalizeTransform` - - Normalize a image with mean and standard deviation. - - :param mean: means for each channel, if len(mean)=1, mean will be broadcasted to each channel, - otherwise its length should be same with the length of image shape - :type mean: list, default=[0.0] - :param std: stds for each channel, if len(std)=1, std will be broadcasted to each channel, - otherwise its length should be same with the length of image shape - :type std: list, default=[1.0] - - :returns: tuple of processed image and label - - .. py:method:: __call__(sample) - - Normalize the image in sample. - - - -.. py:class:: NormalizeTransform(mean=[0.0], std=[1.0]) - - Bases: :py:obj:`BaseTransform` - - Normalize a image with mean and standard deviation. - - :param mean: means for each channel, if len(mean)=1, mean will be broadcasted to each channel, - otherwise its length should be same with the length of image shape - :type mean: list, default=[0.0] - :param std: stds for each channel, if len(std)=1, std will be broadcasted to each channel, - otherwise its length should be same with the length of image shape - :type std: list, default=[1.0] - - :returns: tuple of processed image and label - - .. py:method:: __call__(sample) - - Normalize the image in sample. - - - -.. py:class:: RandomCropTransform(size) - - Bases: :py:obj:`BaseTransform` - - Crop the image at a random location to the given size. - - :param size: Size of the result - :type size: list or tuple or int - - :returns: tuple of processed image and label - - .. py:method:: __call__(sample) - - Crop the image in sample to the given size. - - - -.. py:class:: RandomResizedCropTransform(size, scale=(0.08, 1.0), ratio=(3.0 / 4.0, 4.0 / 3.0), interpolation='bilinear') - - Bases: :py:obj:`BaseTransform` - - Crop the given image to random size and aspect ratio. - - :param size: Size of the result - :type size: list or int - :param scale: range of size of the origin size cropped - :type scale: tuple or list, default=(0.08, 1.0) - :param ratio: range of aspect ratio of the origin aspect ratio cropped - :type ratio: tuple or list, default=(3. / 4., 4. / 3.) - :param interpolation: Desired interpolation type, support 'bilinear', 'nearest' - :type interpolation: str, default='bilinear' - - :returns: tuple of processed image and label - - .. py:method:: get_params(image, scale, ratio) - - Get the image prameters: position, height and width. - - - .. py:method:: __call__(sample) - - Crop the image in sample to random size. - - - -.. py:function:: _compute_softmax(scores) - - Compute softmax probability over raw logits. - - -.. py:function:: _get_best_indexes(logits, n_best_size) - - Get the n-best logits from a list. - - -.. py:function:: get_final_text(pred_text, orig_text, do_lower_case) - - Project the tokenized prediction back to the original text. - - -.. py:class:: SquadExample(qas_id, question_text, doc_tokens, orig_answer_text=None, start_position=None, end_position=None, is_impossible=False) - - Bases: :py:obj:`object` - - A single training/test example for simple sequence classification. - - For examples without an answer, the start and end position are -1. - - -.. py:class:: InputFeatures(unique_id, example_index, doc_span_index, tokens, token_to_orig_map, token_is_max_context, input_ids, input_mask, segment_ids, start_position=None, end_position=None, is_impossible=None) - - Bases: :py:obj:`object` - - A single set of features of data. - - -.. py:function:: read_squad_examples(input_file) - - Read a SQuAD json file into a list of SquadExample. - - -.. py:function:: _check_is_max_context(doc_spans, cur_span_index, position) - - Check if this is the 'max context' doc span for the token. - - -.. py:function:: convert_examples_to_features(examples, tokenizer, max_seq_length, doc_stride, max_query_length, output_fn) - - Load a data file into a list of `InputBatch`s. - - -.. py:class:: CollectTransform(length=10833) - - Bases: :py:obj:`BaseTransform` - - Postprocess the predictions, collect data. - - .. py:method:: __call__(sample) - - Collect postprocess data. - - - -.. py:class:: TFSquadV1PostTransform(label_file, vocab_file, n_best_size=20, max_seq_length=384, max_query_length=64, max_answer_length=30, do_lower_case=True, doc_stride=128) - - Bases: :py:obj:`BaseTransform` - - Postprocess the predictions of bert on SQuAD. - - :param label_file: path of label file - :type label_file: str - :param vocab_file: path of vocabulary file - :type vocab_file: str - :param n_best_size: The total number of n-best predictions to generate in nbest_predictions.json - :type n_best_size: int, default=20 - :param max_seq_length: The maximum total input sequence length after WordPiece tokenization. - Sequences longer than this will be truncated, shorter than this will be padded - :type max_seq_length: int, default=384 - :param max_query_length: The maximum number of tokens for the question. - Questions longer than this will be truncated to this length - :type max_query_length: int, default=64 - :param max_answer_length: The maximum length of an answer that can be generated. This is needed because - the start and end predictions are not conditioned on one another - :type max_answer_length: int, default=30 - :param do_lower_case: Whether to lower case the input text. - Should be True for uncased models and False for cased models - :type do_lower_case: bool, default=True - :param doc_stride: When splitting up a long document into chunks, - how much stride to take between chunks - :type doc_stride: int, default=128 - - :returns: tuple of processed prediction and label - - .. py:method:: process_result(results) - - Get the processed results. - - - .. py:method:: get_postprocess_result(sample) - - Get the post processed results. - - - .. py:method:: __call__(sample) - - Call the get_postprocess_result. - - - -.. py:class:: TFModelZooCollectTransform(length=10833) - - Bases: :py:obj:`CollectTransform` - - Postprocess the predictions of model zoo, collect data. - - .. py:method:: __call__(sample) - - Collect postprocess data. - - - -.. py:class:: TFSquadV1ModelZooPostTransform(label_file, vocab_file, n_best_size=20, max_seq_length=384, max_query_length=64, max_answer_length=30, do_lower_case=True, doc_stride=128) - - Bases: :py:obj:`TFSquadV1PostTransform` - - Postprocess the predictions of bert on SQuADV1.1. - - See class TFSquadV1PostTransform for more details - - .. py:method:: __call__(sample) - - Collect data and get postprocess results. - - - -.. py:class:: ParseDecodeVocTransform - - Bases: :py:obj:`BaseTransform` - - Parse features in Example proto. - - :returns: tuple of parsed image and labels - - .. py:method:: __call__(sample) - - Parse decode voc. - - + neural_compressor.experimental.data.transforms.transform \ No newline at end of file diff --git a/docs/source/io_doc/api-documentation/common/metric.rst b/docs/source/io_doc/api-documentation/common/metric.rst index 7e7919d2938..9349942d733 100644 --- a/docs/source/io_doc/api-documentation/common/metric.rst +++ b/docs/source/io_doc/api-documentation/common/metric.rst @@ -1,12 +1,6 @@ -Metric -########### +Metric +============== -The Metric API information is available: - -.. toctree:: - :maxdepth: 1 - - - metrics - bleu - +.. autoapisummary:: + neural_compressor.experimental.metric.metric + neural_compressor.experimental.metric.bleu \ No newline at end of file diff --git a/docs/source/io_doc/api-documentation/common/metrics.rst b/docs/source/io_doc/api-documentation/common/metrics.rst deleted file mode 100644 index 0ff2d234203..00000000000 --- a/docs/source/io_doc/api-documentation/common/metrics.rst +++ /dev/null @@ -1,622 +0,0 @@ -Metric -====================================================== - -.. py:module:: neural_compressor.experimental.metric.metric - -Module Contents ---------------- - -Classes -~~~~~~~ - -.. autoapisummary:: - - neural_compressor.experimental.metric.metric.BaseMetric - neural_compressor.experimental.metric.metric.F1 - neural_compressor.experimental.metric.metric.Accuracy - neural_compressor.experimental.metric.metric.PyTorchLoss - neural_compressor.experimental.metric.metric.Loss - neural_compressor.experimental.metric.metric.MAE - neural_compressor.experimental.metric.metric.RMSE - neural_compressor.experimental.metric.metric.MSE - neural_compressor.experimental.metric.metric.TensorflowTopK - neural_compressor.experimental.metric.metric.GeneralTopK - neural_compressor.experimental.metric.metric.COCOmAPv2 - neural_compressor.experimental.metric.metric.TensorflowMAP - neural_compressor.experimental.metric.metric.TensorflowCOCOMAP - neural_compressor.experimental.metric.metric.TensorflowVOCMAP - neural_compressor.experimental.metric.metric.SquadF1 - neural_compressor.experimental.metric.metric.mIOU - neural_compressor.experimental.metric.metric.ONNXRTGLUE - neural_compressor.experimental.metric.metric.ROC - - - - - -.. py:class:: BaseMetric(metric, single_output=False, hvd=None) - - Bases: :py:obj:`object` - - The base class of Metric. - - .. py:method:: __call__(*args, **kwargs) - - Evaluate the model predictions, and the reference. - - :returns: The class itself. - - - .. py:method:: update(preds, labels=None, sample_weight=None) - :abstractmethod: - - Update the state that need to be evaluated. - - :param preds: The prediction result. - :param labels: The reference. Defaults to None. - :param sample_weight: The sampling weight. Defaults to None. - - :raises NotImplementedError: The method should be implemented by subclass. - - - .. py:method:: reset() - :abstractmethod: - - Clear the predictions and labels. - - :raises NotImplementedError: The method should be implemented by subclass. - - - .. py:method:: result() - :abstractmethod: - - Evaluate the difference between predictions and labels. - - :raises NotImplementedError: The method should be implemented by subclass. - - - .. py:method:: metric() - :property: - - Return its metric class. - - :returns: The metric class. - - - .. py:method:: hvd() - :property: - - Return its hvd class. - - :returns: The hvd class. - - - -.. py:class:: F1 - - Bases: :py:obj:`BaseMetric` - - F1 score of a binary classification problem. - - The F1 score is the harmonic mean of the precision and recall. - It can be computed with the equation: - F1 = 2 * (precision * recall) / (precision + recall) - - .. py:method:: update(preds, labels) - - Add the predictions and labels. - - :param preds: The predictions. - :param labels: The labels corresponding to the predictions. - - - .. py:method:: reset() - - Clear the predictions and labels. - - - .. py:method:: result() - - Compute the F1 score. - - - - - - - - -.. py:class:: Accuracy - - Bases: :py:obj:`BaseMetric` - - The Accuracy for the classification tasks. - - The accuracy score is the proportion of the total number of predictions - that were correct classified. - - .. attribute:: pred_list - - List of prediction to score. - - .. attribute:: label_list - - List of labels to score. - - .. attribute:: sample - - The total number of samples. - - .. py:method:: update(preds, labels, sample_weight=None) - - Add the predictions and labels. - - :param preds: The predictions. - :param labels: The labels corresponding to the predictions. - :param sample_weight: The sample weight. - - - .. py:method:: reset() - - Clear the predictions and labels. - - - .. py:method:: result() - - Compute the accuracy. - - - -.. py:class:: PyTorchLoss - - A dummy PyTorch Metric. - - A dummy metric that computes the average of predictions and prints it directly. - - .. py:method:: reset() - - Reset the number of samples and total cases to zero. - - - .. py:method:: update(output) - - Add the predictions. - - :param output: The predictions. - - - .. py:method:: compute() - - Compute the average of predictions. - - :raises ValueError: There must have at least one example. - - :returns: The dummy loss. - - - -.. py:class:: Loss - - Bases: :py:obj:`BaseMetric` - - A dummy Metric. - - A dummy metric that computes the average of predictions and prints it directly. - - .. attribute:: sample - - The number of samples. - - .. attribute:: sum - - The sum of prediction. - - .. py:method:: update(preds, labels, sample_weight=None) - - Add the predictions and labels. - - :param preds: The predictions. - :param labels: The labels corresponding to the predictions. - :param sample_weight: The sample weight. - - - .. py:method:: reset() - - Reset the number of samples