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* Add notes for tensors * Optimize some apis * move some warnings * Support build with Paddle2ONNX * Add protobuf support * Fix compile on mac * add clearn package script * Add paddle2onnx code * remove submodule * Add onnx ocde * remove softlink * add onnx code * fix error * Add cmake file * fix patchelf * update paddle2onnx * Delete .gitmodules --------- Co-authored-by: PaddleCI <paddle_ci@example.com> Co-authored-by: pangyoki <pangyoki@126.com> Co-authored-by: jiangjiajun <jiangjiajun@baidu.lcom>
56 lines
2.0 KiB
Markdown
56 lines
2.0 KiB
Markdown
<!--- SPDX-License-Identifier: Apache-2.0 -->
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# ONNX Optimizer
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[](https://pypi.python.org/pypi/onnxoptimizer/)
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[](https://pypi.python.org/pypi/onnxoptimizer/)
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[](https://github.com/onnx/optimizer/pulls)
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## Introduction
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ONNX provides a C++ library for performing arbitrary optimizations on ONNX models, as well as a growing list of prepackaged optimization passes.
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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.
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You may be interested in invoking the provided passes, or in implementing new ones (or both).
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## Installation
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You can install onnxoptimizer from PyPI:
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```bash
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pip3 install onnxoptimizer
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```
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Note that you may need to upgrade your pip first if you have trouble:
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```bash
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pip3 install -U pip
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```
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If you want to build from source:
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```bash
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git clone --recursive https://github.com/onnx/optimizer onnxoptimizer
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cd onnxoptimizer
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pip3 install -e .
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```
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Note that you need to install protobuf before building from source.
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## Roadmap
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* Command-line API (e.g. `python3 -m onnxoptimizer model.onnx output.onnx`)
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* More built-in pass
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* 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)
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## Relevant tools
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* [onnx-simplifier](https://github.com/daquexian/onnx-simplifier): A handy and popular tool based on onnxoptimizer
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* [convertmodel.com](https://convertmodel.com/#outputFormat=onnx&inputFormat=onnx): onnx optimizer compiled as WebAssembly so that it can be used out-of-the-box
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## Code of Conduct
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[ONNX Open Source Code of Conduct](https://onnx.ai/codeofconduct.html)
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