Repository: opencv
Issue: DNN module: ONNX importer can not handle models generated by Pytorch==1.7.0
Labels: priority: low,category: dnn

##### System information (version)
<!-- Example
- OpenCV => 4.2
- Operating System / Platform => Windows 64 Bit
- Compiler => Visual Studio 2017
-->

- OpenCV => 4.5
- Operating System / Platform => Ubuntu 20.04
- Compiler => Qt 5.9.1

##### Detailed description
relates #17570 

[DB_pytorch1_7.onnx](https://drive.google.com/file/d/1WxMuXOsc7A1HsxHNYIjw-ZTSe8qkTerK/view?usp=sharing)
[DB_pytorch1_5.onnx](https://drive.google.com/file/d/1uTi7_TsP334Fr-QKkDy3BAAmMfYS08iK/view?usp=sharing)
The above ONNX models are generated from the same code, and the only difference is the Pytorch version.
I have tested that both of these models can be imported by `onnxruntime`, but `DB_pytorch1_7.onnx` can not be imported by OpenCV.
```
[ERROR:0] global /home/gordon/Hannibal/codes/opencv/modules/dnn/src/onnx/onnx_importer.cpp (1867) handleNode DNN/ONNX: ERROR during processing node with 2 inputs and 1 outputs: [Equal]:(572)
terminate called after throwing an instance of 'cv::Exception'
  what():  OpenCV(4.5.1-pre) /home/gordon/Hannibal/codes/opencv/modules/dnn/src/onnx/onnx_importer.cpp:1878: error: (-2:Unspecified error) in function 'handleNode'
> Node [Equal]:(572) parse error: OpenCV(4.5.1-pre) /home/gordon/Hannibal/codes/opencv/modules/dnn/src/dnn.cpp:614: error: (-2:Unspecified error) Can't create layer "572" of type "Equal" in function 'getLayerInstance'
> 

```

I think it is caused by the different graph of onnx models in different PyTorch version
- Graph of `DB_pytorch1_5.onnx`:

<details>

```
==> Exporting model to ONNX format at 'DB_pytorch1_5.onnx'
graph(%input : Float(1, 3, 736, 736),
      %backbone.conv1.weight : Float(64, 3, 7, 7),
      %backbone.bn1.weight : Float(64),
      %backbone.bn1.bias : Float(64),
      %backbone.bn1.running_mean : Float(64),
      %backbone.bn1.running_var : Float(64),
      %backbone.layer1.0.conv1.weight : Float(64, 64, 1, 1),
      %backbone.layer1.0.bn1.weight : Float(64),
      %backbone.layer1.0.bn1.bias : Float(64),
      %backbone.layer1.0.bn1.running_mean : Float(64),
      %backbone.layer1.0.bn1.running_var : Float(64),
      %backbone.layer1.0.conv2.weight : Float(64, 64, 3, 3),
      %backbone.layer1.0.bn2.weight : Float(64),
      %backbone.layer1.0.bn2.bias : Float(64),
      %backbone.layer1.0.bn2.running_mean : Float(64),
      %backbone.layer1.0.bn2.running_var : Float(64),
      %backbone.layer1.0.conv3.weight : Float(256, 64, 1, 1),
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      %backbone.layer1.0.bn3.bias : Float(256),
      %backbone.layer1.0.bn3.running_mean : Float(256),
      %backbone.layer1.0.bn3.running_var : Float(256),
      %backbone.layer1.0.downsample.0.weight : Float(256, 64, 1, 1),
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      %backbone.layer1.0.downsample.1.bias : Float(256),
      %backbone.layer1.0.downsample.1.running_mean : Float(256),
      %backbone.layer1.0.downsample.1.running_var : Float(256),
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      %backbone.layer1.1.bn1.bias : Float(64),
      %backbone.layer1.1.bn1.running_mean : Float(64),
      %backbone.layer1.1.bn1.running_var : Float(64),
      %backbone.layer1.1.conv2.weight : Float(64, 64, 3, 3),
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      %backbone.layer1.1.bn2.bias : Float(64),
      %backbone.layer1.1.bn2.running_mean : Float(64),
      %backbone.layer1.1.bn2.running_var : Float(64),
      %backbone.layer1.1.conv3.weight : Float(256, 64, 1, 1),
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      %backbone.layer1.1.bn3.bias : Float(256),
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      %backbone.layer1.2.bn3.running_mean : Float(256),
      %backbone.layer1.2.bn3.running_var : Float(256),
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      %backbone.layer2.0.bn1.running_var : Float(128),
      %backbone.layer2.0.conv2.weight : Float(128, 128, 3, 3),
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      %backbone.layer2.0.bn2.bias : Float(128),
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      %backbone.layer2.0.bn2.running_var : Float(128),
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      %backbone.layer2.0.downsample.0.weight : Float(512, 256, 1, 1),
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      %backbone.layer2.1.conv2.weight : Float(128, 128, 3, 3),
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      %backbone.layer2.1.conv3.weight : Float(512, 128, 1, 1),
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      %backbone.layer2.1.bn3.bias : Float(512),
      %backbone.layer2.1.bn3.running_mean : Float(512),
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      %backbone.layer2.2.bn3.running_mean : Float(512),
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      %backbone.layer2.3.conv2.weight : Float(128, 128, 3, 3),
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      %backbone.layer3.5.conv2.weight : Float(256, 256, 3, 3),
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      %backbone.layer3.5.bn3.running_mean : Float(1024),
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      %backbone.layer4.0.bn3.running_mean : Float(2048),
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      %backbone.layer4.1.bn2.bias : Float(512),
      %backbone.layer4.1.bn2.running_mean : Float(512),
      %backbone.layer4.1.bn2.running_var : Float(512),
      %backbone.layer4.1.conv3.weight : Float(2048, 512, 1, 1),
      %backbone.layer4.1.bn3.weight : Float(2048),
      %backbone.layer4.1.bn3.bias : Float(2048),
      %backbone.layer4.1.bn3.running_mean : Float(2048),
      %backbone.layer4.1.bn3.running_var : Float(2048),
      %backbone.layer4.2.conv1.weight : Float(512, 2048, 1, 1),
      %backbone.layer4.2.bn1.weight : Float(512),
      %backbone.layer4.2.bn1.bias : Float(512),
      %backbone.layer4.2.bn1.running_mean : Float(512),
      %backbone.layer4.2.bn1.running_var : Float(512),
      %backbone.layer4.2.conv2.weight : Float(512, 512, 3, 3),
      %backbone.layer4.2.bn2.weight : Float(512),
      %backbone.layer4.2.bn2.bias : Float(512),
      %backbone.layer4.2.bn2.running_mean : Float(512),
      %backbone.layer4.2.bn2.running_var : Float(512),
      %backbone.layer4.2.conv3.weight : Float(2048, 512, 1, 1),
      %backbone.layer4.2.bn3.weight : Float(2048),
      %backbone.layer4.2.bn3.bias : Float(2048),
      %backbone.layer4.2.bn3.running_mean : Float(2048),
      %backbone.layer4.2.bn3.running_var : Float(2048),
      %decoder.in5.weight : Float(256, 2048, 1, 1),
      %decoder.in4.weight : Float(256, 1024, 1, 1),
      %decoder.in3.weight : Float(256, 512, 1, 1),
      %decoder.in2.weight : Float(256, 256, 1, 1),
      %decoder.out5.0.weight : Float(64, 256, 3, 3),
      %decoder.out4.0.weight : Float(64, 256, 3, 3),
      %decoder.out3.0.weight : Float(64, 256, 3, 3),
      %decoder.out2.weight : Float(64, 256, 3, 3),
      %decoder.binarize.0.weight : Float(64, 256, 3, 3),
      %decoder.binarize.1.weight : Float(64),
      %decoder.binarize.1.bias : Float(64),
      %decoder.binarize.1.running_mean : Float(64),
      %decoder.binarize.1.running_var : Float(64),