and total cases to zero. - - - .. py:method:: result() - - Compute the average of predictions. - - :returns: The dummy loss. - - - -.. py:class:: MAE(compare_label=True) - - Bases: :py:obj:`BaseMetric` - - Computes Mean Absolute Error (MAE) loss. - - Mean Absolute Error (MAE) is the mean of the magnitude of - difference between the predicted and actual numeric values. - - .. attribute:: pred_list - - List of prediction to score. - - .. attribute:: label_list - - List of references corresponding to the prediction result. - - .. attribute:: compare_label - - Whether to compare label. False if there are no - labels and will use FP32 preds as labels. - - :type: bool - - .. py:method:: update(preds, labels, sample_weight=None) - - Add the predictions and labels. - - :param preds: The predictions. - :param labels: The labels corresponding to the predictions. - :param sample_weight: The sample weight. - - - .. py:method:: reset() - - Clear the predictions and labels. - - - .. py:method:: result() - - Compute the MAE score. - - :returns: The MAE score. - - - -.. py:class:: RMSE(compare_label=True) - - Bases: :py:obj:`BaseMetric` - - Computes Root Mean Squared Error (RMSE) loss. - - .. attribute:: mse - - The instance of MSE Metric. - - .. py:method:: update(preds, labels, sample_weight=None) - - Add the predictions and labels. - - :param preds: The predictions. - :param labels: The labels corresponding to the predictions. - :param sample_weight: The sample weight. - - - .. py:method:: reset() - - Clear the predictions and labels. - - - .. py:method:: result() - - Compute the RMSE score. - - :returns: The RMSE score. - - - -.. py:class:: MSE(compare_label=True) - - Bases: :py:obj:`BaseMetric` - - Computes Mean Squared Error (MSE) loss. - - Mean Squared Error(MSE) represents the average of the squares of errors. - For example, the average squared difference between the estimated values - and the actual values. - - .. attribute:: pred_list - - List of prediction to score. - - .. attribute:: label_list - - List of references corresponding to the prediction result. - - .. attribute:: compare_label - - Whether to compare label. False if there are no labels - and will use FP32 preds as labels. - - :type: bool - - .. py:method:: update(preds, labels, sample_weight=None) - - Add the predictions and labels. - - :param preds: The predictions. - :param labels: The labels corresponding to the predictions. - :param sample_weight: The sample weight. - - - .. py:method:: reset() - - Clear the predictions and labels. - - - .. py:method:: result() - - Compute the MSE score. - - :returns: The MSE score. - - - -.. py:class:: TensorflowTopK(k=1) - - Bases: :py:obj:`BaseMetric` - - Compute Top-k Accuracy classification score for Tensorflow model. - - This metric computes the number of times where the correct label is among - the top k labels predicted. - - .. attribute:: k - - The number of most likely outcomes considered to find the correct label. - - :type: int - - .. attribute:: num_correct - - The number of predictions that were correct classified. - - .. attribute:: num_sample - - The total number of predictions. - - .. py:method:: update(preds, labels, sample_weight=None) - - Add the predictions and labels. - - :param preds: The predictions. - :param labels: The labels corresponding to the predictions. - :param sample_weight: The sample weight. - - - .. py:method:: reset() - - Reset the number of samples and correct predictions. - - - .. py:method:: result() - - Compute the top-k score. - - :returns: The top-k score. - - - -.. py:class:: GeneralTopK(k=1) - - Bases: :py:obj:`BaseMetric` - - Compute Top-k Accuracy classification score. - - This metric computes the number of times where the correct label is among - the top k labels predicted. - - .. attribute:: k - - The number of most likely outcomes considered to find the correct label. - - :type: int - - .. attribute:: num_correct - - The number of predictions that were correct classified. - - .. attribute:: num_sample - - The total number of predictions. - - .. py:method:: update(preds, labels, sample_weight=None) - - Add the predictions and labels. - - :param preds: The predictions. - :param labels: The labels corresponding to the predictions. - :param sample_weight: The sample weight. - - - .. py:method:: reset() - - Reset the number of samples and correct predictions. - - - .. py:method:: result() - - Compute the top-k score. - - :returns: The top-k score. - - - -.. py:class:: COCOmAPv2(anno_path=None, iou_thrs='0.5:0.05:0.95', map_points=101, map_key='DetectionBoxes_Precision/mAP', output_index_mapping={'num_detections': -1, 'boxes': 0, 'scores': 1, 'classes': 2}) - - Bases: :py:obj:`BaseMetric` - - Compute mean average precision of the detection task. - - .. py:method:: update(predicts, labels, sample_weight=None) - - Add the predictions and labels. - - :param predicts: The predictions. - :param labels: The labels corresponding to the predictions. - :param sample_weight: The sample weight. Defaults to None. - - - .. py:method:: reset() - - Reset the prediction and labels. - - - .. py:method:: result() - - Compute mean average precision. - - :returns: The mean average precision score. - - - -.. py:class:: TensorflowMAP(anno_path=None, iou_thrs=0.5, map_points=0, map_key='DetectionBoxes_Precision/mAP') - - Bases: :py:obj:`BaseMetric` - - Computes mean average precision. - - .. py:method:: update(predicts, labels, sample_weight=None) - - Add the predictions and labels. - - :param predicts: The predictions. - :param labels: The labels corresponding to the predictions. - :param sample_weight: The sample weight. - - - .. py:method:: reset() - - Reset the prediction and labels. - - - .. py:method:: result() - - Compute mean average precision. - - :returns: The mean average precision score. - - - -.. py:class:: TensorflowCOCOMAP(anno_path=None, iou_thrs=None, map_points=None, map_key='DetectionBoxes_Precision/mAP') - - Bases: :py:obj:`TensorflowMAP` - - Computes mean average precision using algorithm in COCO. - - -.. py:class:: TensorflowVOCMAP(anno_path=None, iou_thrs=None, map_points=None, map_key='DetectionBoxes_Precision/mAP') - - Bases: :py:obj:`TensorflowMAP` - - Computes mean average precision using algorithm in VOC. - - -.. py:class:: SquadF1 - - Bases: :py:obj:`BaseMetric` - - Evaluate for v1.1 of the SQuAD dataset. - - .. py:method:: update(preds, labels, sample_weight=None) - - Add the predictions and labels. - - :param preds: The predictions. - :param labels: The labels corresponding to the predictions. - :param sample_weight: The sample weight. - - - .. py:method:: reset() - - Reset the score list. - - - .. py:method:: result() - - Compute F1 score. - - - -.. py:class:: mIOU(num_classes=21) - - Bases: :py:obj:`BaseMetric` - - Compute the mean IOU(Intersection over Union) score. - - .. py:method:: update(preds, labels) - - Add the predictions and labels. - - :param preds: The predictions. - :param labels: The labels corresponding to the predictions. - - - .. py:method:: reset() - - Reset the hist. - - - .. py:method:: result() - - Compute mean IOU. - - :returns: The mean IOU score. - - - -.. py:class:: ONNXRTGLUE(task='mrpc') - - Bases: :py:obj:`BaseMetric` - - Compute the GLUE score. - - .. py:method:: update(preds, labels) - - Add the predictions and labels. - - :param preds: The predictions. - :param labels: The labels corresponding to the predictions. - - - .. py:method:: reset() - - Reset the prediction and labels. - - - .. py:method:: result() - - Compute the GLUE score. - - - -.. py:class:: ROC(task='dlrm') - - Bases: :py:obj:`BaseMetric` - - Computes ROC score. - - .. py:method:: update(preds, labels) - - Add the predictions and labels. - - :param preds: The predictions. - :param labels: The labels corresponding to the predictions. - - - .. py:method:: reset() - - Reset the prediction and labels. - - - .. py:method:: result() - - Compute the ROC score. - - diff --git a/docs/source/io_doc/api-documentation/common/model.rst b/docs/source/io_doc/api-documentation/common/model.rst index 3f55747449f..b632b177c65 100644 --- a/docs/source/io_doc/api-documentation/common/model.rst +++ b/docs/source/io_doc/api-documentation/common/model.rst @@ -1,7 +1,6 @@ -.. _quantization-api - Model -############ +============== + +.. autoapisummary:: -.. .. automodule:: neural_compressor.quantization -.. :members: \ No newline at end of file + neural_compressor.experimental.common.model diff --git a/docs/source/io_doc/api-documentation/common/optimizer.rst b/docs/source/io_doc/api-documentation/common/optimizer.rst index 655522a8c3a..e714f856f7e 100644 --- a/docs/source/io_doc/api-documentation/common/optimizer.rst +++ b/docs/source/io_doc/api-documentation/common/optimizer.rst @@ -1,203 +1,5 @@ Optimizer -========================================================= - -.. py:module:: neural_compressor.experimental.common.optimizer - -.. autoapi-nested-parse:: - - Intel Neural Compressor built-in Optimizers on multiple framework backends. - - -Module Contents ---------------- - -Classes -~~~~~~~ +============== .. autoapisummary:: - - neural_compressor.experimental.common.optimizer.TensorflowOptimizers - neural_compressor.experimental.common.optimizer.PyTorchOptimizers - neural_compressor.experimental.common.optimizer.Optimizers - neural_compressor.experimental.common.optimizer.TensorFlowSGD - neural_compressor.experimental.common.optimizer.TensorFlowAdamW - neural_compressor.experimental.common.optimizer.TensorFlowAdam - neural_compressor.experimental.common.optimizer.PyTorchSGD - - - -Functions -~~~~~~~~~ - -.. autoapisummary:: - - neural_compressor.experimental.common.optimizer.optimizer_registry - - - -Attributes -~~~~~~~~~~ - -.. autoapisummary:: - - neural_compressor.experimental.common.optimizer.torch - neural_compressor.experimental.common.optimizer.tf - neural_compressor.experimental.common.optimizer.tfa - neural_compressor.experimental.common.optimizer.framework_optimizers - neural_compressor.experimental.common.optimizer.TENSORFLOW_OPTIMIZERS - neural_compressor.experimental.common.optimizer.PYTORCH_OPTIMIZERS - neural_compressor.experimental.common.optimizer.registry_optimizers - - -.. py:data:: torch - - - - -.. py:data:: tf - - - - -.. py:data:: tfa - - - - -.. py:class:: TensorflowOptimizers - - Bases: :py:obj:`object` - - Class to get all registered TensorFlow Optimizers once only. - - -.. py:class:: PyTorchOptimizers - - Bases: :py:obj:`object` - - Class to get all registered PyTorch Optimizers once only. - - -.. py:data:: framework_optimizers - - - - -.. py:data:: TENSORFLOW_OPTIMIZERS - - - - -.. py:data:: PYTORCH_OPTIMIZERS - - - - -.. py:data:: registry_optimizers - - - - -.. py:class:: Optimizers(framework) - - Bases: :py:obj:`object` - - Main entry to get the specific type of optimizer. - - .. py:method:: __getitem__(optimizer_type) - - Return the specific type of optimizer object according to the given optimizer_type. - - - .. py:method:: register(name, optimizer_cls) - - Allow registration of non-built-in optimizers. - - - -.. py:function:: optimizer_registry(optimizer_type, framework) - - Class decorator used to register all Optimizer subclasses. - - Cross framework optimizer is supported by add param as framework='tensorflow, pytorch' - - :param optimizer_type: The string of supported criterion. - :type optimizer_type: str - :param framework: The string of supported framework. - :type framework: str - - :returns: The class of register. - :rtype: cls - - -.. py:class:: TensorFlowSGD(param_dict) - - Bases: :py:obj:`object` - - TensorFlow keras SGD optimizer. - - :param param_dict: The dict of parameters setting by user for SGD optimizer - :type param_dict: dict - - .. py:method:: _mapping() - - - .. py:method:: __call__(**kwargs) - - Call `TensorFlowSGD` object. - - - -.. py:class:: TensorFlowAdamW(param_dict) - - Bases: :py:obj:`object` - - tensorflow_addons AdamW optimizer. - - :param param_dict: The dict of parameters setting by user for AdamW optimizer - :type param_dict: dict - - .. py:method:: _mapping() - - - .. py:method:: __call__(**kwargs) - - Call `TensorFlowAdamW` object. - - - -.. py:class:: TensorFlowAdam(param_dict) - - Bases: :py:obj:`object` - - tensorflow Adam optimizer. - - :param param_dict: The dict of parameters setting by user for Adam optimizer - :type param_dict: dict - - .. py:method:: _mapping() - - - .. py:method:: __call__(**kwargs) - - Call `TensorFlowAdam` object. - - - -.. py:class:: PyTorchSGD(param_dict) - - Bases: :py:obj:`object` - - PyTorch SGD optimizer. - - :param param_dict: The dict of parameters setting by user for SGD optimizer - :type param_dict: dict - - .. py:method:: _mapping() - - - .. py:method:: __call__(**kwargs) - - Call `PyTorchSGD` object. - - + neural_compressor.experimental.common.optimizer diff --git a/docs/source/io_doc/api-documentation/component/Benchmark.rst b/docs/source/io_doc/api-documentation/component/Benchmark.rst deleted file mode 100644 index d831ccef703..00000000000 --- a/docs/source/io_doc/api-documentation/component/Benchmark.rst +++ /dev/null @@ -1,13 +0,0 @@ -.. _data-api - -Benchmark -######### - -.. .. automodule:: neural_compressor.objective -.. :members: - -.. .. autoclass:: neural_compressor.objective.Measurer -.. :members: - -.. .. autoclass:: neural_compressor.objective.FootprintMeasure -.. :members: \ No newline at end of file diff --git a/docs/source/io_doc/api-documentation/component/Distillation.rst b/docs/source/io_doc/api-documentation/component/Distillation.rst index 0a453401d5e..7cb9766904a 100644 --- a/docs/source/io_doc/api-documentation/component/Distillation.rst +++ b/docs/source/io_doc/api-documentation/component/Distillation.rst @@ -1,13 +1,6 @@ -.. _data-api - Distillation -############# - -.. .. automodule:: neural_compressor.objective -.. :members: +============== -.. .. autoclass:: neural_compressor.objective.Measurer -.. :members: +.. autoapisummary:: -.. .. autoclass:: neural_compressor.objective.FootprintMeasure -.. :members: \ No newline at end of file + neural_compressor.experimental.distillation diff --git a/docs/source/io_doc/api-documentation/component/MixedPrecision.rst b/docs/source/io_doc/api-documentation/component/MixedPrecision.rst index 9341c8fd24e..6152894ac75 100644 --- a/docs/source/io_doc/api-documentation/component/MixedPrecision.rst +++ b/docs/source/io_doc/api-documentation/component/MixedPrecision.rst @@ -1,13 +1,6 @@ -.. _data-api - MixedPrecision -############### - -.. .. automodule:: neural_compressor.objective -.. :members: +================ -.. .. autoclass:: neural_compressor.objective.Measurer -.. :members: +.. autoapisummary:: -.. .. autoclass:: neural_compressor.objective.FootprintMeasure -.. :members: \ No newline at end of file + neural_compressor.experimental.mixed_precision \ No newline at end of file diff --git a/docs/source/io_doc/api-documentation/component/ModelConversion.rst b/docs/source/io_doc/api-documentation/component/ModelConversion.rst index 03552e260df..3ce2e1fb891 100644 --- a/docs/source/io_doc/api-documentation/component/ModelConversion.rst +++ b/docs/source/io_doc/api-documentation/component/ModelConversion.rst @@ -1,13 +1,6 @@ -.. _data-api - ModelConversion -################ - -.. .. automodule:: neural_compressor.objective -.. :members: +================ -.. .. autoclass:: neural_compressor.objective.Measurer -.. :members: +.. autoapisummary:: -.. .. autoclass:: neural_compressor.objective.FootprintMeasure -.. :members: \ No newline at end of file + neural_compressor.experimental.model_conversion diff --git a/docs/source/io_doc/api-documentation/component/Nas.rst b/docs/source/io_doc/api-documentation/component/Nas.rst index 0488db0aae3..d432c44beb0 100644 --- a/docs/source/io_doc/api-documentation/component/Nas.rst +++ b/docs/source/io_doc/api-documentation/component/Nas.rst @@ -1,120 +1,15 @@ -NAS -============================================ - -.. py:module:: neural_compressor.experimental.nas - - -.. autoapisummary:: - - neural_compressor.experimental.nas.basic_nas - neural_compressor.experimental.nas.dynas - +NAS +================ Package Contents ---------------- - -Classes -~~~~~~~ - .. autoapisummary:: - neural_compressor.experimental.nas.BasicNAS - neural_compressor.experimental.nas.DyNAS - neural_compressor.experimental.nas.NAS - - - - -.. py:class:: BasicNAS(conf_fname_or_obj, search_space=None, model_builder=None) - - Bases: :py:obj:`neural_compressor.experimental.nas.nas.NASBase`, :py:obj:`neural_compressor.experimental.component.Component` - - :param conf_fname: The path to the YAML configuration file. - :type conf_fname: string - :param search_space: A dictionary for defining the search space. - :type search_space: dict - :param model_builder: A function to build model instance with the specified - model architecture parameters. - :type model_builder: function obj - - .. py:method:: execute() - - Initialize the dataloader and train/eval functions from yaml config. - Component base class provides default function to initialize dataloaders and functions - from user config. And for derived classes(Pruning, Quantization, etc.), an override - function is required. - - - .. py:method:: estimate(model) - - Estimate performance of the model. - Depends on specific NAS algorithm. Here we use train and evaluate. - - :returns: Evaluated metrics of the model. - - - .. py:method:: init_by_cfg(conf_fname_or_obj) - - - .. py:method:: pre_process() - - Initialize the dataloader and train/eval functions from yaml config. - Component base class provides default function to initialize dataloaders and functions - from user config. And for derived classes(Pruning, Quantization, etc.), an override - function is required. - - - .. py:method:: __repr__() - - Return repr(self). - + neural_compressor.experimental.nas.nas +Classes +---------------- +.. autoapisummary:: -.. py:class:: DyNAS(conf_fname_or_obj) - - Bases: :py:obj:`neural_compressor.experimental.nas.nas.NASBase` - - :param conf_fname_or_obj: The path to the YAML configuration file or the object of NASConfig. - :type conf_fname_or_obj: string or obj - - .. py:method:: estimate(individual) - - Estimate performance of the model. Depends on specific NAS algorithm. - - :returns: Evaluated metrics of the model. - - - .. py:method:: init_for_search() - - - .. py:method:: search() - - NAS search process. - - :returns: Best model architecture found in search process. - - - .. py:method:: select_model_arch() - - Propose architecture of the model based on search algorithm for next search iteration. - - :returns: Model architecture description. - - - .. py:method:: create_acc_predictor() - - - .. py:method:: create_macs_predictor() - - - .. py:method:: create_latency_predictor() - - - .. py:method:: init_cfg(conf_fname_or_obj) - - - -.. py:class:: NAS - - Bases: :py:obj:`object` - + neural_compressor.experimental.nas.basic_nas + neural_compressor.experimental.nas.dynas \ No newline at end of file diff --git a/docs/source/io_doc/api-documentation/component/Pruning.rst b/docs/source/io_doc/api-documentation/component/Pruning.rst index a49e019381a..3bec7485947 100644 --- a/docs/source/io_doc/api-documentation/component/Pruning.rst +++ b/docs/source/io_doc/api-documentation/component/Pruning.rst @@ -1,13 +1,7 @@ -.. _data-api - Pruning -######### - -.. .. automodule:: neural_compressor.objective -.. :members: +============== -.. .. autoclass:: neural_compressor.objective.Measurer -.. :members: +.. autoapisummary:: -.. .. autoclass:: neural_compressor.objective.FootprintMeasure -.. :members: \ No newline at end of file + neural_compressor.experimental.pruning + neural_compressor.experimental.pytorch_pruner.pruning \ No newline at end of file diff --git a/docs/source/io_doc/api-documentation/component/Quantization.rst b/docs/source/io_doc/api-documentation/component/Quantization.rst index 6a2c5cfc985..afa6fc3cf75 100644 --- a/docs/source/io_doc/api-documentation/component/Quantization.rst +++ b/docs/source/io_doc/api-documentation/component/Quantization.rst @@ -1,13 +1,6 @@ -.. _data-api - Quantization -############# - -.. .. automodule:: neural_compressor.objective -.. :members: +============== -.. .. autoclass:: neural_compressor.objective.Measurer -.. :members: +.. autoapisummary:: -.. .. autoclass:: neural_compressor.objective.FootprintMeasure -.. :members: \ No newline at end of file + neural_compressor.experimental.quantization diff --git a/docs/source/io_doc/api-documentation/component/Scheduler.rst b/docs/source/io_doc/api-documentation/component/Scheduler.rst index 0c9942106ae..f74516a816e 100644 --- a/docs/source/io_doc/api-documentation/component/Scheduler.rst +++ b/docs/source/io_doc/api-documentation/component/Scheduler.rst @@ -1,13 +1,6 @@ -.. _data-api - scheduler -########## - -.. .. automodule:: neural_compressor.objective -.. :members: +============== -.. .. autoclass:: neural_compressor.objective.Measurer -.. :members: +.. autoapisummary:: -.. .. autoclass:: neural_compressor.objective.FootprintMeasure -.. :members: \ No newline at end of file + neural_compressor.experimental.scheduler From f34f69582be4a7712a18ec392df474dc8681b4b3 Mon Sep 17 00:00:00 2001 From: "Yue, Wenjiao" Date: Tue, 22 Nov 2022 09:38:25 +0800 Subject: [PATCH 07/17] modify IO docs --- README.md | 56 ++++++++-------- docs/Makefile | 12 ++-- docs/source/{io_doc => }/CODE_OF_CONDUCT.md | 0 docs/source/{io_doc => }/FX.md | 0 docs/source/{io_doc => }/NAS.md | 2 +- docs/source/{io_doc => }/PTQ.md | 0 docs/source/{io_doc => }/QAT.md | 2 +- docs/source/{io_doc => }/Quantization.md | 6 +- docs/source/{io_doc => }/README.md | 18 +++--- docs/source/_static/index.html | 2 +- docs/source/_templates/layout.html | 4 +- docs/source/{io_doc => }/adaptor.md | 0 .../api-documentation/adaptor.rst | 0 .../api-documentation/api-introduction.md | 0 .../{io_doc => }/api-documentation/apis.rst | 0 .../{io_doc => }/api-documentation/common.rst | 0 .../api-documentation/common/benchmark.rst | 0 .../api-documentation/common/bleu.rst | 0 .../api-documentation/common/criterion.rst | 0 .../api-documentation/common/data.rst | 0 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docs/source/{io_doc => }/tuning_strategies.md (99%) rename docs/source/{io_doc => }/validated_model_list.md (100%) rename docs/source/{io_doc => }/welcome.md (96%) diff --git a/README.md b/README.md index d7f7fda1447..522ac2e9402 100644 --- a/README.md +++ b/README.md @@ -44,7 +44,7 @@ Python version: 3.7, 3.8, 3.9, 3.10 # Or install nightly full version from pip (including GUI) pip install -i https://test.pypi.org/simple/ neural-compressor-full ``` -More installation methods can be found at [Installation Guide](./docs/source/io_doc/installation_guide.md). Please check out our [FAQ](./docs/source/io_doc/faq.md) for more details. +More installation methods can be found at [Installation Guide](./docs/source/installation_guide.md). Please check out our [FAQ](./docs/source/faq.md) for more details. ## Getting Started ### Quantization with Python API @@ -71,7 +71,7 @@ Search for ```jupyter-lab-neural-compressor``` in the Extension Manager in Jupyt Extension -### Quantization with [GUI](./docs/source/io_doc/bench.md) +### Quantization with [GUI](./docs/source/bench.md) ```shell # An ONNX Example pip install onnx==1.12.0 onnxruntime==1.12.1 onnxruntime-extensions @@ -98,7 +98,7 @@ inc_bench #### Intel® Neural Compressor quantized ONNX models support multiple hardware vendors through ONNX Runtime: -* Intel CPU, AMD/ARM CPU, and NVidia GPU. Please refer to the validated model [list](./docs/source/io_doc/validated_model_list.md#Validated-ONNX-QDQ-INT8-models-on-multiple-hardware-through-ONNX-Runtime). +* Intel CPU, AMD/ARM CPU, and NVidia GPU. Please refer to the validated model [list](./docs/source/validated_model_list.md#Validated-ONNX-QDQ-INT8-models-on-multiple-hardware-through-ONNX-Runtime). ### Validated Software Environment @@ -146,7 +146,7 @@ inc_bench > Set the environment variable ``TF_ENABLE_ONEDNN_OPTS=1`` to enable oneDNN optimizations if you are using TensorFlow v2.6 to v2.8. oneDNN is the default for TensorFlow v2.9. ### Validated Models -Intel® Neural Compressor validated 420+ [examples](./examples) for quantization with a performance speedup geomean of 2.2x and up to 4.2x on VNNI while minimizing accuracy loss. Over 30 pruning and knowledge distillation samples are also available. More details for validated models are available [here](./docs/source/io_doc/validated_model_list.md). +Intel® Neural Compressor validated 420+ [examples](./examples) for quantization with a performance speedup geomean of 2.2x and up to 4.2x on VNNI while minimizing accuracy loss. Over 30 pruning and knowledge distillation samples are also available. More details for validated models are available [here](./docs/source/validated_model_list.md).
