%global _empty_manifest_terminate_build 0 Name: python-onnxoptimizer Version: 0.3.13 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/68/bd/e8671229c2f1f99eb02961cac51e55ca64dbbe0d62791b6743cc8b9950b1/onnxoptimizer-0.3.13.tar.gz Requires: python3-onnx Requires: python3-mypy %description # ONNX Optimizer [![PyPI version](https://img.shields.io/pypi/v/onnxoptimizer.svg)](https://pypi.python.org/pypi/onnxoptimizer/) [![PyPI license](https://img.shields.io/pypi/l/onnxoptimizer.svg)](https://pypi.python.org/pypi/onnxoptimizer/) [![PRs Welcome](https://img.shields.io/badge/PRs-welcome-brightgreen.svg)](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 # ONNX Optimizer [![PyPI version](https://img.shields.io/pypi/v/onnxoptimizer.svg)](https://pypi.python.org/pypi/onnxoptimizer/) [![PyPI license](https://img.shields.io/pypi/l/onnxoptimizer.svg)](https://pypi.python.org/pypi/onnxoptimizer/) [![PRs Welcome](https://img.shields.io/badge/PRs-welcome-brightgreen.svg)](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 # ONNX Optimizer [![PyPI version](https://img.shields.io/pypi/v/onnxoptimizer.svg)](https://pypi.python.org/pypi/onnxoptimizer/) [![PyPI license](https://img.shields.io/pypi/l/onnxoptimizer.svg)](https://pypi.python.org/pypi/onnxoptimizer/) [![PRs Welcome](https://img.shields.io/badge/PRs-welcome-brightgreen.svg)](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-0.3.13 %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 * Sun Apr 23 2023 Python_Bot - 0.3.13-1 - Package Spec generated