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Update README.md, bump up version to 0.2.0
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README.md

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```
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Currently supported and tested models from [onnx_zoo](https://github.com/onnx/models):
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- MobileNet
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- ResNet
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- ShuffleNet
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- Bert
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- [MobileNet](https://github.com/onnx/models/tree/master/vision/classification/mobilenet)
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- [ResNet](https://github.com/onnx/models/tree/master/vision/classification/resnet)
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- [ShuffleNet_V2](https://github.com/onnx/models/tree/master/vision/classification/shufflenet)
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- [BERT-Squad](https://github.com/onnx/models/tree/master/text/machine_comprehension/bert-squad)
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- [EfficientNet-Lite4](https://github.com/onnx/models/tree/master/vision/classification/efficientnet-lite4)
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- [Fast Neural Style Transfer](https://github.com/onnx/models/tree/master/vision/style_transfer/fast_neural_style)
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- [Super Resolution](https://github.com/onnx/models/tree/master/vision/super_resolution/sub_pixel_cnn_2016)
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- [YOLOv4](https://github.com/onnx/models/tree/master/vision/object_detection_segmentation/yolov4)
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(Not exactly the same, nearest neighbour interpolation in pytorch differs)
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- [U-net](https://pytorch.org/hub/mateuszbuda_brain-segmentation-pytorch_unet/)
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(Converted from pytorch to onnx and then back)
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## Limitations
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Known current version limitations are:
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### Testing
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[Pytest](https://docs.pytest.org/en/latest/) and [tox](https://tox.readthedocs.io/en/latest/).
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```tox```
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```tox```
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#### Test fixtures
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To test the complete conversion of an onnx model download pre-trained models:
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```./download_fixtures.sh```
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Use flag `--all` to download more models.
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Add any custom models to `./fixtures` folder to test their conversion.
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### Debugging
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Set `ConvertModel(..., debug=True)` to compare each converted
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activation from pytorch with the activation from onnxruntime.
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This helps identify where in the graph the activations start to differ.

setup.py

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setup(
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name="onnx2pytorch",
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version="0.1.1",
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version="0.2.0",
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description="Library to transform onnx model to pytorch.",
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license="apache-2.0",
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author="Talmaj Marinc",

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