@@ -164,10 +164,10 @@ Intel® Neural Compressor validated 420+ [examples](./examples) for quantization - Architecture + Architecture Examples - GUI - APIs + GUI + APIs Intel oneAPI AI Analytics Toolkit @@ -181,10 +181,10 @@ Intel® Neural Compressor validated 420+ [examples](./examples) for quantization - Transform - Dataset - Metric - Objective + Transform + Dataset + Metric + Objective @@ -194,20 +194,20 @@ Intel® Neural Compressor validated 420+ [examples](./examples) for quantization - Quantization - Pruning(Sparsity) - Knowledge Distillation - Mixed Precision - Orchestration + Quantization + Pruning(Sparsity) + Knowledge Distillation + Mixed Precision + Orchestration - Benchmarking - Distributed Training - Model Conversion - TensorBoard + Benchmarking + Distributed Training + Model Conversion + TensorBoard - Distillation for Quantization + Distillation for Quantization Neural Coder @@ -219,9 +219,9 @@ Intel® Neural Compressor validated 420+ [examples](./examples) for quantization - Adaptor - Strategy - Reference Example + Adaptor + Strategy + Reference Example @@ -237,13 +237,13 @@ Intel® Neural Compressor validated 420+ [examples](./examples) for quantization * [Alibaba Cloud and Intel Neural Compressor Deliver Better Productivity for PyTorch Users](https://medium.com/intel-analytics-software/alibaba-cloud-collaborates-with-intel-neural-compressor-for-better-productivity-and-performance-83cdb6500420) (Sep 2022) * [Efficient Text Classification with Intel Neural Compressor](https://medium.com/intel-analytics-software/efficient-text-classification-with-intel-neural-compressor-4853296deeac) (Sep 2022) -> View our [full publication list](./docs/source/io_doc/publication_list.md). +> View our [full publication list](./docs/source/publication_list.md). ## Additional Content -* [Release Information](./docs/source/io_doc/releases_info.md) -* [Contribution Guidelines](./docs/source/io_doc/contributions.md) -* [Legal Information](./docs/source/io_doc/legal_information.md) +* [Release Information](./docs/source/releases_info.md) +* [Contribution Guidelines](./docs/source/contributions.md) +* [Legal Information](./docs/source/legal_information.md) * [Security Policy](SECURITY.md) * [Intel® Neural Compressor Website](https://intel.github.io/neural-compressor) diff --git a/docs/Makefile b/docs/Makefile index e3e1697e92b..329d0ea574d 100644 --- a/docs/Makefile +++ b/docs/Makefile @@ -7,7 +7,7 @@ SPHINXBUILD = sphinx-build SOURCEDIR = source BUILDDIR = _build IMGDIR = source/_static/imgs -BUILDIMGDIR = _build/html/io_doc/imgs +BUILDIMGDIR = _build/html/imgs CODEIMGDIR = _build/html/_static # Put it first so that "make" without argument is like "make help". @@ -19,10 +19,10 @@ help: html: # cp README.md to docs, modify response-link - cp -f "../README.md" "./source/io_doc/README.md" - cp -f "./source/io_doc/README.md" "./source/io_doc/README.md.tmp" - sed 's/.md/.html/g; s/.\/docs\/source\/io_doc/./g; s/.\/examples/..\/..\/..\/..\/examples/g; s/.\/neural_coder/..\/..\/..\/..\/neural_coder/g; s/.\/docs\/source\/_static/..\/\/_static/g;' "./source/io_doc/README.md.tmp" > "./source/io_doc/README.md" - rm -f "./source/io_doc/README.md.tmp" + cp -f "../README.md" "./source/README.md" + cp -f "./source/README.md" "./source/README.md.tmp" + sed 's/.md/.html/g; s/.\/docs\/source\//.\//g; s/.\/neural_coder\/extensions\/screenshots/imgs/g; s/.\/docs\/source\/_static/..\/\/_static/g;' "./source/README.md.tmp" > "./source/README.md" + rm -f "./source/README.md.tmp" # make sure other png can display normal $(SPHINXBUILD) -b html "$(SOURCEDIR)" "$(BUILDDIR)/html" $(SPHINXOPTS) $(O) @@ -33,7 +33,7 @@ html: cp -f "$(CODEIMGDIR)/imgs/common/code.svg" "$(CODEIMGDIR)/images/view-page-source-icon.svg" cp -f "$(CODEIMGDIR)/imgs/common/right.svg" "$(CODEIMGDIR)/images/chevron-right-orange.svg" - cp "../neural_coder/extensions/screenshots/extmanager.png" "_build/html/_static/imgs/extmanager.png" + cp "../neural_coder/extensions/screenshots/extmanager.png" "$(BUILDIMGDIR)/extmanager.png" cp "$(IMGDIR)/INC_GUI.gif" "$(BUILDIMGDIR)/INC_GUI.gif" cp "$(IMGDIR)/release_data.png" "$(BUILDIMGDIR)/release_data.png" diff --git a/docs/source/io_doc/CODE_OF_CONDUCT.md b/docs/source/CODE_OF_CONDUCT.md similarity index 100% rename from docs/source/io_doc/CODE_OF_CONDUCT.md rename to docs/source/CODE_OF_CONDUCT.md diff --git a/docs/source/io_doc/FX.md b/docs/source/FX.md similarity index 100% rename from docs/source/io_doc/FX.md rename to docs/source/FX.md diff --git a/docs/source/io_doc/NAS.md b/docs/source/NAS.md similarity index 99% rename from docs/source/io_doc/NAS.md rename to docs/source/NAS.md index a2eb0eb456b..98eac4d8217 100644 --- a/docs/source/io_doc/NAS.md +++ b/docs/source/NAS.md @@ -136,7 +136,7 @@ Dynamic Neural Architecture Search (DyNAS) is a super-network-based NAS approach
The flow of the DyNAS approach is shown in the following figure. In the first phase of the search, a small population of sub-networks are randomly sampled from the super-network and evaluated (validation measurement) to provide the initial training set for the inner predictor loop. After the predictors are trained, a multi-objective evolutionary search is performed in the predictor objective space. After this extensive search is performed, the best performing sub-network configurations are selected to be the next iteration's validation population. The cycle continues until the search concludes when the user defined evaluation count is met.
-![DyNAS Workflow](./imgs/dynas.png) +![DyNAS Workflow](./_static/imgs/dynas.png)
This class is also registered to the Intel® Neural Compressor as a built-in NAS method through a decorator `nas_registry`, its interface is shown below. diff --git a/docs/source/io_doc/PTQ.md b/docs/source/PTQ.md similarity index 100% rename from docs/source/io_doc/PTQ.md rename to docs/source/PTQ.md diff --git a/docs/source/io_doc/QAT.md b/docs/source/QAT.md similarity index 98% rename from docs/source/io_doc/QAT.md rename to docs/source/QAT.md index 7bad1c0fcd0..e899f2157c1 100644 --- a/docs/source/io_doc/QAT.md +++ b/docs/source/QAT.md @@ -4,7 +4,7 @@ Quantization-aware training (QAT) simulates low-precision inference-time computation in the forward pass of the training process. With QAT, all weights and activations are "fake quantized" during both the forward and backward passes of training: that is, float values are rounded to mimic int8 values, but all computations are still done with floating point numbers. Thus, all the weight adjustments during training are made while "aware" of the fact that the model will ultimately be quantized; after quantizing, therefore, this method will usually yield higher accuracy than either dynamic quantization or post-training static quantization. -fake quantize +fake quantize ## Usage diff --git a/docs/source/io_doc/Quantization.md b/docs/source/Quantization.md similarity index 95% rename from docs/source/io_doc/Quantization.md rename to docs/source/Quantization.md index dd3dbce3c7d..c57c60f8054 100644 --- a/docs/source/io_doc/Quantization.md +++ b/docs/source/Quantization.md @@ -62,15 +62,15 @@ Quantization methods include the following three types: ### [Post-Training Static Quantization](./PTQ.md) performs quantization on already trained models, it requires an additional pass over the dataset to work, only activations do calibration. -PTQ +PTQ
### [Post-Training Dynamic Quantization](./dynamic_quantization.md) simply multiplies input values by a scaling factor, then rounds the result to the nearest, it determines the scale factor for activations dynamically based on the data range observed at runtime. Weights are quantized ahead of time but the activations are dynamically quantized during inference. -Dynamic Quantization +Dynamic Quantization
### [Quantization-aware Training (QAT)](./QAT.md) quantizes models during training and typically provides higher accuracy comparing with post-training quantization, but QAT may require additional hyper-parameter tuning and it may take more time to deployment. -QAT +QAT ## Examples of Quantization diff --git a/docs/source/io_doc/README.md b/docs/source/README.md similarity index 92% rename from docs/source/io_doc/README.md rename to docs/source/README.md index 9e86cd28029..c912168f5a7 100644 --- a/docs/source/io_doc/README.md +++ b/docs/source/README.md @@ -64,11 +64,11 @@ dataset = quantizer.dataset('dummy', shape=(1, 224, 224, 3)) quantizer.calib_dataloader = common.DataLoader(dataset) quantizer.fit() ``` -### Quantization with [JupyterLab Extension](../../../../neural_coder/extensions/neural_compressor_ext_lab/README.html) +### Quantization with [JupyterLab Extension](./neural_coder/extensions/neural_compressor_ext_lab/README.html) Search for ```jupyter-lab-neural-compressor``` in the Extension Manager in JupyterLab and install with one click: - - Extension + + Extension ### Quantization with [GUI](./bench.html) @@ -80,8 +80,8 @@ wget https://github.com/onnx/models/raw/main/vision/classification/resnet/model/ # Start GUI inc_bench ``` - - Architecture + + Architecture ## System Requirements @@ -146,11 +146,11 @@ inc_bench > Set the environment variable ``TF_ENABLE_ONEDNN_OPTS=1`` to enable oneDNN optimizations if you are using TensorFlow v2.6 to v2.8. oneDNN is the default for TensorFlow v2.9. ### Validated Models -Intel® Neural Compressor validated 420+ [examples](../../../../examples) for quantization with a performance speedup geomean of 2.2x and up to 4.2x on VNNI while minimizing accuracy loss. Over 30 pruning and knowledge distillation samples are also available. More details for validated models are available [here](./validated_model_list.html). +Intel® Neural Compressor validated 420+ [examples](./examples) for quantization with a performance speedup geomean of 2.2x and up to 4.2x on VNNI while minimizing accuracy loss. Over 30 pruning and knowledge distillation samples are also available. More details for validated models are available [here](./validated_model_list.html). @@ -165,7 +165,7 @@ Intel® Neural Compressor validated 420+ [examples](../../../../examples) for qu Architecture - Examples + Examples GUI APIs diff --git a/docs/source/_static/index.html b/docs/source/_static/index.html index 74ee81a72ad..5f62e3d9bef 100644 --- a/docs/source/_static/index.html +++ b/docs/source/_static/index.html @@ -1 +1 @@ - \ No newline at end of file + \ No newline at end of file diff --git a/docs/source/_templates/layout.html b/docs/source/_templates/layout.html index dab50e1942c..8f3719c2401 100644 --- a/docs/source/_templates/layout.html +++ b/docs/source/_templates/layout.html @@ -26,11 +26,11 @@ {% endblock %} {% block menu %} -
+ {{ super() }} {% endblock %} diff --git a/docs/source/io_doc/adaptor.md b/docs/source/adaptor.md similarity index 100% rename from docs/source/io_doc/adaptor.md rename to docs/source/adaptor.md diff --git a/docs/source/io_doc/api-documentation/adaptor.rst b/docs/source/api-documentation/adaptor.rst similarity index 100% rename from docs/source/io_doc/api-documentation/adaptor.rst rename to docs/source/api-documentation/adaptor.rst diff --git a/docs/source/io_doc/api-documentation/api-introduction.md b/docs/source/api-documentation/api-introduction.md similarity index 100% rename from docs/source/io_doc/api-documentation/api-introduction.md rename to docs/source/api-documentation/api-introduction.md diff --git a/docs/source/io_doc/api-documentation/apis.rst b/docs/source/api-documentation/apis.rst similarity index 100% rename from docs/source/io_doc/api-documentation/apis.rst rename to docs/source/api-documentation/apis.rst diff --git a/docs/source/io_doc/api-documentation/common.rst b/docs/source/api-documentation/common.rst similarity index 100% rename from docs/source/io_doc/api-documentation/common.rst rename to docs/source/api-documentation/common.rst