      %decoder.binarize.3.weight : Float(64, 64, 2, 2),
      %decoder.binarize.3.bias : Float(64),
      %decoder.binarize.4.weight : Float(64),
      %decoder.binarize.4.bias : Float(64),
      %decoder.binarize.4.running_mean : Float(64),
      %decoder.binarize.4.running_var : Float(64),
      %decoder.binarize.6.weight : Float(64, 1, 2, 2),
      %decoder.binarize.6.bias : Float(1)):
  %346 : Float(1, 64, 368, 368) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[7, 7], pads=[3, 3, 3, 3], strides=[2, 2]](%input, %backbone.conv1.weight) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/modules/conv.py:346:0
  %347 : Float(1, 64, 368, 368) = onnx::BatchNormalization[epsilon=1.0000000000000001e-05, momentum=0.90000000000000002](%346, %backbone.bn1.weight, %backbone.bn1.bias, %backbone.bn1.running_mean, %backbone.bn1.running_var) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/functional.py:1923:0
  %348 : Float(1, 64, 368, 368) = onnx::Relu(%347) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/functional.py:1061:0
  %349 : Float(1, 64, 184, 184) = onnx::MaxPool[ceil_mode=0, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[2, 2]](%348) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/functional.py:539:0
  %350 : Float(1, 64, 184, 184) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%349, %backbone.layer1.0.conv1.weight) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/modules/conv.py:346:0
  %351 : Float(1, 64, 184, 184) = onnx::BatchNormalization[epsilon=1.0000000000000001e-05, momentum=0.90000000000000002](%350, %backbone.layer1.0.bn1.weight, %backbone.layer1.0.bn1.bias, %backbone.layer1.0.bn1.running_mean, %backbone.layer1.0.bn1.running_var) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/functional.py:1923:0
  %352 : Float(1, 64, 184, 184) = onnx::Relu(%351) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/functional.py:1061:0
  %353 : Float(1, 64, 184, 184) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1]](%352, %backbone.layer1.0.conv2.weight) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/modules/conv.py:346:0
  %354 : Float(1, 64, 184, 184) = onnx::BatchNormalization[epsilon=1.0000000000000001e-05, momentum=0.90000000000000002](%353, %backbone.layer1.0.bn2.weight, %backbone.layer1.0.bn2.bias, %backbone.layer1.0.bn2.running_mean, %backbone.layer1.0.bn2.running_var) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/functional.py:1923:0
  %355 : Float(1, 64, 184, 184) = onnx::Relu(%354) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/functional.py:1061:0
  %356 : Float(1, 256, 184, 184) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%355, %backbone.layer1.0.conv3.weight) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/modules/conv.py:346:0
  %357 : Float(1, 256, 184, 184) = onnx::BatchNormalization[epsilon=1.0000000000000001e-05, momentum=0.90000000000000002](%356, %backbone.layer1.0.bn3.weight, %backbone.layer1.0.bn3.bias, %backbone.layer1.0.bn3.running_mean, %backbone.layer1.0.bn3.running_var) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/functional.py:1923:0
  %358 : Float(1, 256, 184, 184) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%349, %backbone.layer1.0.downsample.0.weight) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/modules/conv.py:346:0
  %359 : Float(1, 256, 184, 184) = onnx::BatchNormalization[epsilon=1.0000000000000001e-05, momentum=0.90000000000000002](%358, %backbone.layer1.0.downsample.1.weight, %backbone.layer1.0.downsample.1.bias, %backbone.layer1.0.downsample.1.running_mean, %backbone.layer1.0.downsample.1.running_var) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/functional.py:1923:0
  %360 : Float(1, 256, 184, 184) = onnx::Add(%357, %359)
  %361 : Float(1, 256, 184, 184) = onnx::Relu(%360) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/functional.py:1061:0
  %362 : Float(1, 64, 184, 184) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%361, %backbone.layer1.1.conv1.weight) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/modules/conv.py:346:0
  %363 : Float(1, 64, 184, 184) = onnx::BatchNormalization[epsilon=1.0000000000000001e-05, momentum=0.90000000000000002](%362, %backbone.layer1.1.bn1.weight, %backbone.layer1.1.bn1.bias, %backbone.layer1.1.bn1.running_mean, %backbone.layer1.1.bn1.running_var) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/functional.py:1923:0
  %364 : Float(1, 64, 184, 184) = onnx::Relu(%363) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/functional.py:1061:0
  %365 : Float(1, 64, 184, 184) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1]](%364, %backbone.layer1.1.conv2.weight) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/modules/conv.py:346:0
  %366 : Float(1, 64, 184, 184) = onnx::BatchNormalization[epsilon=1.0000000000000001e-05, momentum=0.90000000000000002](%365, %backbone.layer1.1.bn2.weight, %backbone.layer1.1.bn2.bias, %backbone.layer1.1.bn2.running_mean, %backbone.layer1.1.bn2.running_var) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/functional.py:1923:0
  %367 : Float(1, 64, 184, 184) = onnx::Relu(%366) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/functional.py:1061:0
  %368 : Float(1, 256, 184, 184) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%367, %backbone.layer1.1.conv3.weight) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/modules/conv.py:346:0
  %369 : Float(1, 256, 184, 184) = onnx::BatchNormalization[epsilon=1.0000000000000001e-05, momentum=0.90000000000000002](%368, %backbone.layer1.1.bn3.weight, %backbone.layer1.1.bn3.bias, %backbone.layer1.1.bn3.running_mean, %backbone.layer1.1.bn3.running_var) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/functional.py:1923:0
  %370 : Float(1, 256, 184, 184) = onnx::Add(%369, %361)
  %371 : Float(1, 256, 184, 184) = onnx::Relu(%370) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/functional.py:1061:0
  %372 : Float(1, 64, 184, 184) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%371, %backbone.layer1.2.conv1.weight) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/modules/conv.py:346:0
  %373 : Float(1, 64, 184, 184) = onnx::BatchNormalization[epsilon=1.0000000000000001e-05, momentum=0.90000000000000002](%372, %backbone.layer1.2.bn1.weight, %backbone.layer1.2.bn1.bias, %backbone.layer1.2.bn1.running_mean, %backbone.layer1.2.bn1.running_var) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/functional.py:1923:0
  %374 : Float(1, 64, 184, 184) = onnx::Relu(%373) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/functional.py:1061:0
  %375 : Float(1, 64, 184, 184) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1]](%374, %backbone.layer1.2.conv2.weight) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/modules/conv.py:346:0
  %376 : Float(1, 64, 184, 184) = onnx::BatchNormalization[epsilon=1.0000000000000001e-05, momentum=0.90000000000000002](%375, %backbone.layer1.2.bn2.weight, %backbone.layer1.2.bn2.bias, %backbone.layer1.2.bn2.running_mean, %backbone.layer1.2.bn2.running_var) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/functional.py:1923:0
  %377 : Float(1, 64, 184, 184) = onnx::Relu(%376) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/functional.py:1061:0
