Metadata-Version: 2.1
Name: onnxoptimizer
Version: 0.3.2
Summary: Open Neural Network Exchange
Home-page: https://github.com/onnx/optimizer
Author: ONNX Optimizer Authors
Author-email: onnx-technical-discuss@lists.lfai.foundation
License: Apache License v2.0
Keywords: deep-learning ONNX
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: onnx
Provides-Extra: mypy
Requires-Dist: mypy (==0.600) ; extra == 'mypy'

<!--- SPDX-License-Identifier: Apache-2.0 -->

# 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.

## Roadmap

* Command-line API (e.g. `python3 -m onnxoptimizer model.onnx output.onnx`)
* 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)
