A library to transform ONNX model to PyTorch. This library enables use of PyTorch backend and all of its great features for manipulation of neural networks.

pip install onnx2pytorch

import onnx
from onnx2pytorch import ConvertModel

onnx_model = onnx.load(path_to_onnx_model)
pytorch_model = ConvertModel(onnx_model)

Currently supported and tested models from onnx_zoo:

• MobileNet
• ResNet
• ShuffleNet_V2
• BERT-Squad
• EfficientNet-Lite4
• Fast Neural Style Transfer
• Super Resolution
• YOLOv4 (Not exactly the same, nearest neighbour interpolation in pytorch differs)
• U-net (Converted from pytorch to onnx and then back)

Known current version limitations are:

• batch_size > 1 could deliver unexpected results due to ambiguity of onnx's BatchNorm layer.
  That is why in this case for now we raise an assertion error.
  Set experimental=True in ConvertModel to be able to use batch_size > 1.
• Fine tuning and training of converted models was not tested yet, only inference.

pip install -r requirements.txt

From onnxruntime>=1.5.0 you need to add the following to your .bashrc or .zshrc if you are running OSx: export KMP_DUPLICATE_LIB_OK=True

The Uncompromising Code Formatter: Black
black {source_file_or_directory}

Install it into pre-commit hook to always commit nicely formatted code:
pre-commmit install

Pytest and tox.
tox

To test the complete conversion of an onnx model download pre-trained models: ./download_fixtures.sh
Use flag --all to download more models. Add any custom models to ./fixtures folder to test their conversion.

Set ConvertModel(..., debug=True) to compare each converted activation from pytorch with the activation from onnxruntime.
This helps identify where in the graph the activations start to differ.