  %378 : Float(1, 256, 184, 184) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%377, %backbone.layer1.2.conv3.weight) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/modules/conv.py:346:0
  %379 : Float(1, 256, 184, 184) = onnx::BatchNormalization[epsilon=1.0000000000000001e-05, momentum=0.90000000000000002](%378, %backbone.layer1.2.bn3.weight, %backbone.layer1.2.bn3.bias, %backbone.layer1.2.bn3.running_mean, %backbone.layer1.2.bn3.running_var) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/functional.py:1923:0
  %380 : Float(1, 256, 184, 184) = onnx::Add(%379, %371)
  %381 : Float(1, 256, 184, 184) = onnx::Relu(%380) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/functional.py:1061:0
  %382 : Float(1, 128, 184, 184) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%381, %backbone.layer2.0.conv1.weight) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/modules/conv.py:346:0
  %383 : Float(1, 128, 184, 184) = onnx::BatchNormalization[epsilon=1.0000000000000001e-05, momentum=0.90000000000000002](%382, %backbone.layer2.0.bn1.weight, %backbone.layer2.0.bn1.bias, %backbone.layer2.0.bn1.running_mean, %backbone.layer2.0.bn1.running_var) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/functional.py:1923:0
  %384 : Float(1, 128, 184, 184) = onnx::Relu(%383) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/functional.py:1061:0
  %385 : Float(1, 128, 92, 92) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[2, 2]](%384, %backbone.layer2.0.conv2.weight) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/modules/conv.py:346:0
  %386 : Float(1, 128, 92, 92) = onnx::BatchNormalization[epsilon=1.0000000000000001e-05, momentum=0.90000000000000002](%385, %backbone.layer2.0.bn2.weight, %backbone.layer2.0.bn2.bias, %backbone.layer2.0.bn2.running_mean, %backbone.layer2.0.bn2.running_var) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/functional.py:1923:0
  %387 : Float(1, 128, 92, 92) = onnx::Relu(%386) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/functional.py:1061:0
  %388 : Float(1, 512, 92, 92) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%387, %backbone.layer2.0.conv3.weight) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/modules/conv.py:346:0
  %389 : Float(1, 512, 92, 92) = onnx::BatchNormalization[epsilon=1.0000000000000001e-05, momentum=0.90000000000000002](%388, %backbone.layer2.0.bn3.weight, %backbone.layer2.0.bn3.bias, %backbone.layer2.0.bn3.running_mean, %backbone.layer2.0.bn3.running_var) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/functional.py:1923:0
  %390 : Float(1, 512, 92, 92) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[2, 2]](%381, %backbone.layer2.0.downsample.0.weight) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/modules/conv.py:346:0
  %391 : Float(1, 512, 92, 92) = onnx::BatchNormalization[epsilon=1.0000000000000001e-05, momentum=0.90000000000000002](%390, %backbone.layer2.0.downsample.1.weight, %backbone.layer2.0.downsample.1.bias, %backbone.layer2.0.downsample.1.running_mean, %backbone.layer2.0.downsample.1.running_var) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/functional.py:1923:0
  %392 : Float(1, 512, 92, 92) = onnx::Add(%389, %391)
  %393 : Float(1, 512, 92, 92) = onnx::Relu(%392) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/functional.py:1061:0
  %394 : Float(1, 128, 92, 92) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%393, %backbone.layer2.1.conv1.weight) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/modules/conv.py:346:0
  %395 : Float(1, 128, 92, 92) = onnx::BatchNormalization[epsilon=1.0000000000000001e-05, momentum=0.90000000000000002](%394, %backbone.layer2.1.bn1.weight, %backbone.layer2.1.bn1.bias, %backbone.layer2.1.bn1.running_mean, %backbone.layer2.1.bn1.running_var) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/functional.py:1923:0
  %396 : Float(1, 128, 92, 92) = onnx::Relu(%395) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/functional.py:1061:0
  %397 : Float(1, 128, 92, 92) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1]](%396, %backbone.layer2.1.conv2.weight) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/modules/conv.py:346:0
  %398 : Float(1, 128, 92, 92) = onnx::BatchNormalization[epsilon=1.0000000000000001e-05, momentum=0.90000000000000002](%397, %backbone.layer2.1.bn2.weight, %backbone.layer2.1.bn2.bias, %backbone.layer2.1.bn2.running_mean, %backbone.layer2.1.bn2.running_var) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/functional.py:1923:0
  %399 : Float(1, 128, 92, 92) = onnx::Relu(%398) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/functional.py:1061:0
  %400 : Float(1, 512, 92, 92) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%399, %backbone.layer2.1.conv3.weight) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/modules/conv.py:346:0
  %401 : Float(1, 512, 92, 92) = onnx::BatchNormalization[epsilon=1.0000000000000001e-05, momentum=0.90000000000000002](%400, %backbone.layer2.1.bn3.weight, %backbone.layer2.1.bn3.bias, %backbone.layer2.1.bn3.running_mean, %backbone.layer2.1.bn3.running_var) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/functional.py:1923:0
  %402 : Float(1, 512, 92, 92) = onnx::Add(%401, %393)
  %403 : Float(1, 512, 92, 92) = onnx::Relu(%402) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/functional.py:1061:0
  %404 : Float(1, 128, 92, 92) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%403, %backbone.layer2.2.conv1.weight) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/modules/conv.py:346:0
  %405 : Float(1, 128, 92, 92) = onnx::BatchNormalization[epsilon=1.0000000000000001e-05, momentum=0.90000000000000002](%404, %backbone.layer2.2.bn1.weight, %backbone.layer2.2.bn1.bias, %backbone.layer2.2.bn1.running_mean, %backbone.layer2.2.bn1.running_var) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/functional.py:1923:0
  %406 : Float(1, 128, 92, 92) = onnx::Relu(%405) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/functional.py:1061:0
  %407 : Float(1, 128, 92, 92) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1]](%406, %backbone.layer2.2.conv2.weight) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/modules/conv.py:346:0
  %408 : Float(1, 128, 92, 92) = onnx::BatchNormalization[epsilon=1.0000000000000001e-05, momentum=0.90000000000000002](%407, %backbone.layer2.2.bn2.weight, %backbone.layer2.2.bn2.bias, %backbone.layer2.2.bn2.running_mean, %backbone.layer2.2.bn2.running_var) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/functional.py:1923:0
  %409 : Float(1, 128, 92, 92) = onnx::Relu(%408) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/functional.py:1061:0
  %410 : Float(1, 512, 92, 92) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%409, %backbone.layer2.2.conv3.weight) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/modules/conv.py:346:0
  %411 : Float(1, 512, 92, 92) = onnx::BatchNormalization[epsilon=1.0000000000000001e-05, momentum=0.90000000000000002](%410, %backbone.layer2.2.bn3.weight, %backbone.layer2.2.bn3.bias, %backbone.layer2.2.bn3.running_mean, %backbone.layer2.2.bn3.running_var) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/functional.py:1923:0
  %412 : Float(1, 512, 92, 92) = onnx::Add(%411, %403)
  %413 : Float(1, 512, 92, 92) = onnx::Relu(%412) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/functional.py:1061:0
