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diff --git a/python-onnxoptimizer.spec b/python-onnxoptimizer.spec new file mode 100644 index 0000000..5bac16d --- /dev/null +++ b/python-onnxoptimizer.spec @@ -0,0 +1,310 @@ +%global _empty_manifest_terminate_build 0 +Name: python-onnxoptimizer +Version: 0.3.10 +Release: 1 +Summary: Open Neural Network Exchange +License: Apache License v2.0 +URL: https://github.com/onnx/optimizer +Source0: https://mirrors.nju.edu.cn/pypi/web/packages/90/d4/f0100c670fea4653dd3bdbde7848f71b19dc49696e5c73077a44099d9911/onnxoptimizer-0.3.10.tar.gz + +Requires: python3-onnx +Requires: python3-mypy + +%description +<!--- SPDX-License-Identifier: Apache-2.0 --> + +# ONNX Optimizer + +[](https://pypi.python.org/pypi/onnxoptimizer/) +[](https://pypi.python.org/pypi/onnxoptimizer/) +[](https://github.com/onnx/optimizer/pulls) + +## Introduction + +ONNX provides a C++ library for performing arbitrary optimizations on ONNX models, as well as a growing list of prepackaged optimization passes. + +The primary motivation is to share work between the many ONNX backend implementations. Not all possible optimizations can be directly implemented on ONNX graphs - some will need additional backend-specific information - but many can, and our aim is to provide all such passes along with ONNX so that they can be re-used with a single function call. + +You may be interested in invoking the provided passes, or in implementing new ones (or both). + +## Installation + +You can install onnxoptimizer from PyPI: + +```bash +pip3 install onnxoptimizer +``` + +Note that you may need to upgrade your pip first if you have trouble: + +```bash +pip3 install -U pip +``` + +If you want to build from source: + +```bash +git clone --recursive https://github.com/onnx/optimizer onnxoptimizer +cd onnxoptimizer +pip3 install -e . +``` + +Note that you need to install protobuf before building from source. + + +## Command-line API +Now you can use command-line api in terminal instead of python script. + +``` +python -m onnxoptimizer input_model.onnx output_model.onnx +``` + +Arguments list is following: +``` +# python3 -m onnxoptimizer -h +usage: python -m onnxoptimizer input_model.onnx output_model.onnx + +onnxoptimizer command-line api + +optional arguments: + -h, --help show this help message and exit + --print_all_passes print all available passes + --print_fuse_elimination_passes + print all fuse and elimination passes + -p [PASSES ...], --passes [PASSES ...] + list of optimization passes name, if no set, fuse_and_elimination_passes will be used + --fixed_point fixed point +``` +## Roadmap + +* More built-in pass +* Separate graph rewriting and constant folding (or a pure graph rewriting mode, see [issue #9](https://github.com/onnx/optimizer/issues/9) for the details) + +## Relevant tools + +* [onnx-simplifier](https://github.com/daquexian/onnx-simplifier): A handy and popular tool based on onnxoptimizer + +* [convertmodel.com](https://convertmodel.com/#outputFormat=onnx&inputFormat=onnx): onnx optimizer compiled as WebAssembly so that it can be used out-of-the-box + +## Code of Conduct + +[ONNX Open Source Code of Conduct](https://onnx.ai/codeofconduct.html) + + +%package -n python3-onnxoptimizer +Summary: Open Neural Network Exchange +Provides: python-onnxoptimizer +BuildRequires: python3-devel +BuildRequires: python3-setuptools +BuildRequires: python3-pip +BuildRequires: python3-cffi +BuildRequires: gcc +BuildRequires: gdb +%description -n python3-onnxoptimizer +<!--- SPDX-License-Identifier: Apache-2.0 --> + +# ONNX Optimizer + +[](https://pypi.python.org/pypi/onnxoptimizer/) +[](https://pypi.python.org/pypi/onnxoptimizer/) +[](https://github.com/onnx/optimizer/pulls) + +## Introduction + +ONNX provides a C++ library for performing arbitrary optimizations on ONNX models, as well as a growing list of prepackaged optimization passes. + +The primary motivation is to share work between the many ONNX backend implementations. Not all possible optimizations can be directly implemented on ONNX graphs - some will need additional backend-specific information - but many can, and our aim is to provide all such passes along with ONNX so that they can be re-used with a single function call. + +You may be interested in invoking the provided passes, or in implementing new ones (or both). + +## Installation + +You can install onnxoptimizer from PyPI: + +```bash +pip3 install onnxoptimizer +``` + +Note that you may need to upgrade your pip first if you have trouble: + +```bash +pip3 install -U pip +``` + +If you want to build from source: + +```bash +git clone --recursive https://github.com/onnx/optimizer onnxoptimizer +cd onnxoptimizer +pip3 install -e . +``` + +Note that you need to install protobuf before building from source. + + +## Command-line API +Now you can use command-line api in terminal instead of python script. + +``` +python -m onnxoptimizer input_model.onnx output_model.onnx +``` + +Arguments