diff --git a/docs/source/io_doc/api-documentation/common/benchmark.rst b/docs/source/api-documentation/common/benchmark.rst similarity index 100% rename from docs/source/io_doc/api-documentation/common/benchmark.rst rename to docs/source/api-documentation/common/benchmark.rst diff --git a/docs/source/io_doc/api-documentation/common/bleu.rst b/docs/source/api-documentation/common/bleu.rst similarity index 100% rename from docs/source/io_doc/api-documentation/common/bleu.rst rename to docs/source/api-documentation/common/bleu.rst diff --git a/docs/source/io_doc/api-documentation/common/criterion.rst b/docs/source/api-documentation/common/criterion.rst similarity index 100% rename from docs/source/io_doc/api-documentation/common/criterion.rst rename to docs/source/api-documentation/common/criterion.rst diff --git a/docs/source/io_doc/api-documentation/common/data.rst b/docs/source/api-documentation/common/data.rst similarity index 100% rename from docs/source/io_doc/api-documentation/common/data.rst rename to docs/source/api-documentation/common/data.rst diff --git a/docs/source/io_doc/api-documentation/common/data/dataloader.rst b/docs/source/api-documentation/common/data/dataloader.rst similarity index 100% rename from docs/source/io_doc/api-documentation/common/data/dataloader.rst rename to docs/source/api-documentation/common/data/dataloader.rst diff --git a/docs/source/io_doc/api-documentation/common/data/datasets.rst b/docs/source/api-documentation/common/data/datasets.rst similarity index 100% rename from docs/source/io_doc/api-documentation/common/data/datasets.rst rename to docs/source/api-documentation/common/data/datasets.rst diff --git a/docs/source/io_doc/api-documentation/common/data/transforms.rst b/docs/source/api-documentation/common/data/transforms.rst similarity index 100% rename from docs/source/io_doc/api-documentation/common/data/transforms.rst rename to docs/source/api-documentation/common/data/transforms.rst diff --git a/docs/source/io_doc/api-documentation/common/metric.rst b/docs/source/api-documentation/common/metric.rst similarity index 100% rename from docs/source/io_doc/api-documentation/common/metric.rst rename to docs/source/api-documentation/common/metric.rst diff --git a/docs/source/io_doc/api-documentation/common/model.rst b/docs/source/api-documentation/common/model.rst similarity index 100% rename from docs/source/io_doc/api-documentation/common/model.rst rename to docs/source/api-documentation/common/model.rst diff --git a/docs/source/io_doc/api-documentation/common/optimizer.rst b/docs/source/api-documentation/common/optimizer.rst similarity index 100% rename from docs/source/io_doc/api-documentation/common/optimizer.rst rename to docs/source/api-documentation/common/optimizer.rst diff --git a/docs/source/io_doc/api-documentation/component.rst b/docs/source/api-documentation/component.rst similarity index 100% rename from docs/source/io_doc/api-documentation/component.rst rename to docs/source/api-documentation/component.rst diff --git a/docs/source/io_doc/api-documentation/component/Distillation.rst b/docs/source/api-documentation/component/Distillation.rst similarity index 100% rename from docs/source/io_doc/api-documentation/component/Distillation.rst rename to docs/source/api-documentation/component/Distillation.rst diff --git a/docs/source/io_doc/api-documentation/component/MixedPrecision.rst b/docs/source/api-documentation/component/MixedPrecision.rst similarity index 100% rename from docs/source/io_doc/api-documentation/component/MixedPrecision.rst rename to docs/source/api-documentation/component/MixedPrecision.rst diff --git a/docs/source/io_doc/api-documentation/component/ModelConversion.rst b/docs/source/api-documentation/component/ModelConversion.rst similarity index 100% rename from docs/source/io_doc/api-documentation/component/ModelConversion.rst rename to docs/source/api-documentation/component/ModelConversion.rst diff --git a/docs/source/io_doc/api-documentation/component/Nas.rst b/docs/source/api-documentation/component/Nas.rst similarity index 100% rename from docs/source/io_doc/api-documentation/component/Nas.rst rename to docs/source/api-documentation/component/Nas.rst diff --git a/docs/source/io_doc/api-documentation/component/Pruning.rst b/docs/source/api-documentation/component/Pruning.rst similarity index 100% rename from docs/source/io_doc/api-documentation/component/Pruning.rst rename to docs/source/api-documentation/component/Pruning.rst diff --git a/docs/source/io_doc/api-documentation/component/Quantization.rst b/docs/source/api-documentation/component/Quantization.rst similarity index 100% rename from docs/source/io_doc/api-documentation/component/Quantization.rst rename to docs/source/api-documentation/component/Quantization.rst diff --git a/docs/source/io_doc/api-documentation/component/Scheduler.rst b/docs/source/api-documentation/component/Scheduler.rst similarity index 89% rename from docs/source/io_doc/api-documentation/component/Scheduler.rst rename to docs/source/api-documentation/component/Scheduler.rst index f74516a816e..44bc31212ce 100644 --- a/docs/source/io_doc/api-documentation/component/Scheduler.rst +++ b/docs/source/api-documentation/component/Scheduler.rst @@ -1,4 +1,4 @@ -scheduler +Scheduler ============== .. autoapisummary:: diff --git a/docs/source/io_doc/api-documentation/pythonic.rst b/docs/source/api-documentation/pythonic.rst similarity index 100% rename from docs/source/io_doc/api-documentation/pythonic.rst rename to docs/source/api-documentation/pythonic.rst diff --git a/docs/source/io_doc/api-documentation/strategy.rst b/docs/source/api-documentation/strategy.rst similarity index 100% rename from docs/source/io_doc/api-documentation/strategy.rst rename to docs/source/api-documentation/strategy.rst diff --git a/docs/source/io_doc/api-introduction.md b/docs/source/api-introduction.md similarity index 100% rename from docs/source/io_doc/api-introduction.md rename to docs/source/api-introduction.md diff --git a/docs/source/io_doc/backend_quant.md b/docs/source/backend_quant.md similarity index 100% rename from docs/source/io_doc/backend_quant.md rename to docs/source/backend_quant.md diff --git a/docs/source/io_doc/bench.md b/docs/source/bench.md similarity index 79% rename from docs/source/io_doc/bench.md rename to docs/source/bench.md index 8938b916357..e0b9802ee7a 100644 --- a/docs/source/io_doc/bench.md +++ b/docs/source/bench.md @@ -91,113 +91,113 @@ or specify different port that is already opened, for example 8080: ## Home screen This view shows introduction to Intel® Neural Compressor Bench and a button for creating new project. After clicking this button, pop-up with project wizard will be shown. -![Home](imgs/bench/home.png "Home") +![Home](_static/imgs/bench/home.png "Home") # Create new project To create a new project, in first step you need to choose its name. -![Project1](imgs/bench/project1.png "Project1") +![Project1](_static/imgs/bench/project1.png "Project1") In second step there are 2 possible options to choose from: * *predefined model* - you choose model from predefined examples list, you don't need to set any additional parameters, * *custom model* - in this scenario you can set more parameters and customize your model. -![Project2](imgs/bench/project2.png "Project2") +![Project2](_static/imgs/bench/project2.png "Project2") ## Predefined model First you need to choose domain for the model (image recognition or object detection). For each domain there are few available models to choose from. When you click *Finish* the chosen model will be downloaded. -![Project3-predefined](imgs/bench/project3-predefined.png "Project3-predefined") +![Project3-predefined](_static/imgs/bench/project3-predefined.png "Project3-predefined") ## Custom model First you have to choose the model path. When it is chosen, in most cases all other fields will be completed automatically. You can edit its input and output nodes, see the model graph (if it is available for this model) and set shape for synthetic dataset. If model domain was not detected, you need to choose it from the list. Model domain is used to set some default parameters for the model. -![Project3-custom](imgs/bench/project3-custom.png "Project3-custom") +![Project3-custom](_static/imgs/bench/project3-custom.png "Project3-custom") ## Display model graph -For several model types there will be a button available ![Show graph](imgs/bench/show_graph_button.png "Show graph") in the project wizard. It is also possible to see the graph in Diagnosis tab. The graph by default is collapsed, but when you click on plus icon, sections will be unfolded. +For several model types there will be a button available ![Show graph](_static/imgs/bench/show_graph_button.png "Show graph") in the project wizard. It is also possible to see the graph in Diagnosis tab. The graph by default is collapsed, but when you click on plus icon, sections will be unfolded. -![Bert model graph](imgs/bench/graph_bert.png "Bert model graph"). +![Bert model graph](_static/imgs/bench/graph_bert.png "Bert model graph"). # Project list On the left hand side there is a panel with list of created projects. When you click on the project name, you can see its details. "Create new project" button navigates to new project wizard pop-up described in previous section. -![Menu](imgs/bench/menu.png "Menu") +![Menu](_static/imgs/bench/menu.png "Menu") ## Remove project If you want to remove project, you have to click the trash icon next to project name (it is visible when the cursor is on the project name). -![Remove1](imgs/bench/remove1.png "Remove1") +![Remove1](_static/imgs/bench/remove1.png "Remove1") Then you will be prompted to confirm your choice by typing the project name. Project removal is not reversible. -![Remove2](imgs/bench/remove2.png "Remove2") +![Remove2](_static/imgs/bench/remove2.png "Remove2") # Develop the project ## Optimization tab ### Optimization table In Optimizations tab you can see list of optimizations in the project. Currently UI supports three optimization precisions and two types of optimization. -![Optimizations-table](imgs/bench/optimizations-table.png "Optimizations-table") +![Optimizations-table](_static/imgs/bench/optimizations-table.png "Optimizations-table") ### Optimization wizard To add new optimization, click "Add new optimization" button at the bottom of the table and follow the steps. -![Optimizations-wizard](imgs/bench/optimizations-wizard.png "Optimizations-wizard") +![Optimizations-wizard](_static/imgs/bench/optimizations-wizard.png "Optimizations-wizard") ### Editing optimization entries There is a possibility to modify some optimization parameters even after exit from Wizard. If optimization has not been run yet, the pencil icon on the right hand side should be in light blue color. That indicates that it can be modified. After click on that pencil icon you can select different precision or dataset. For Quantization you can also modify Tuning details before optimizing model. -![Optimization-tuning-details](imgs/bench/optimization-tuning-details.png "Optimization-tuning-details") +![Optimization-tuning-details](_static/imgs/bench/optimization-tuning-details.png "Optimization-tuning-details") ### Optimization details To perform optimization click "Run" button. Once process is finished you can click on row with specific optimization to display details about optimization parameters and optimized model. When you click on blue arrow icon in model path line, you can download optimized model. -![Optimization-details](imgs/bench/optimization-details.png "Optimization-details") +![Optimization-details](_static/imgs/bench/optimization-details.png "Optimization-details") ## Benchmark tab ### Benchmark table For each optimization and input model you can add benchmark. Benchmark have 2 modes: accuracy and performance. In benchmark tab you can see all your benchmarks. When you check checkboxes in the last column you can choose benchmark you want to compare in the chart (visible after clicking "Compare selected"). -![Benchmarks-table](imgs/bench/benchmarks-table.png "Benchmarks-table") +![Benchmarks-table](_static/imgs/bench/benchmarks-table.png "Benchmarks-table") ### Benchmark wizard To add new benchmark, click "Add new benchmark" button at the bottom of the table and follow the steps. -![Benchmarks-wizard](imgs/bench/benchmarks-wizard.png "Benchmarks-wizard") +![Benchmarks-wizard](_static/imgs/bench/benchmarks-wizard.png "Benchmarks-wizard") ### Editing benchmark entries As for optimizations you can also modify benchmark parameters. You can modify benchmark mode, dataset and benchmark parameters like batch size, number of instances and number of cores per instance. -![Benchmark-edit-wizard](imgs/bench/benchmark-edit-wizard.png "Benchmark-edit-wizard") +![Benchmark-edit-wizard](_static/imgs/bench/benchmark-edit-wizard.png "Benchmark-edit-wizard") ### Benchmark details When the benchmark is added, you can click "Run" button to execute it. Results will be filled in the table and in details view visible after clicking row in the table. You can also see config and output logs when clicking links highlighted in blue. -![Benchmark-details](imgs/bench/benchmark-details.png "Benchmark-details") +![Benchmark-details](_static/imgs/bench/benchmark-details.png "Benchmark-details") ## Profiling tab ### Profiling table It is also possible to do profiling of all Tensorflow frozen models in project. -![Profiling-table](imgs/bench/profiling-table.png "Profiling-table") +![Profiling-table](_static/imgs/bench/profiling-table.png "Profiling-table") ### Profiling wizard To profile model, click "Add new profiling" button at the bottom of the table and follow the steps. -![Profiling-wizard](imgs/bench/profiling-wizard.png "Profiling-wizard") +![Profiling-wizard](_static/imgs/bench/profiling-wizard.png "Profiling-wizard") ### Editing profiling entries In Profiling tab you can edit dataset and number or threads. -![Profiling-edit-wizard](imgs/bench/profiling-edit-wizard.png "Profiling-edit-wizard") +![Profiling-edit-wizard](_static/imgs/bench/profiling-edit-wizard.png "Profiling-edit-wizard") ### Profiling details Once profiling entry is added, you can click "Run" button to execute it. After completing the process, the results will appear in the form of a bar chart and a table with full profiling data. The table is also used to control which operations are included in the chart. Check the box next to the selected row and click "Update chart" button to include it in the bar chart. Click "Download .csv file" button to get profiling data in .csv file. -![Profiling-details](imgs/bench/profiling-details.png "Profiling-details") +![Profiling-details](_static/imgs/bench/profiling-details.png "Profiling-details") # Diagnosis tab @@ -205,43 +205,43 @@ Diagnosis tab offers convenient debug information for optimizations with easy wa To get OP list you need to execute quantization optimization and select optimized model on left hand side. In OP table you can see list of OPs with MSE and min/max activation values. Selecting one of OP in table highlights its position in graph. Configuration for currently selected OP can be set in section under OP table. -![Diagnosis-tab](imgs/bench/diagnosis-tab.png "Diagnosis-tab") +![Diagnosis-tab](_static/imgs/bench/diagnosis-tab.png "Diagnosis-tab") You can set model wise parameters that apply to whole model by clicking button with "Model wise". When you set specific configuration you can view summary and generate new optimization config. -![Diagnosis-actions](imgs/bench/diagnosis-actions.png "Diagnosis-actions") +![Diagnosis-actions](_static/imgs/bench/diagnosis-actions.png "Diagnosis-actions") Model wise configuration provides separate settings for weights and activations. -![Diagnosis-model-wise-wizard](imgs/bench/diagnosis-model-wise-wizard.png "Diagnosis-model-wise-wizard") +![Diagnosis-model-wise-wizard](_static/imgs/bench/diagnosis-model-wise-wizard.png "Diagnosis-model-wise-wizard") ## Dataset tab ### Dataset list Dataset tab presents list of datasets assigned to a project. In most cases the "dummy" dataset consisting of synthetic data should be automatically added while creating a project. -![Datasets-table](imgs/bench/datasets-table.png "Datasets-table") +![Datasets-table](_static/imgs/bench/datasets-table.png "Datasets-table") ### Dataset wizard New dataset can be defined by clicking "Add new profiling" button at the bottom of the table and follow the steps. -![Datasets-wizard](imgs/bench/datasets-wizard.png "Datasets-wizard") +![Datasets-wizard](_static/imgs/bench/datasets-wizard.png "Datasets-wizard") ### Dataset details Dataset details can be inspected by clicking specific row. -![Dataset-details](imgs/bench/dataset-details.png "Dataset-details") +![Dataset-details](_static/imgs/bench/dataset-details.png "Dataset-details") ### Custom dataset When adding the dataset, you can choose *custom* in dataloader and metric field. In that case a template file will be created. The path to the template file will be available in dataset details. You should edit this file to add your custom configuration before using this dataset in optimizations or benchmarks. Small yellow warning will remind about it. -![Custom dataset](imgs/bench/custom-dataset.png "Custom dataset") +![Custom dataset](_static/imgs/bench/custom-dataset.png "Custom dataset") ## Project information Last tab is called "Project info". You can find here details about the project, when it was created and modified, what is the framework and some details about input model. It is also possible to add some notes about the project. -![Project info](imgs/bench/project-info.png "Project info") +![Project info](_static/imgs/bench/project-info.png "Project info") ## System information -One can see system information by clicking ![System info](imgs/bench/system_info.png "System info") button. The result is details dialog: +One can see system information by clicking ![System info](_static/imgs/bench/system_info.png "System info") button. The result is details dialog: -![System info table](imgs/bench/system_info_table.png "System info table") +![System info table](_static/imgs/bench/system_info_table.png "System info table") ## Security diff --git a/docs/source/io_doc/benchmark.md b/docs/source/benchmark.md similarity index 100% rename from docs/source/io_doc/benchmark.md rename to docs/source/benchmark.md diff --git a/docs/source/io_doc/contributions.md b/docs/source/contributions.md similarity index 100% rename from docs/source/io_doc/contributions.md rename to docs/source/contributions.md diff --git a/docs/source/io_doc/dataloader.md b/docs/source/dataloader.md similarity index 100% rename from docs/source/io_doc/dataloader.md rename to docs/source/dataloader.md diff --git a/docs/source/io_doc/dataset.md b/docs/source/dataset.md similarity index 100% rename from docs/source/io_doc/dataset.md rename to docs/source/dataset.md diff --git a/docs/source/design.md b/docs/source/design.md new file mode 100644 index 00000000000..e75e25df785 --- /dev/null +++ b/docs/source/design.md @@ -0,0 +1,15 @@ +Design +===== +Intel® Neural Compressor features an architecture and workflow that aids in increasing performance and faster deployments across infrastructures. + +## Architecture + + + Architecture + + +## Workflow + + + Workflow + diff --git a/docs/source/io_doc/distillation.md b/docs/source/distillation.md similarity index 97% rename from docs/source/io_doc/distillation.md rename to docs/source/distillation.md index 1c4f248fcd4..49cec901185 100644 --- a/docs/source/io_doc/distillation.md +++ b/docs/source/distillation.md @@ -18,7 +18,7 @@ Distillation Distillation is one of popular approaches of network compression, which transfers knowledge from a large model to a smaller one without loss of validity. As smaller models are less expensive to evaluate, they can be deployed on less powerful hardware (such as a mobile device). Graph shown below is the workflow of the distillation, the teacher model will take the same input that feed into the student model to produce the output that contains knowledge of the teacher model to instruct the student model.
-Architecture +Architecture Intel® Neural Compressor supports Knowledge Distillation and Intermediate Layer Knowledge Distillation algorithms. @@ -43,7 +43,7 @@ Self-distillation ia a one-stage training method where the teacher model and stu The additional classifiers in self-distillation allow the neural network to work in a dynamic manner, which leads to a much higher acceleration.
-Architecture +Architecture Architecture from paper [Self-Distillation: Towards Efficient and Compact Neural Networks](https://ieeexplore.ieee.org/document/9381661) diff --git a/docs/source/io_doc/distillation_quantization.md b/docs/source/distillation_quantization.md similarity index 100% rename from docs/source/io_doc/distillation_quantization.md rename to docs/source/distillation_quantization.md diff --git a/docs/source/io_doc/distributed.md b/docs/source/distributed.md similarity index 100% rename from docs/source/io_doc/distributed.md rename to docs/source/distributed.md diff --git a/docs/source/io_doc/doclist.rst b/docs/source/doclist.rst similarity index 100% rename from docs/source/io_doc/doclist.rst rename to docs/source/doclist.rst diff --git a/docs/source/io_doc/dynamic_quantization.md b/docs/source/dynamic_quantization.md similarity index 100% rename from docs/source/io_doc/dynamic_quantization.md rename to docs/source/dynamic_quantization.md diff --git a/docs/source/io_doc/examples_readme.md b/docs/source/examples_readme.md similarity index 100% rename from docs/source/io_doc/examples_readme.md rename to docs/source/examples_readme.md diff --git a/docs/source/io_doc/faq.md b/docs/source/faq.md similarity index 100% rename from docs/source/io_doc/faq.md rename to docs/source/faq.md diff --git a/docs/source/io_doc/getting_started.md b/docs/source/getting_started.md similarity index 100% rename from docs/source/io_doc/getting_started.md rename to docs/source/getting_started.md diff --git a/docs/source/io_doc/graph_optimization.md b/docs/source/graph_optimization.md similarity index 100% rename from docs/source/io_doc/graph_optimization.md rename to docs/source/graph_optimization.md diff --git a/docs/source/io_doc/incompatible_changes.md b/docs/source/incompatible_changes.md similarity index 100% rename from docs/source/io_doc/incompatible_changes.md rename to docs/source/incompatible_changes.md diff --git a/docs/source/index.rst b/docs/source/index.rst index c915ba2f3fa..2ab2e1d8bbb 100644 --- a/docs/source/index.rst +++ b/docs/source/index.rst @@ -10,15 +10,15 @@ Sections .. toctree:: :maxdepth: 1 - io_doc/README.md - io_doc/tutorial.md - io_doc/examples_readme.md - io_doc/api-documentation/apis.rst - io_doc/doclist.rst - io_doc/releases_info.md - io_doc/contributions.md - io_doc/legal_information.md - io_doc/security_policy.md + README.md + tutorial.md + examples_readme.md + api-documentation/apis.rst + doclist.rst + releases_info.md + contributions.md + legal_information.md + security_policy.md Intel® Neural Compressor repository diff --git a/docs/source/io_doc/infrastructure.md b/docs/source/infrastructure.md similarity index 98% rename from docs/source/io_doc/infrastructure.md rename to docs/source/infrastructure.md index f09ec322d6a..c2c2aae62e4 100644 --- a/docs/source/io_doc/infrastructure.md +++ b/docs/source/infrastructure.md @@ -11,8 +11,8 @@ Neural Coder automatically inserts quantization code snippets on a PyTorch model ## Architecture - - Architecture + + Architecture Intel® Neural Compressor has unified interfaces