  %414 : Float(1, 128, 92, 92) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%413, %backbone.layer2.3.conv1.weight) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/modules/conv.py:346:0
  %415 : Float(1, 128, 92, 92) = onnx::BatchNormalization[epsilon=1.0000000000000001e-05, momentum=0.90000000000000002](%414, %backbone.layer2.3.bn1.weight, %backbone.layer2.3.bn1.bias, %backbone.layer2.3.bn1.running_mean, %backbone.layer2.3.bn1.running_var) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/functional.py:1923:0
  %416 : Float(1, 128, 92, 92) = onnx::Relu(%415) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/functional.py:1061:0
  %417 : Float(1, 128, 92, 92) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1]](%416, %backbone.layer2.3.conv2.weight) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/modules/conv.py:346:0
  %418 : Float(1, 128, 92, 92) = onnx::BatchNormalization[epsilon=1.0000000000000001e-05, momentum=0.90000000000000002](%417, %backbone.layer2.3.bn2.weight, %backbone.layer2.3.bn2.bias, %backbone.layer2.3.bn2.running_mean, %backbone.layer2.3.bn2.running_var) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/functional.py:1923:0
  %419 : Float(1, 128, 92, 92) = onnx::Relu(%418) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/functional.py:1061:0
  %420 : Float(1, 512, 92, 92) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%419, %backbone.layer2.3.conv3.weight) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/modules/conv.py:346:0
  %421 : Float(1, 512, 92, 92) = onnx::BatchNormalization[epsilon=1.0000000000000001e-05, momentum=0.90000000000000002](%420, %backbone.layer2.3.bn3.weight, %backbone.layer2.3.bn3.bias, %backbone.layer2.3.bn3.running_mean, %backbone.layer2.3.bn3.running_var) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/functional.py:1923:0
  %422 : Float(1, 512, 92, 92) = onnx::Add(%421, %413)
  %423 : Float(1, 512, 92, 92) = onnx::Relu(%422) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/functional.py:1061:0
  %424 : Float(1, 256, 92, 92) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%423, %backbone.layer3.0.conv1.weight) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/modules/conv.py:346:0
  %425 : Float(1, 256, 92, 92) = onnx::BatchNormalization[epsilon=1.0000000000000001e-05, momentum=0.90000000000000002](%424, %backbone.layer3.0.bn1.weight, %backbone.layer3.0.bn1.bias, %backbone.layer3.0.bn1.running_mean, %backbone.layer3.0.bn1.running_var) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/functional.py:1923:0
  %426 : Float(1, 256, 92, 92) = onnx::Relu(%425) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/functional.py:1061:0
  %427 : Float(1, 256, 46, 46) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[2, 2]](%426, %backbone.layer3.0.conv2.weight) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/modules/conv.py:346:0
  %428 : Float(1, 256, 46, 46) = onnx::BatchNormalization[epsilon=1.0000000000000001e-05, momentum=0.90000000000000002](%427, %backbone.layer3.0.bn2.weight, %backbone.layer3.0.bn2.bias, %backbone.layer3.0.bn2.running_mean, %backbone.layer3.0.bn2.running_var) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/functional.py:1923:0
  %429 : Float(1, 256, 46, 46) = onnx::Relu(%428) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/functional.py:1061:0
  %430 : Float(1, 1024, 46, 46) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%429, %backbone.layer3.0.conv3.weight) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/modules/conv.py:346:0
  %431 : Float(1, 1024, 46, 46) = onnx::BatchNormalization[epsilon=1.0000000000000001e-05, momentum=0.90000000000000002](%430, %backbone.layer3.0.bn3.weight, %backbone.layer3.0.bn3.bias, %backbone.layer3.0.bn3.running_mean, %backbone.layer3.0.bn3.running_var) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/functional.py:1923:0
  %432 : Float(1, 1024, 46, 46) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[2, 2]](%423, %backbone.layer3.0.downsample.0.weight) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/modules/conv.py:346:0
  %433 : Float(1, 1024, 46, 46) = onnx::BatchNormalization[epsilon=1.0000000000000001e-05, momentum=0.90000000000000002](%432, %backbone.layer3.0.downsample.1.weight, %backbone.layer3.0.downsample.1.bias, %backbone.layer3.0.downsample.1.running_mean, %backbone.layer3.0.downsample.1.running_var) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/functional.py:1923:0
  %434 : Float(1, 1024, 46, 46) = onnx::Add(%431, %433)
  %435 : Float(1, 1024, 46, 46) = onnx::Relu(%434) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/functional.py:1061:0
  %436 : Float(1, 256, 46, 46) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%435, %backbone.layer3.1.conv1.weight) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/modules/conv.py:346:0
  %437 : Float(1, 256, 46, 46) = onnx::BatchNormalization[epsilon=1.0000000000000001e-05, momentum=0.90000000000000002](%436, %backbone.layer3.1.bn1.weight, %backbone.layer3.1.bn1.bias, %backbone.layer3.1.bn1.running_mean, %backbone.layer3.1.bn1.running_var) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/functional.py:1923:0
  %438 : Float(1, 256, 46, 46) = onnx::Relu(%437) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/functional.py:1061:0
  %439 : Float(1, 256, 46, 46) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1]](%438, %backbone.layer3.1.conv2.weight) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/modules/conv.py:346:0
  %440 : Float(1, 256, 46, 46) = onnx::BatchNormalization[epsilon=1.0000000000000001e-05, momentum=0.90000000000000002](%439, %backbone.layer3.1.bn2.weight, %backbone.layer3.1.bn2.bias, %backbone.layer3.1.bn2.running_mean, %backbone.layer3.1.bn2.running_var) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/functional.py:1923:0
  %441 : Float(1, 256, 46, 46) = onnx::Relu(%440) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/functional.py:1061:0
  %442 : Float(1, 1024, 46, 46) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%441, %backbone.layer3.1.conv3.weight) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/modules/conv.py:346:0
  %443 : Float(1, 1024, 46, 46) = onnx::BatchNormalization[epsilon=1.0000000000000001e-05, momentum=0.90000000000000002](%442, %backbone.layer3.1.bn3.weight, %backbone.layer3.1.bn3.bias, %backbone.layer3.1.bn3.running_mean, %backbone.layer3.1.bn3.running_var) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/functional.py:1923:0
  %444 : Float(1, 1024, 46, 46) = onnx::Add(%443, %435)
  %445 : Float(1, 1024, 46, 46) = onnx::Relu(%444) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/functional.py:1061:0
  %446 : Float(1, 256, 46, 46) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%445, %backbone.layer3.2.conv1.weight) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/modules/conv.py:346:0
  %447 : Float(1, 256, 46, 46) = onnx::BatchNormalization[epsilon=1.0000000000000001e-05, momentum=0.90000000000000002](%446, %backbone.layer3.2.bn1.weight, %backbone.layer3.2.bn1.bias, %backbone.layer3.2.bn1.running_mean, %backbone.layer3.2.bn1.running_var) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/functional.py:1923:0
  %448 : Float(1, 256, 46, 46) = onnx::Relu(%447) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/functional.py:1061:0
