Refactoring for use as importable package

[lillekemiker]

Refactoring for use as importable package

[PINTO0309]

Thanks for the suggestion.

I would like to refactor the code. For example, as you suggest, I would like to add an interface to call from Script or clean up the redundant and buggy code. However, I maintain a lot of other OSS and cannot allocate enough time to work on replacing this tool right now.

My main focus and maintenance right now is this tool that directly processes ONNX.
https://github.com/PINTO0309/simple-onnx-processing-tools

I understand that using onnx-tf will significantly degrade the performance of the TensorFlow model due to the large amount of useless Transpose OPs extrapolated. However, since I can see no real advantage other than running TensorFlow Lite on Android Phone, I am considering onnxruntime as my mainstay runtime, noting that it offers significantly greater operational flexibility by switching between various backends.

e.g.

• TensorRT
• TensorFlow Lite
• OpenVINO (CPU/GPU)
• MNN
• TVM
• CUDA
• Barracuda

Therefore, it is very hard for me to re-enhance this tool by myself, which has become bloated after two years of continually enhancing its functions little by little.

Pull requests that improve the usefulness of the tool are very welcome. However, I think it will take a very long time to review.
And now I feel it would be much more efficient to create my own onnx-tf, which is different from the onnx-tf maintained by Microsoft.

[lillekemiker]

My use case is actually running TFLite on an Android (and iOS) phone.
Is there currently a different workflow you would recommend for this?

[PINTO0309]

https://github.com/PINTO0309/onnx2tf