Why does onnx-tensorflow add Transpose layers for each Conv2D layer?

[AlexeyAB]

Describe the bug

1. Why does onnx-tensorflow add Transpose layers for each Conv2D layer?

2. Why does onnx-tensorflow use multiple Conv2D layers instead of one GroupedConv2D layer?

It increases inference time, so PB-model that is converted by using onnx-tensorflow is slower than the same native TF-model.

If I want to convert NCHW-ONNX to NCHW-PB to run on GPU, and then convert NCHW-PB to NHWC-TFLITE, then how should I do this? (Currently tf.lite.TFLiteConverter.from_saved_model(saved_model_export_dir) allows to convert NCHW-PB to NHWC-TFLITE without adding extra-transpose layers.

To Reproduce

https://colab.research.google.com/gist/AlexeyAB/10b1bf880152b1ad7ca116b428863068/pytorch-onnx-tf-extra-transpose.ipynb

pytorch_onnx_tf_extra_transpose_ipynp.zip

Python, ONNX, ONNX-TF, Tensorflow version

This section can be obtained by running get_version.py from util folder.

Python version:
3.6.9 (default, Jul 17 2020, 12:50:27) 
[GCC 8.4.0]
ONNX version:
1.7.0
ONNX-TF version:
1.6.0
Tensorflow version:
2.4.0-dev20201010

Additional context

PB-model with extra Transpose layers:

---

Result TFLITE-model with extra Transpose layers:

[li2508264]

@AlexeyAB I have the same problem,transpose op increases inference time。Do you have any solutions？

[ZhangJunsong-NR]

Same with you. Any method to deal with these transpose ops? it cost too much time.

[Mkarami3]

Any update on this issue?

[shashwat14]

I have the same issue

[TalhaUsuf]

I have same issue, pytorch(NCHW) --> onnx(NCHW) --> tf-saved-model(NHWC) [transpose layers introduced here].

@AlexeyAB Please suggest if you found solution?

[TalhaUsuf]

I have same issue, pytorch(NCHW) --> onnx(NCHW) --> tf-saved-model(NHWC) [transpose layers introduced here].

@AlexeyAB Please suggest if you found solution?

I found the solution, perform following to convert form pytorch ---> tflite (NCHW ---> NHWC):

• pytorch official export to onnx [N,C,H,W] ---> [N,C,H,W]
• onnx conversion to open-vino IR format [N,C,H,W] ---> [N,C,H,W] (using openvino docker image)
• open-vino-IR format to tensorflow format [N,C,H,W] ---> [N,H,W,C] (using openvino2tensorflow :zap: :bulb: docker image, see https://github.com/PINTO0309/openvino2tensorflow.git)
• tensorflow format to tflite [N,H,W,C] ---> [N,H,W,C] :smile:

[PINTO0309]

At this time, this tool, which is a greatly improved version of openvino2tensorflow, is much easier and more accurate for conversion. Incidentally, GroupedConvolution is also supported.

"Self-Created Tools to convert ONNX files (NCHW) to TensorFlow/TFLite/Keras format (NHWC). The purpose of this tool is to solve the massive Transpose extrapolation problem in onnx-tensorflow (onnx-tf)." https://github.com/PINTO0309/onnx2tf