  %449 : Float(1, 256, 46, 46) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1]](%448, %backbone.layer3.2.conv2.weight) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/modules/conv.py:346:0
  %450 : Float(1, 256, 46, 46) = onnx::BatchNormalization[epsilon=1.0000000000000001e-05, momentum=0.90000000000000002](%449, %backbone.layer3.2.bn2.weight, %backbone.layer3.2.bn2.bias, %backbone.layer3.2.bn2.running_mean, %backbone.layer3.2.bn2.running_var) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/functional.py:1923:0
  %451 : Float(1, 256, 46, 46) = onnx::Relu(%450) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/functional.py:1061:0
  %452 : Float(1, 1024, 46, 46) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%451, %backbone.layer3.2.conv3.weight) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/modules/conv.py:346:0
  %453 : Float(1, 1024, 46, 46) = onnx::BatchNormalization[epsilon=1.0000000000000001e-05, momentum=0.90000000000000002](%452, %backbone.layer3.2.bn3.weight, %backbone.layer3.2.bn3.bias, %backbone.layer3.2.bn3.running_mean, %backbone.layer3.2.bn3.running_var) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/functional.py:1923:0
  %454 : Float(1, 1024, 46, 46) = onnx::Add(%453, %445)
  %455 : Float(1, 1024, 46, 46) = onnx::Relu(%454) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/functional.py:1061:0
  %456 : Float(1, 256, 46, 46) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%455, %backbone.layer3.3.conv1.weight) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/modules/conv.py:346:0
  %457 : Float(1, 256, 46, 46) = onnx::BatchNormalization[epsilon=1.0000000000000001e-05, momentum=0.90000000000000002](%456, %backbone.layer3.3.bn1.weight, %backbone.layer3.3.bn1.bias, %backbone.layer3.3.bn1.running_mean, %backbone.layer3.3.bn1.running_var) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/functional.py:1923:0
  %458 : Float(1, 256, 46, 46) = onnx::Relu(%457) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/functional.py:1061:0
  %459 : Float(1, 256, 46, 46) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1]](%458, %backbone.layer3.3.conv2.weight) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/modules/conv.py:346:0
  %460 : Float(1, 256, 46, 46) = onnx::BatchNormalization[epsilon=1.0000000000000001e-05, momentum=0.90000000000000002](%459, %backbone.layer3.3.bn2.weight, %backbone.layer3.3.bn2.bias, %backbone.layer3.3.bn2.running_mean, %backbone.layer3.3.bn2.running_var) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/functional.py:1923:0
  %461 : Float(1, 256, 46, 46) = onnx::Relu(%460) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/functional.py:1061:0
  %462 : Float(1, 1024, 46, 46) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%461, %backbone.layer3.3.conv3.weight) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/modules/conv.py:346:0
  %463 : Float(1, 1024, 46, 46) = onnx::BatchNormalization[epsilon=1.0000000000000001e-05, momentum=0.90000000000000002](%462, %backbone.layer3.3.bn3.weight, %backbone.layer3.3.bn3.bias, %backbone.layer3.3.bn3.running_mean, %backbone.layer3.3.bn3.running_var) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/functional.py:1923:0
  %464 : Float(1, 1024, 46, 46) = onnx::Add(%463, %455)
  %465 : Float(1, 1024, 46, 46) = onnx::Relu(%464) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/functional.py:1061:0
  %466 : Float(1, 256, 46, 46) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%465, %backbone.layer3.4.conv1.weight) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/modules/conv.py:346:0
  %467 : Float(1, 256, 46, 46) = onnx::BatchNormalization[epsilon=1.0000000000000001e-05, momentum=0.90000000000000002](%466, %backbone.layer3.4.bn1.weight, %backbone.layer3.4.bn1.bias, %backbone.layer3.4.bn1.running_mean, %backbone.layer3.4.bn1.running_var) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/functional.py:1923:0
  %468 : Float(1, 256, 46, 46) = onnx::Relu(%467) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/functional.py:1061:0
  %469 : Float(1, 256, 46, 46) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1]](%468, %backbone.layer3.4.conv2.weight) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/modules/conv.py:346:0
  %470 : Float(1, 256, 46, 46) = onnx::BatchNormalization[epsilon=1.0000000000000001e-05, momentum=0.90000000000000002](%469, %backbone.layer3.4.bn2.weight, %backbone.layer3.4.bn2.bias, %backbone.layer3.4.bn2.running_mean, %backbone.layer3.4.bn2.running_var) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/functional.py:1923:0
  %471 : Float(1, 256, 46, 46) = onnx::Relu(%470) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/functional.py:1061:0
  %472 : Float(1, 1024, 46, 46) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%471, %backbone.layer3.4.conv3.weight) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/modules/conv.py:346:0
  %473 : Float(1, 1024, 46, 46) = onnx::BatchNormalization[epsilon=1.0000000000000001e-05, momentum=0.90000000000000002](%472, %backbone.layer3.4.bn3.weight, %backbone.layer3.4.bn3.bias, %backbone.layer3.4.bn3.running_mean, %backbone.layer3.4.bn3.running_var) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/functional.py:1923:0
  %474 : Float(1, 1024, 46, 46) = onnx::Add(%473, %465)
  %475 : Float(1, 1024, 46, 46) = onnx::Relu(%474) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/functional.py:1061:0
  %476 : Float(1, 256, 46, 46) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%475, %backbone.layer3.5.conv1.weight) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/modules/conv.py:346:0
  %477 : Float(1, 256, 46, 46) = onnx::BatchNormalization[epsilon=1.0000000000000001e-05, momentum=0.90000000000000002](%476, %backbone.layer3.5.bn1.weight, %backbone.layer3.5.bn1.bias, %backbone.layer3.5.bn1.running_mean, %backbone.layer3.5.bn1.running_var) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/functional.py:1923:0
  %478 : Float(1, 256, 46, 46) = onnx::Relu(%477) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/functional.py:1061:0
  %479 : Float(1, 256, 46, 46) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1]](%478, %backbone.layer3.5.conv2.weight) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/modules/conv.py:346:0
  %480 : Float(1, 256, 46, 46) = onnx::BatchNormalization[epsilon=1.0000000000000001e-05, momentum=0.90000000000000002](%479, %backbone.layer3.5.bn2.weight, %backbone.layer3.5.bn2.bias, %backbone.layer3.5.bn2.running_mean, %backbone.layer3.5.bn2.running_var) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/functional.py:1923:0
  %481 : Float(1, 256, 46, 46) = onnx::Relu(%480) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/functional.py:1061:0
  %482 : Float(1, 1024, 46, 46) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%481, %backbone.layer3.5.conv3.weight) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/modules/conv.py:346:0
  %483 : Float(1, 1024, 46, 46) = onnx::BatchNormalization[epsilon=1.0000000000000001e-05, momentum=0.90000000000000002](%482, %backbone.layer3.5.bn3.weight, %backbone.layer3.5.bn3.bias, %backbone.layer3.5.bn3.running_mean, %backbone.layer3.5.bn3.running_var) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/functional.py:1923:0
  %484 : Float(1, 1024, 46, 46) = onnx::Add(%483, %475)