list is following: +``` +# python3 -m onnxoptimizer -h +usage: python -m onnxoptimizer input_model.onnx output_model.onnx + +onnxoptimizer command-line api + +optional arguments: + -h, --help show this help message and exit + --print_all_passes print all available passes + --print_fuse_elimination_passes + print all fuse and elimination passes + -p [PASSES ...], --passes [PASSES ...] + list of optimization passes name, if no set, fuse_and_elimination_passes will be used + --fixed_point fixed point +``` +## Roadmap + +* More built-in pass +* Separate graph rewriting and constant folding (or a pure graph rewriting mode, see [issue #9](https://github.com/onnx/optimizer/issues/9) for the details) + +## Relevant tools + +* [onnx-simplifier](https://github.com/daquexian/onnx-simplifier): A handy and popular tool based on onnxoptimizer + +* [convertmodel.com](https://convertmodel.com/#outputFormat=onnx&inputFormat=onnx): onnx optimizer compiled as WebAssembly so that it can be used out-of-the-box + +## Code of Conduct + +[ONNX Open Source Code of Conduct](https://onnx.ai/codeofconduct.html) + + +%package help +Summary: Development documents and examples for onnxoptimizer +Provides: python3-onnxoptimizer-doc +%description help +<!--- SPDX-License-Identifier: Apache-2.0 --> + +# ONNX Optimizer + +[](https://pypi.python.org/pypi/onnxoptimizer/) +[](https://pypi.python.org/pypi/onnxoptimizer/) +[](https://github.com/onnx/optimizer/pulls) + +## Introduction + +ONNX provides a C++ library for performing arbitrary optimizations on ONNX models, as well as a growing list of prepackaged optimization passes. + +The primary motivation is to share work between the many ONNX backend implementations. Not all possible optimizations can be directly implemented on ONNX graphs - some will need additional backend-specific information - but many can, and our aim is to provide all such passes along with ONNX so that they can be re-used with a single function call. + +You may be interested in invoking the provided passes, or in implementing new ones (or both). + +## Installation + +You can install onnxoptimizer from PyPI: + +```bash +pip3 install onnxoptimizer +``` + +Note that you may need to upgrade your pip first if you have trouble: + +```bash +pip3 install -U pip +``` + +If you want to build from source: + +```bash +git clone --recursive https://github.com/onnx/optimizer onnxoptimizer +cd onnxoptimizer +pip3 install -e . +``` + +Note that you need to install protobuf before building from source. + + +## Command-line API +Now you can use command-line api in terminal instead of python script. + +``` +python -m onnxoptimizer input_model.onnx output_model.onnx +``` + +Arguments list is following: +``` +# python3 -m onnxoptimizer -h +usage: python -m onnxoptimizer input_model.onnx output_model.onnx + +onnxoptimizer command-line api + +optional arguments: + -h, --help show this help message and exit + --print_all_passes print all available passes + --print_fuse_elimination_passes + print all fuse and elimination passes + -p [PASSES ...], --passes [PASSES ...] + list of optimization passes name, if no set, fuse_and_elimination_passes will be used + --fixed_point fixed point +``` +## Roadmap + +* More built-in pass +* Separate graph rewriting and constant folding (or a pure graph rewriting mode, see [issue #9](https://github.com/onnx/optimizer/issues/9) for the details) + +## Relevant tools + +* [onnx-simplifier](https://github.com/daquexian/onnx-simplifier): A handy and popular tool based on onnxoptimizer + +* [convertmodel.com](https://convertmodel.com/#outputFormat=onnx&inputFormat=onnx): onnx optimizer compiled as WebAssembly so that it can be used out-of-the-box + +## Code of Conduct + +[ONNX Open Source Code of Conduct](https://onnx.ai/codeofconduct.html) + + +%prep +%autosetup -n onnxoptimizer-0.3.10 + +%build +%py3_build + +%install +%py3_install +install -d -m755 %{buildroot}/%{_pkgdocdir} +if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi +if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi +if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi +if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi +pushd %{buildroot} +if [ -d usr/lib ]; then + find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst +fi +if [ -d usr/lib64 ]; then + find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst +fi +if [ -d usr/bin ]; then + find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst +fi +if [ -d usr/sbin ]; then + find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst +fi +touch doclist.lst +if [ -d usr/share/man ]; then + find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst +fi +popd +mv %{buildroot}/filelist.lst . +mv %{buildroot}/doclist.lst . + +%files -n python3-onnxoptimizer -f filelist.lst +%dir %{python3_sitearch}/* + +%files help -f doclist.lst +%{_docdir}/* + +%changelog +* Tue Apr 11 2023 Python_Bot <Python_Bot@openeuler.org> - 0.3.10-1 +- Package Spec generated |