Custom ONNX operators and converters
Project description
Tutorial on how to convert machine learned models into ONNX, implement your own converter or runtime. The module must be compiled to be used inplace:
python setup.py build_ext --inplace
Generate the setup in subfolder dist:
python setup.py sdist
Generate the documentation in folder dist/html:
python -m sphinx -T -b html doc dist/html
Run the unit tests:
python -m unittest discover tests
Or:
python -m pytest
To check style:
python -m flake8 onnxcustom tests examples
The function check or the command line python -m onnxcustom check checks the module is properly installed and returns processing time for a couple of functions or simply:
import onnxcustom onnxcustom.check()
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