  %485 : Float(1, 1024, 46, 46) = onnx::Relu(%484) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/functional.py:1061:0
  %486 : Float(1, 512, 46, 46) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%485, %backbone.layer4.0.conv1.weight) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/modules/conv.py:346:0
  %487 : Float(1, 512, 46, 46) = onnx::BatchNormalization[epsilon=1.0000000000000001e-05, momentum=0.90000000000000002](%486, %backbone.layer4.0.bn1.weight, %backbone.layer4.0.bn1.bias, %backbone.layer4.0.bn1.running_mean, %backbone.layer4.0.bn1.running_var) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/functional.py:1923:0
  %488 : Float(1, 512, 46, 46) = onnx::Relu(%487) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/functional.py:1061:0
  %489 : Float(1, 512, 23, 23) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[2, 2]](%488, %backbone.layer4.0.conv2.weight) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/modules/conv.py:346:0
  %490 : Float(1, 512, 23, 23) = onnx::BatchNormalization[epsilon=1.0000000000000001e-05, momentum=0.90000000000000002](%489, %backbone.layer4.0.bn2.weight, %backbone.layer4.0.bn2.bias, %backbone.layer4.0.bn2.running_mean, %backbone.layer4.0.bn2.running_var) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/functional.py:1923:0
  %491 : Float(1, 512, 23, 23) = onnx::Relu(%490) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/functional.py:1061:0
  %492 : Float(1, 2048, 23, 23) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%491, %backbone.layer4.0.conv3.weight) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/modules/conv.py:346:0
  %493 : Float(1, 2048, 23, 23) = onnx::BatchNormalization[epsilon=1.0000000000000001e-05, momentum=0.90000000000000002](%492, %backbone.layer4.0.bn3.weight, %backbone.layer4.0.bn3.bias, %backbone.layer4.0.bn3.running_mean, %backbone.layer4.0.bn3.running_var) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/functional.py:1923:0
  %494 : Float(1, 2048, 23, 23) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[2, 2]](%485, %backbone.layer4.0.downsample.0.weight) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/modules/conv.py:346:0
  %495 : Float(1, 2048, 23, 23) = onnx::BatchNormalization[epsilon=1.0000000000000001e-05, momentum=0.90000000000000002](%494, %backbone.layer4.0.downsample.1.weight, %backbone.layer4.0.downsample.1.bias, %backbone.layer4.0.downsample.1.running_mean, %backbone.layer4.0.downsample.1.running_var) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/functional.py:1923:0
  %496 : Float(1, 2048, 23, 23) = onnx::Add(%493, %495)
  %497 : Float(1, 2048, 23, 23) = onnx::Relu(%496) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/functional.py:1061:0
  %498 : Float(1, 512, 23, 23) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%497, %backbone.layer4.1.conv1.weight) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/modules/conv.py:346:0
  %499 : Float(1, 512, 23, 23) = onnx::BatchNormalization[epsilon=1.0000000000000001e-05, momentum=0.90000000000000002](%498, %backbone.layer4.1.bn1.weight, %backbone.layer4.1.bn1.bias, %backbone.layer4.1.bn1.running_mean, %backbone.layer4.1.bn1.running_var) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/functional.py:1923:0
  %500 : Float(1, 512, 23, 23) = onnx::Relu(%499) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/functional.py:1061:0
  %501 : Float(1, 512, 23, 23) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1]](%500, %backbone.layer4.1.conv2.weight) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/modules/conv.py:346:0
  %502 : Float(1, 512, 23, 23) = onnx::BatchNormalization[epsilon=1.0000000000000001e-05, momentum=0.90000000000000002](%501, %backbone.layer4.1.bn2.weight, %backbone.layer4.1.bn2.bias, %backbone.layer4.1.bn2.running_mean, %backbone.layer4.1.bn2.running_var) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/functional.py:1923:0
  %503 : Float(1, 512, 23, 23) = onnx::Relu(%502) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/functional.py:1061:0
  %504 : Float(1, 2048, 23, 23) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%503, %backbone.layer4.1.conv3.weight) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/modules/conv.py:346:0
  %505 : Float(1, 2048, 23, 23) = onnx::BatchNormalization[epsilon=1.0000000000000001e-05, momentum=0.90000000000000002](%504, %backbone.layer4.1.bn3.weight, %backbone.layer4.1.bn3.bias, %backbone.layer4.1.bn3.running_mean, %backbone.layer4.1.bn3.running_var) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/functional.py:1923:0
  %506 : Float(1, 2048, 23, 23) = onnx::Add(%505, %497)
  %507 : Float(1, 2048, 23, 23) = onnx::Relu(%506) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/functional.py:1061:0
  %508 : Float(1, 512, 23, 23) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%507, %backbone.layer4.2.conv1.weight) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/modules/conv.py:346:0
  %509 : Float(1, 512, 23, 23) = onnx::BatchNormalization[epsilon=1.0000000000000001e-05, momentum=0.90000000000000002](%508, %backbone.layer4.2.bn1.weight, %backbone.layer4.2.bn1.bias, %backbone.layer4.2.bn1.running_mean, %backbone.layer4.2.bn1.running_var) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/functional.py:1923:0
  %510 : Float(1, 512, 23, 23) = onnx::Relu(%509) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/functional.py:1061:0
  %511 : Float(1, 512, 23, 23) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1]](%510, %backbone.layer4.2.conv2.weight) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/modules/conv.py:346:0
  %512 : Float(1, 512, 23, 23) = onnx::BatchNormalization[epsilon=1.0000000000000001e-05, momentum=0.90000000000000002](%511, %backbone.layer4.2.bn2.weight, %backbone.layer4.2.bn2.bias, %backbone.layer4.2.bn2.running_mean, %backbone.layer4.2.bn2.running_var) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/functional.py:1923:0
  %513 : Float(1, 512, 23, 23) = onnx::Relu(%512) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/functional.py:1061:0
  %514 : Float(1, 2048, 23, 23) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%513, %backbone.layer4.2.conv3.weight) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/modules/conv.py:346:0
  %515 : Float(1, 2048, 23, 23) = onnx::BatchNormalization[epsilon=1.0000000000000001e-05, momentum=0.90000000000000002](%514, %backbone.layer4.2.bn3.weight, %backbone.layer4.2.bn3.bias, %backbone.layer4.2.bn3.running_mean, %backbone.layer4.2.bn3.running_var) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/functional.py:1923:0
  %516 : Float(1, 2048, 23, 23) = onnx::Add(%515, %507)
  %517 : Float(1, 2048, 23, 23) = onnx::Relu(%516) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/functional.py:1061:0
  %518 : Float(1, 256, 23, 23) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%517, %decoder.in5.weight) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/modules/conv.py:346:0
  %519 : Float(1, 256, 46, 46) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%485, %decoder.in4.weight) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/modules/conv.py:346:0
  %520 : Float(1, 256, 92, 92) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%423, %decoder.in3.weight) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/modules/conv.py:346:0