which dispatch tasks to different frameworks via adaptor layer. The adaptor layer is the bridge between the tuning strategy and vanilla framework quantization APIs. Users can select tuning strategies and the strategy module contains model configs and tuning configs. Model configs define the quantization approach, if it's post-training static quantization, users need to set more parameters like calibration and so on. There are several tuning strategies for users to choose from while the basic strategy is set as default. diff --git a/docs/source/io_doc/installation_guide.md b/docs/source/installation_guide.md similarity index 100% rename from docs/source/io_doc/installation_guide.md rename to docs/source/installation_guide.md diff --git a/docs/source/io_doc/design.md b/docs/source/io_doc/design.md deleted file mode 100644 index bee2fa124b8..00000000000 --- a/docs/source/io_doc/design.md +++ /dev/null @@ -1,15 +0,0 @@ -Design -===== -Intel® Neural Compressor features an architecture and workflow that aids in increasing performance and faster deployments across infrastructures. - -## Architecture - - - Architecture - - -## Workflow - - - Workflow - diff --git a/docs/source/io_doc/legal_information.md b/docs/source/legal_information.md similarity index 100% rename from docs/source/io_doc/legal_information.md rename to docs/source/legal_information.md diff --git a/docs/source/io_doc/metric.md b/docs/source/metric.md similarity index 100% rename from docs/source/io_doc/metric.md rename to docs/source/metric.md diff --git a/docs/source/io_doc/mixed_precision.md b/docs/source/mixed_precision.md similarity index 95% rename from docs/source/io_doc/mixed_precision.md rename to docs/source/mixed_precision.md index 04b155bb8f1..4a0ff3830fe 100644 --- a/docs/source/io_doc/mixed_precision.md +++ b/docs/source/mixed_precision.md @@ -12,9 +12,9 @@ The recent growth of Deep Learning has driven the development of more complex mo The recently launched 3rd Gen Intel® Xeon® Scalable processor (codenamed Cooper Lake), featuring Intel® Deep Learning Boost, is the first general-purpose x86 CPU to support the bfloat16 format. Specifically, three new bfloat16 instructions are added as a part of the AVX512_BF16 extension within Intel Deep Learning Boost: VCVTNE2PS2BF16, VCVTNEPS2BF16, and VDPBF16PS. The first two instructions allow converting to and from bfloat16 data type, while the last one performs a dot product of bfloat16 pairs. Further details can be found in the [hardware numerics document](https://software.intel.com/content/www/us/en/develop/download/bfloat16-hardware-numerics-definition.html) published by Intel. - +
- Architecture + Architecture
diff --git a/docs/source/io_doc/model.md b/docs/source/model.md similarity index 95% rename from docs/source/io_doc/model.md rename to docs/source/model.md index 3bb4f0e9a52..b0ca55236f0 100644 --- a/docs/source/io_doc/model.md +++ b/docs/source/model.md @@ -11,9 +11,9 @@ Model ## Introduction The Neural Compressor Model feature is used to encapsulate the behavior of model building and saving. By simply providing information such as different model formats and framework_specific_info, Neural Compressor performs optimizations and quantization on this model object and returns a Neural Compressor Model object for further model persistence or benchmarking. A Neural Compressor Model helps users to maintain necessary model information which is required during optimization and quantization such as the input/output names, workspace path, and other model format knowledge. This helps unify the features gap brought by different model formats and frameworks. - +
- Architecture + Architecture
diff --git a/docs/source/io_doc/model_conversion.md b/docs/source/model_conversion.md similarity index 100% rename from docs/source/io_doc/model_conversion.md rename to docs/source/model_conversion.md diff --git a/docs/source/io_doc/objective.md b/docs/source/objective.md similarity index 100% rename from docs/source/io_doc/objective.md rename to docs/source/objective.md diff --git a/docs/source/io_doc/orchestration.md b/docs/source/orchestration.md similarity index 100% rename from docs/source/io_doc/orchestration.md rename to docs/source/orchestration.md diff --git a/docs/source/io_doc/platform_configuration.md b/docs/source/platform_configuration.md similarity index 100% rename from docs/source/io_doc/platform_configuration.md rename to docs/source/platform_configuration.md diff --git a/docs/source/io_doc/pruning.md b/docs/source/pruning.md similarity index 94% rename from docs/source/io_doc/pruning.md rename to docs/source/pruning.md index 95f06d81f01..e886bb0ffae 100644 --- a/docs/source/io_doc/pruning.md +++ b/docs/source/pruning.md @@ -22,8 +22,8 @@ Pruning ### Neural Network Pruning Neural network pruning (briefly known as pruning or sparsity) is one of the most promising model compression techniques. It removes the least important parameters in the network and achieves compact architectures with minimal accuracy drop and maximal inference acceleration. As current state-of-the-art models have increasingly more parameters, pruning plays a crucial role in enabling them to run on devices whose memory footprints and computing resources are limited. - - pruning intro + + pruning intro @@ -31,8 +31,8 @@ Neural network pruning (briefly known as pruning or sparsity) is one of the most Pruning patterns defines the rules of pruned weights' arrangements in space. - - Sparsity Pattern + + Sparsity Pattern @@ -56,8 +56,8 @@ For a typical Convolution, the weight dimension is ```OC x IC x KH x KW```, wher Here is a figure showing a matrix with ```IC``` = 32 and ```OC``` = 16 dimension, and a block-wise sparsity pattern with block size 4 on ```OC``` dimension. - - block sparsity Pattern + + block sparsity Pattern ### Pruning Criteria diff --git a/docs/source/io_doc/publication_list.md b/docs/source/publication_list.md similarity index 100% rename from docs/source/io_doc/publication_list.md rename to docs/source/publication_list.md diff --git a/docs/source/io_doc/quantization_mixed_precision.md b/docs/source/quantization_mixed_precision.md similarity index 88% rename from docs/source/io_doc/quantization_mixed_precision.md rename to docs/source/quantization_mixed_precision.md index e4b800616b8..9352a81f8cf 100644 --- a/docs/source/io_doc/quantization_mixed_precision.md +++ b/docs/source/quantization_mixed_precision.md @@ -16,9 +16,9 @@ Intel has worked with the TensorFlow development team to enhance TensorFlow to i - BF16 conversion during quantization in TensorFlow - +
- Architecture + Architecture
@@ -43,9 +43,9 @@ Intel has also worked with the PyTorch development team to enhance PyTorch to in - BF16 conversion during quantization in PyTorch - +
- Architecture + Architecture
diff --git a/docs/source/io_doc/reference_examples.md b/docs/source/reference_examples.md similarity index 100% rename from docs/source/io_doc/reference_examples.md rename to docs/source/reference_examples.md diff --git a/docs/source/io_doc/releases_info.md b/docs/source/releases_info.md similarity index 100% rename from docs/source/io_doc/releases_info.md rename to docs/source/releases_info.md diff --git a/docs/source/io_doc/sigopt_strategy.md b/docs/source/sigopt_strategy.md similarity index 100% rename from docs/source/io_doc/sigopt_strategy.md rename to docs/source/sigopt_strategy.md diff --git a/docs/source/io_doc/tensorboard.md b/docs/source/tensorboard.md similarity index 96% rename from docs/source/io_doc/tensorboard.md rename to docs/source/tensorboard.md index ad8965032fc..716e094eb35 100644 --- a/docs/source/io_doc/tensorboard.md +++ b/docs/source/tensorboard.md @@ -185,13 +185,13 @@ See the [tensorflow.py](https://github.com/intel/neural-compressor/tree/master/n * From the **GRAPHS** tab, select "baseline/." in the "Run" box and find the first 'Conv2d' op after 'input' op. The op name is "v0/cg/conv0/Relu": -![TensorBoard Baseline](imgs/tensorboard_baseline_v0_cg_conv0.png "TensorBoard Baseline") +![TensorBoard Baseline](./_static/imgs/tensorboard_baseline_v0_cg_conv0.png "TensorBoard Baseline") * From the **GRAPHS** tab, select "tune_1/." in the "Run" box and find the first 'Conv2d' op after 'input' op. The tensor name is 'v0/cg/conv0/conv2d/Conv2D_eightbit_requantize': -![TensorBoard Tuning](imgs/tensorboard_tune_1_v0_cg_conv0.png "TensorBoard Tuning") +![TensorBoard Tuning](./_static/imgs/tensorboard_tune_1_v0_cg_conv0.png "TensorBoard Tuning") * Switch to the **HISTOGRAMS** tab. Click the 'v0/cg/conv0' op name in the search box. TensorBoard groups the tensors with the same op name together so you can compare the tensor of baseline 'v0/cg/conv0/Relu' with the tensor of tune_1 'v0/cg/conv0/conv2d/Conv2D_eightbit_requantize_int8.output'. Note that the tensor name can be changed after quantization, so group the tensor by op name and compare. From the chart below, we can see that the histogram of the first conv2d output tensor are different. This is due to a known TensorFlow issue. After filtering the 'v0/cg/conv0/conv2d/Conv2D' op by adding "op_wise" in the yaml file, the issue disappears. -![TensorBoard Histogram](imgs/tensorboard_v0_cg_conv0_histogram.png "TensorBoard Histogram") +![TensorBoard Histogram](./_static/imgs/tensorboard_v0_cg_conv0_histogram.png "TensorBoard Histogram") diff --git a/docs/source/io_doc/transform.md b/docs/source/transform.md similarity index 100% rename from docs/source/io_doc/transform.md rename to docs/source/transform.md diff --git a/docs/source/io_doc/tuning_strategies.md b/docs/source/tuning_strategies.md similarity index 99% rename from docs/source/io_doc/tuning_strategies.md rename to docs/source/tuning_strategies.md index 55c5fcae411..6e11941559a 100644 --- a/docs/source/io_doc/tuning_strategies.md +++ b/docs/source/tuning_strategies.md @@ -17,7 +17,7 @@ Each strategy generates the next quantization configuration according to its logic and the last quantization result. The function of strategies is shown below: -![Tuning Strategy](imgs/strategy.png "Strategy Framework") +![Tuning Strategy](./_static/imgs/strategy.png "Strategy Framework") Strategies begin with an adaptor layer (Framework Adaptor) where the user passes a framework-specific model to initialize an instance of the diff --git a/docs/source/io_doc/validated_model_list.md b/docs/source/validated_model_list.md similarity index 100% rename from docs/source/io_doc/validated_model_list.md rename to docs/source/validated_model_list.md diff --git a/docs/source/io_doc/welcome.md b/docs/source/welcome.md similarity index 96% rename from docs/source/io_doc/welcome.md rename to docs/source/welcome.md index 51e12e13a40..3531bf0e052 100644 --- a/docs/source/io_doc/welcome.md +++ b/docs/source/welcome.md @@ -7,7 +7,7 @@ Intel® Neural Compressor (formerly known as Intel® Low Precision Optimization | Architecture | Workflow | | - | - | -| ![Architecture](imgs/architecture.png "Architecture") | ![Workflow](imgs/workflow.png "Workflow") | +| ![Architecture](./_static/imgs/architecture.png "Architecture") | ![Workflow](./_static/imgs/workflow.png "Workflow") | Supported deep learning frameworks are: diff --git a/examples/notebook/usage_example.md b/examples/notebook/usage_example.md index b1454ce04c8..90f910140e6 100644 --- a/examples/notebook/usage_example.md +++ b/examples/notebook/usage_example.md @@ -3,7 +3,7 @@ ## Steps The following diagram shows steps for enabling model with Neural Compressor: -Tutorial +Tutorial ## Example diff --git a/examples/pytorch/nlp/huggingface_models/common/README.md b/examples/pytorch/nlp/huggingface_models/common/README.md index 17a2b3d22e3..4904434f6b4 100644 --- a/examples/pytorch/nlp/huggingface_models/common/README.md +++ b/examples/pytorch/nlp/huggingface_models/common/README.md @@ -16,7 +16,7 @@ limitations under the License.


