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@@ -19,7 +19,7 @@ The following procedures were used to generate our ONNX models.
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pip install -r requirements.txt
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```
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-2. A few modifications to the PyTorch source. This is needed before pull request [#60080](https://github.com/pytorch/pytorch/pull/60080) is merged into later version of PyTorch. If you are exporting MobileNetV3 variant, go to your local PyTorch install and override the following method to file `site-packages/torch/onnx/symbolic_opset9.py`. This allows export of `hardswish` as native ops.
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+2. (Only for PyTorch <= 1.9) A few modifications to the PyTorch source. This is needed before pull request [#60080](https://github.com/pytorch/pytorch/pull/60080) is merged into later version of PyTorch. If you are exporting MobileNetV3 variant, go to your local PyTorch install and override the following method to file `site-packages/torch/onnx/symbolic_opset9.py`. This allows export of `hardswish` as native ops.
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Also note, if your inference backend does not support `hardswish` or `hardsigmoid`. You can also use this hack to replace them with primitive ops.
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@@ -43,4 +43,4 @@ python export_onnx.py \
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## Additional
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-Our model is tested to work on ONNX Runtime's CPU and CUDA backends. If your inference backend has compatibility issue to certain ops, you can file an issue on GitHub, but we don't guarantee solutions. Feel free to write your own export code that fits your need.
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+Our model is tested to work on ONNX Runtime's CPU and CUDA backends. If your inference backend has compatibility issue to certain ops, you can file an issue on GitHub, but we don't guarantee solutions. Feel free to write your own export code that fits your need.
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Peter Lin