  %521 : Float(1, 256, 184, 184) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%381, %decoder.in2.weight) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/modules/conv.py:346:0
  %522 : Tensor = onnx::Shape(%518)
  %523 : Tensor = onnx::Constant[value={2}]()
  %524 : Long() = onnx::Gather[axis=0](%522, %523) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/functional.py:2864:0
  %525 : Float() = onnx::Cast[to=1](%524) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/functional.py:2864:0
  %526 : Float() = onnx::Constant[value={2}]()
  %527 : Float() = onnx::Mul(%525, %526)
  %528 : Float() = onnx::Cast[to=1](%527) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/functional.py:2864:0
  %529 : Float() = onnx::Floor(%528) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/functional.py:2864:0
  %530 : Tensor = onnx::Shape(%518)
  %531 : Tensor = onnx::Constant[value={3}]()
  %532 : Long() = onnx::Gather[axis=0](%530, %531) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/functional.py:2864:0
  %533 : Float() = onnx::Cast[to=1](%532) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/functional.py:2864:0
  %534 : Float() = onnx::Constant[value={2}]()
  %535 : Float() = onnx::Mul(%533, %534)
  %536 : Float() = onnx::Cast[to=1](%535) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/functional.py:2864:0
  %537 : Float() = onnx::Floor(%536) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/functional.py:2864:0
  %538 : Tensor = onnx::Unsqueeze[axes=[0]](%529)
  %539 : Tensor = onnx::Unsqueeze[axes=[0]](%537)
  %540 : Tensor = onnx::Concat[axis=0](%538, %539)
  %541 : Tensor = onnx::Constant[value=[ CPUFloatType{0} ]]()
  %542 : Tensor = onnx::Shape(%518)
  %543 : Tensor = onnx::Constant[value={0}]()
  %544 : Tensor = onnx::Constant[value={0}]()
  %545 : Tensor = onnx::Constant[value={2}]()
  %546 : Tensor = onnx::Slice(%542, %544, %545, %543)
  %547 : Tensor = onnx::Cast[to=7](%540)
  %548 : Tensor = onnx::Concat[axis=0](%546, %547)
  %549 : Tensor = onnx::Constant[value=[ CPUFloatType{0} ]]()
  %550 : Float(1, 256, 46, 46) = onnx::Resize[coordinate_transformation_mode="asymmetric", cubic_coeff_a=-0.75, mode="nearest", nearest_mode="floor"](%518, %541, %549, %548) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/functional.py:2991:0
  %551 : Float(1, 256, 46, 46) = onnx::Add(%550, %519) # /home/gordon/PycharmProjects/GSoC_pytorch2onnx/DB/onnx_model.py:67:0
  %552 : Tensor = onnx::Shape(%551)
  %553 : Tensor = onnx::Constant[value={2}]()
  %554 : Long() = onnx::Gather[axis=0](%552, %553) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/functional.py:2864:0
  %555 : Float() = onnx::Cast[to=1](%554) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/functional.py:2864:0
  %556 : Float() = onnx::Constant[value={2}]()
  %557 : Float() = onnx::Mul(%555, %556)
  %558 : Float() = onnx::Cast[to=1](%557) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/functional.py:2864:0
  %559 : Float() = onnx::Floor(%558) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/functional.py:2864:0
  %560 : Tensor = onnx::Shape(%551)
  %561 : Tensor = onnx::Constant[value={3}]()
  %562 : Long() = onnx::Gather[axis=0](%560, %561) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/functional.py:2864:0
  %563 : Float() = onnx::Cast[to=1](%562) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/functional.py:2864:0
  %564 : Float() = onnx::Constant[value={2}]()
  %565 : Float() = onnx::Mul(%563, %564)
  %566 : Float() = onnx::Cast[to=1](%565) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/functional.py:2864:0
  %567 : Float() = onnx::Floor(%566) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/functional.py:2864:0
  %568 : Tensor = onnx::Unsqueeze[axes=[0]](%559)
  %569 : Tensor = onnx::Unsqueeze[axes=[0]](%567)
  %570 : Tensor = onnx::Concat[axis=0](%568, %569)
  %571 : Tensor = onnx::Constant[value=[ CPUFloatType{0} ]]()
  %572 : Tensor = onnx::Shape(%551)
  %573 : Tensor = onnx::Constant[value={0}]()
  %574 : Tensor = onnx::Constant[value={0}]()
  %575 : Tensor = onnx::Constant[value={2}]()
  %576 : Tensor = onnx::Slice(%572, %574, %575, %573)
  %577 : Tensor = onnx::Cast[to=7](%570)
  %578 : Tensor = onnx::Concat[axis=0](%576, %577)
  %579 : Tensor = onnx::Constant[value=[ CPUFloatType{0} ]]()
  %580 : Float(1, 256, 92, 92) = onnx::Resize[coordinate_transformation_mode="asymmetric", cubic_coeff_a=-0.75, mode="nearest", nearest_mode="floor"](%551, %571, %579, %578) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/functional.py:2991:0
  %581 : Float(1, 256, 92, 92) = onnx::Add(%580, %520) # /home/gordon/PycharmProjects/GSoC_pytorch2onnx/DB/onnx_model.py:68:0
  %582 : Tensor = onnx::Shape(%581)
  %583 : Tensor = onnx::Constant[value={2}]()
  %584 : Long() = onnx::Gather[axis=0](%582, %583) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/functional.py:2864:0
  %585 : Float() = onnx::Cast[to=1](%584) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/functional.py:2864:0
  %586 : Float() = onnx::Constant[value={2}]()
  %587 : Float() = onnx::Mul(%585, %586)
  %588 : Float() = onnx::Cast[to=1](%587) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/functional.py:2864:0
  %589 : Float() = onnx::Floor(%588) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/functional.py:2864:0
  %590 : Tensor = onnx::Shape(%581)
  %591 : Tensor = onnx::Constant[value={3}]()
  %592 : Long() = onnx::Gather[axis=0](%590, %591) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/functional.py:2864:0
  %593 : Float() = onnx::Cast[to=1](%592) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/functional.py:2864:0
  %594 : Float() = onnx::Constant[value={2}]()
  %595 : Float() = onnx::Mul(%593, %594)
  %596 : Float() = onnx::Cast[to=1](%595) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/functional.py:2864:0
  %597 : Float() = onnx::Floor(%596) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/functional.py:2864:0
  %598 : Tensor = onnx::Unsqueeze[axes=[0]](%589)
  %599 : Tensor = onnx::Unsqueeze[axes=[0]](%597)
  %600 : Tensor = onnx::Concat[axis=0](%598, %599)
  %601 : Tensor = onnx::Constant[value=[ CPUFloatType{0} ]]()
  %602 : Tensor = onnx::Shape(%581)
  %603 : Tensor = onnx::Constant[value={0}]()
  %604 : Tensor = onnx::Constant[value={0}]()
  %605 : Tensor = onnx::Constant[value={2}]()
  %606 : Tensor = onnx::Slice(%602, %604, %605, %603)
  %607 : Tensor = onnx::Cast[to=7](%600)
  %608 : Tensor = onnx::Concat[axis=0](%606, %607)
  %609 : Tensor = onnx::Constant[value=[ CPUFloatType{0} ]]()
  %610 : Float(1, 256, 184, 184) = onnx::Resize[coordinate_transformation_mode="asymmetric", cubic_coeff_a=-0.75, mode="nearest", nearest_mode="floor"](%581, %601, %609, %608) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/functional.py:2991:0
  %611 : Float(1, 256, 184, 184) = onnx::Add(%610, %521) # /home/gordon/PycharmProjects/GSoC_pytorch2onnx/DB/onnx_model.py:69:0
  %612 : Float(1, 64, 23, 23) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1]](%518, %decoder.out5.0.weight) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/modules/conv.py:346:0
  %613 : Tensor = onnx::Shape(%612)
  %614 : Tensor = onnx::Constant[value={2}]()
  %615 : Long() = onnx::Gather[axis=0](%613, %614) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/functional.py:2864:0