- +

From 9891f10c0166ccddc7064fc69ec8036fd680154e Mon Sep 17 00:00:00 2001 From: "Yue, Wenjiao" Date: Tue, 22 Nov 2022 09:39:32 +0800 Subject: [PATCH 08/17] modify makefile --- docs/Makefile | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/Makefile b/docs/Makefile index 329d0ea574d..adfbe405234 100644 --- a/docs/Makefile +++ b/docs/Makefile @@ -18,7 +18,7 @@ help: html: - # cp README.md to docs, modify response-link + # cp README.md to docs, modify link cp -f "../README.md" "./source/README.md" cp -f "./source/README.md" "./source/README.md.tmp" sed 's/.md/.html/g; s/.\/docs\/source\//.\//g; s/.\/neural_coder\/extensions\/screenshots/imgs/g; s/.\/docs\/source\/_static/..\/\/_static/g;' "./source/README.md.tmp" > "./source/README.md" From 1b79dccadcc0d268add7a7fc504a99d0c7deeeb8 Mon Sep 17 00:00:00 2001 From: "Yue, Wenjiao" Date: Tue, 22 Nov 2022 16:47:10 +0800 Subject: [PATCH 09/17] modify publihs.yml --- .github/workflows/publish.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/publish.yml b/.github/workflows/publish.yml index f3297bef2d8..69348ebd344 100644 --- a/.github/workflows/publish.yml +++ b/.github/workflows/publish.yml @@ -3,7 +3,7 @@ name: Publish on: push: branches: - - api-docs + - master jobs: build: From 5813de1f6612cf0cb53ba2dca5258b37c2fe0082 Mon Sep 17 00:00:00 2001 From: "Yue, Wenjiao" Date: Tue, 22 Nov 2022 16:48:05 +0800 Subject: [PATCH 10/17] modify makefile Signed-off-by: Yue, Wenjiao --- docs/Makefile | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/Makefile b/docs/Makefile index adfbe405234..329d0ea574d 100644 --- a/docs/Makefile +++ b/docs/Makefile @@ -18,7 +18,7 @@ help: html: - # cp README.md to docs, modify link + # cp README.md to docs, modify response-link cp -f "../README.md" "./source/README.md" cp -f "./source/README.md" "./source/README.md.tmp" sed 's/.md/.html/g; s/.\/docs\/source\//.\//g; s/.\/neural_coder\/extensions\/screenshots/imgs/g; s/.\/docs\/source\/_static/..\/\/_static/g;' "./source/README.md.tmp" > "./source/README.md" From f4f85de617148021c7ff7520fb0b10223b2c9c4c Mon Sep 17 00:00:00 2001 From: "Yue, Wenjiao" Date: Tue, 22 Nov 2022 17:37:11 +0800 Subject: [PATCH 11/17] modify publish.yml push branch Signed-off-by: Yue, Wenjiao --- .github/workflows/publish.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/publish.yml b/.github/workflows/publish.yml index 69348ebd344..f3297bef2d8 100644 --- a/.github/workflows/publish.yml +++ b/.github/workflows/publish.yml @@ -3,7 +3,7 @@ name: Publish on: push: branches: - - master + - api-docs jobs: build: From 94346e9bf29565d1b49d03a4528d44683310f146 Mon Sep 17 00:00:00 2001 From: "Yue, Wenjiao" Date: Tue, 22 Nov 2022 21:19:10 +0800 Subject: [PATCH 12/17] modify not display rule Signed-off-by: Yue, Wenjiao --- neural_compressor/experimental/data/datasets/bert_dataset.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/neural_compressor/experimental/data/datasets/bert_dataset.py b/neural_compressor/experimental/data/datasets/bert_dataset.py index 335fcd557ae..ddc8380b0fd 100644 --- a/neural_compressor/experimental/data/datasets/bert_dataset.py +++ b/neural_compressor/experimental/data/datasets/bert_dataset.py @@ -30,7 +30,7 @@ @dataset_registry(dataset_type="bert", framework="pytorch", dataset_format='') class PytorchBertDataset(Dataset): - """Not displayed. + """Not displayed in API Docs. Dataset used for model Bert. This Dataset is to construct from the Bert TensorDataset and not a full implementation from yaml config. The original repo link is: https://github.com/huggingface/transformers. From d16735cf71edc694e65f77ed0df27a159c6e57b3 Mon Sep 17 00:00:00 2001 From: WenjiaoYue <108783334+WenjiaoYue@users.noreply.github.com> Date: Fri, 25 Nov 2022 17:01:20 +0800 Subject: [PATCH 13/17] add line for DocString --- neural_compressor/experimental/data/datasets/bert_dataset.py | 1 + 1 file changed, 1 insertion(+) diff --git a/neural_compressor/experimental/data/datasets/bert_dataset.py b/neural_compressor/experimental/data/datasets/bert_dataset.py index 23dd884db51..c22abaa996e 100644 --- a/neural_compressor/experimental/data/datasets/bert_dataset.py +++ b/neural_compressor/experimental/data/datasets/bert_dataset.py @@ -33,6 +33,7 @@ @dataset_registry(dataset_type="bert", framework="pytorch", dataset_format='') class PytorchBertDataset(Dataset): """PyTorch dataset used for model Bert. + This Dataset is to construct from the Bert TensorDataset and not a full implementation from yaml config. The original repo link is: https://github.com/huggingface/transformers. When you want use this Dataset, you should add it before you initialize your DataLoader. From 96381a1fe50bae74eff98995a8e36f8510a45a7d Mon Sep 17 00:00:00 2001 From: WenjiaoYue <108783334+WenjiaoYue@users.noreply.github.com> Date: Fri, 25 Nov 2022 17:04:40 +0800 Subject: [PATCH 14/17] modify DocString --- neural_compressor/experimental/common/criterion.py | 13 +++++++------ 1 file changed, 7 insertions(+), 6 deletions(-) diff --git a/neural_compressor/experimental/common/criterion.py b/neural_compressor/experimental/common/criterion.py index 4382e827225..50f4972353b 100644 --- a/neural_compressor/experimental/common/criterion.py +++ b/neural_compressor/experimental/common/criterion.py @@ -1252,12 +1252,14 @@ def __call__(self, **kwargs): class SelfKnowledgeDistillationLoss(KnowledgeDistillationFramework): """SelfKnowledge Distillation Loss.""" - def __init__(self, layer_mappings=[], loss_types=None, loss_weights=None, temperature=1.0,add_origin_loss=False, student_model=None, teacher_model=None): + def __init__(self, layer_mappings=[], loss_types=None, loss_weights=None, + temperature=1.0,add_origin_loss=False, student_model=None, teacher_model=None): """Initialize SelfKnowledge Distillation Loss class. Args: layer_mappings (list): layers of distillation.Format like - [[[student1_layer_name1, teacher_layer_name1],[student2_layer_name1, teacher_layer_name1]],[[student1_layer_name2, teacher_layer_name2],[student2_layer_name2, teacher_layer_name2]]] + [[[student1_layer_name1, teacher_layer_name1],[student2_layer_name1, teacher_layer_name1]], + [[student1_layer_name2, teacher_layer_name2],[student2_layer_name2, teacher_layer_name2]]] loss_types (list, optional): loss types. Defaults to ['CE'] * len(layer_mappings). loss_weights (list, optional): loss weights. Defaults to [1.0 / len(layer_mappings)] * len(layer_mappings).temperature (float, optional): use to calculate the soft label CE. temperature (optional): temperature. Defaults to 1.0. @@ -1338,16 +1340,15 @@ def __call__(self, student_outputs, targets): return 0 -class PyTorchSelfKnowledgeDistillationLoss( - SelfKnowledgeDistillationLoss -): +class PyTorchSelfKnowledgeDistillationLoss(SelfKnowledgeDistillationLoss): """PyTorch SelfKnowledge Distillation Loss.""" def __init__(self, layer_mappings=[], loss_types=None, loss_weights=None, temperature=1.0,add_origin_loss=False, student_model=None, teacher_model=None): """Initialize PyTorch SelfKnowledge Distillation Loss class. Args: layer_mappings (list): layers of distillation.Format like - [[[student1_layer_name1, teacher_layer_name1],[student2_layer_name1, teacher_layer_name1]],[[student1_layer_name2, teacher_layer_name2],[student2_layer_name2, teacher_layer_name2]]] + [[[student1_layer_name1, teacher_layer_name1],[student2_layer_name1, teacher_layer_name1]], + [[student1_layer_name2, teacher_layer_name2],[student2_layer_name2, teacher_layer_name2]]] loss_types (list, optional): loss types. Defaults to ['CE'] * len(layer_mappings). loss_weights (list, optional): loss weights. Defaults to [1.0 / len(layer_mappings)] * len(layer_mappings).temperature (float, optional): use to calculate the soft label CE. temperature (optional): temperature. Defaults to 1.0. From 7af9e8545f10580efc0876cc6d77201d937ab407 Mon Sep 17 00:00:00 2001 From: WenjiaoYue <108783334+WenjiaoYue@users.noreply.github.com> Date: Fri, 25 Nov 2022 17:09:08 +0800 Subject: [PATCH 15/17] add lpot_dict for makeFile --- .../scripts/codeScan/pyspelling/lpot_dict.txt | 8 ++++++++ 1 file changed, 8 insertions(+) diff --git a/.azure-pipelines/scripts/codeScan/pyspelling/lpot_dict.txt b/.azure-pipelines/scripts/codeScan/pyspelling/lpot_dict.txt index ec5c0321b29..f692cb5bc63 100644 --- a/.azure-pipelines/scripts/codeScan/pyspelling/lpot_dict.txt +++ b/.azure-pipelines/scripts/codeScan/pyspelling/lpot_dict.txt @@ -2378,3 +2378,11 @@ constfold grappler amsgrad qoperator +BUILDDIR +BUILDIMGDIR +CODEIMGDIR +IMGDIR +SOURCEDIR +SPHINXBUILD +SPHINXOPTS +makefile From f4bccf357f9bdfa718c8954fa356f4203e3cc178 Mon Sep 17 00:00:00 2001 From: WenjiaoYue <108783334+WenjiaoYue@users.noreply.github.com> Date: Fri, 25 Nov 2022 18:26:02 +0800 Subject: [PATCH 16/17] add autoapi into dict --- .azure-pipelines/scripts/codeScan/pyspelling/lpot_dict.txt | 1 + 1 file changed, 1 insertion(+) diff --git a/.azure-pipelines/scripts/codeScan/pyspelling/lpot_dict.txt b/.azure-pipelines/scripts/codeScan/pyspelling/lpot_dict.txt index f692cb5bc63..49db2f1d945 100644 --- a/.azure-pipelines/scripts/codeScan/pyspelling/lpot_dict.txt +++ b/.azure-pipelines/scripts/codeScan/pyspelling/lpot_dict.txt @@ -2386,3 +2386,4 @@ SOURCEDIR SPHINXBUILD SPHINXOPTS makefile +autoapi From ad37a105eafd280d5c4f47d8ae82b6e544dcee5e Mon Sep 17 00:00:00 2001 From: "Yue, Wenjiao" Date: Fri, 25 Nov 2022 21:34:39 +0800 Subject: [PATCH 17/17] modify lpot_dict Signed-off-by: Yue, Wenjiao --- .../scripts/codeScan/pyspelling/lpot_dict.txt | 9 --------- .../scripts/codeScan/pyspelling/pyspelling_conf.yaml | 2 +- 2 files changed, 1 insertion(+), 10 deletions(-) diff --git a/.azure-pipelines/scripts/codeScan/pyspelling/lpot_dict.txt b/.azure-pipelines/scripts/codeScan/pyspelling/lpot_dict.txt index 49db2f1d945..ec5c0321b29 100644 --- a/.azure-pipelines/scripts/codeScan/pyspelling/lpot_dict.txt +++ b/.azure-pipelines/scripts/codeScan/pyspelling/lpot_dict.txt @@ -2378,12 +2378,3 @@ constfold grappler amsgrad qoperator -BUILDDIR -BUILDIMGDIR -CODEIMGDIR -IMGDIR -SOURCEDIR -SPHINXBUILD -SPHINXOPTS -makefile -autoapi diff --git a/.azure-pipelines/scripts/codeScan/pyspelling/pyspelling_conf.yaml b/.azure-pipelines/scripts/codeScan/pyspelling/pyspelling_conf.yaml index 07fe82c07f3..209e60ee4dd 100644 --- a/.azure-pipelines/scripts/codeScan/pyspelling/pyspelling_conf.yaml +++ b/.azure-pipelines/scripts/codeScan/pyspelling/pyspelling_conf.yaml @@ -7,7 +7,7 @@ matrix: - ${DICT_DIR}/lpot_dict.txt output: ${DICT_DIR}/lpot_dict.dic sources: - - ${REPO_DIR}/docs/* + - ${REPO_DIR}/docs/source/*.md - ${REPO_DIR}/*.md - ${REPO_DIR}/examples/**/*.md|!${REPO_DIR}/examples/pytorch/**/huggingface_models/**/*.md - ${REPO_DIR}/neural_compressor/**/*.md