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).

You can install onnxoptimizer from PyPI:

pip3 install onnxoptimizer

Note that you may need to upgrade your pip first if you have trouble:

pip3 install -U pip

If you want to build from source:

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.

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

• More built-in pass
• Separate graph rewriting and constant folding (or a pure graph rewriting mode, see issue #9 for the details)

• onnx-simplifier: A handy and popular tool based on onnxoptimizer

ONNX Open Source Code of Conduct