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|
%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
<!--- 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-0.3.13
%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 <Python_Bot@openeuler.org> - 0.3.13-1
- Package Spec generated
|