  %616 : Float() = onnx::Cast[to=1](%615) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/functional.py:2864:0
  %617 : Float() = onnx::Constant[value={8}]()
  %618 : Float() = onnx::Mul(%616, %617)
  %619 : Float() = onnx::Cast[to=1](%618) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/functional.py:2864:0
  %620 : Float() = onnx::Floor(%619) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/functional.py:2864:0
  %621 : Tensor = onnx::Shape(%612)
  %622 : Tensor = onnx::Constant[value={3}]()
  %623 : Long() = onnx::Gather[axis=0](%621, %622) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/functional.py:2864:0
  %624 : Float() = onnx::Cast[to=1](%623) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/functional.py:2864:0
  %625 : Float() = onnx::Constant[value={8}]()
  %626 : Float() = onnx::Mul(%624, %625)
  %627 : Float() = onnx::Cast[to=1](%626) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/functional.py:2864:0
  %628 : Float() = onnx::Floor(%627) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/functional.py:2864:0
  %629 : Tensor = onnx::Unsqueeze[axes=[0]](%620)
  %630 : Tensor = onnx::Unsqueeze[axes=[0]](%628)
  %631 : Tensor = onnx::Concat[axis=0](%629, %630)
  %632 : Tensor = onnx::Constant[value=[ CPUFloatType{0} ]]()
  %633 : Tensor = onnx::Shape(%612)
  %634 : Tensor = onnx::Constant[value={0}]()
  %635 : Tensor = onnx::Constant[value={0}]()
  %636 : Tensor = onnx::Constant[value={2}]()
  %637 : Tensor = onnx::Slice(%633, %635, %636, %634)
  %638 : Tensor = onnx::Cast[to=7](%631)
  %639 : Tensor = onnx::Concat[axis=0](%637, %638)
  %640 : Tensor = onnx::Constant[value=[ CPUFloatType{0} ]]()
  %641 : Float(1, 64, 184, 184) = onnx::Resize[coordinate_transformation_mode="asymmetric", cubic_coeff_a=-0.75, mode="nearest", nearest_mode="floor"](%612, %632, %640, %639) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/functional.py:2991:0
  %642 : Float(1, 64, 46, 46) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1]](%551, %decoder.out4.0.weight) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/modules/conv.py:346:0
  %643 : Tensor = onnx::Shape(%642)
  %644 : Tensor = onnx::Constant[value={2}]()
  %645 : Long() = onnx::Gather[axis=0](%643, %644) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/functional.py:2864:0
  %646 : Float() = onnx::Cast[to=1](%645) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/functional.py:2864:0
  %647 : Float() = onnx::Constant[value={4}]()
  %648 : Float() = onnx::Mul(%646, %647)
  %649 : Float() = onnx::Cast[to=1](%648) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/functional.py:2864:0
  %650 : Float() = onnx::Floor(%649) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/functional.py:2864:0
  %651 : Tensor = onnx::Shape(%642)
  %652 : Tensor = onnx::Constant[value={3}]()
  %653 : Long() = onnx::Gather[axis=0](%651, %652) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/functional.py:2864:0
  %654 : Float() = onnx::Cast[to=1](%653) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/functional.py:2864:0
  %655 : Float() = onnx::Constant[value={4}]()
  %656 : Float() = onnx::Mul(%654, %655)
  %657 : Float() = onnx::Cast[to=1](%656) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/functional.py:2864:0
  %658 : Float() = onnx::Floor(%657) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/functional.py:2864:0
  %659 : Tensor = onnx::Unsqueeze[axes=[0]](%650)
  %660 : Tensor = onnx::Unsqueeze[axes=[0]](%658)
  %661 : Tensor = onnx::Concat[axis=0](%659, %660)
  %662 : Tensor = onnx::Constant[value=[ CPUFloatType{0} ]]()
  %663 : Tensor = onnx::Shape(%642)
  %664 : Tensor = onnx::Constant[value={0}]()
  %665 : Tensor = onnx::Constant[value={0}]()
  %666 : Tensor = onnx::Constant[value={2}]()
  %667 : Tensor = onnx::Slice(%663, %665, %666, %664)
  %668 : Tensor = onnx::Cast[to=7](%661)
  %669 : Tensor = onnx::Concat[axis=0](%667, %668)
  %670 : Tensor = onnx::Constant[value=[ CPUFloatType{0} ]]()
  %671 : Float(1, 64, 184, 184) = onnx::Resize[coordinate_transformation_mode="asymmetric", cubic_coeff_a=-0.75, mode="nearest", nearest_mode="floor"](%642, %662, %670, %669) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/functional.py:2991:0
  %672 : Float(1, 64, 92, 92) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1]](%581, %decoder.out3.0.weight) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/modules/conv.py:346:0
  %673 : Tensor = onnx::Shape(%672)
  %674 : Tensor = onnx::Constant[value={2}]()
  %675 : Long() = onnx::Gather[axis=0](%673, %674) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/functional.py:2864:0
  %676 : Float() = onnx::Cast[to=1](%675) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/functional.py:2864:0
  %677 : Float() = onnx::Constant[value={2}]()
  %678 : Float() = onnx::Mul(%676, %677)
  %679 : Float() = onnx::Cast[to=1](%678) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/functional.py:2864:0
  %680 : Float() = onnx::Floor(%679) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/functional.py:2864:0
  %681 : Tensor = onnx::Shape(%672)
  %682 : Tensor = onnx::Constant[value={3}]()
  %683 : Long() = onnx::Gather[axis=0](%681, %682) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/functional.py:2864:0
  %684 : Float() = onnx::Cast[to=1](%683) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/functional.py:2864:0
  %685 : Float() = onnx::Constant[value={2}]()
  %686 : Float() = onnx::Mul(%684, %685)
  %687 : Float() = onnx::Cast[to=1](%686) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/functional.py:2864:0
  %688 : Float() = onnx::Floor(%687) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/functional.py:2864:0
  %689 : Tensor = onnx::Unsqueeze[axes=[0]](%680)
  %690 : Tensor = onnx::Unsqueeze[axes=[0]](%688)
  %691 : Tensor = onnx::Concat[axis=0](%689, %690)
  %692 : Tensor = onnx::Constant[value=[ CPUFloatType{0} ]]()
  %693 : Tensor = onnx::Shape(%672)
  %694 : Tensor = onnx::Constant[value={0}]()
  %695 : Tensor = onnx::Constant[value={0}]()
  %696 : Tensor = onnx::Constant[value={2}]()
  %697 : Tensor = onnx::Slice(%693, %695, %696, %694)
  %698 : Tensor = onnx::Cast[to=7](%691)
  %699 : Tensor = onnx::Concat[axis=0](%697, %698)
  %700 : Tensor = onnx::Constant[value=[ CPUFloatType{0} ]]()
  %701 : Float(1, 64, 184, 184) = onnx::Resize[coordinate_transformation_mode="asymmetric", cubic_coeff_a=-0.75, mode="nearest", nearest_mode="floor"](%672, %692, %700, %699) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/functional.py:2991:0
  %702 : Float(1, 64, 184, 184) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1]](%611, %decoder.out2.weight) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/modules/conv.py:346:0
  %703 : Float(1, 256, 184, 184) = onnx::Concat[axis=1](%641, %671, %701, %702) # /home/gordon/PycharmProjects/GSoC_pytorch2onnx/DB/onnx_model.py:76:0
  %704 : Float(1, 64, 184, 184) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1]](%703, %decoder.binarize.0.weight) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/modules/conv.py:346:0
  %705 : Float(1, 64, 184, 184) = onnx::BatchNormalization[epsilon=1.0000000000000001e-05, momentum=0.90000000000000002](%704, %decoder.binarize.1.weight, %decoder.binarize.1.bias, %decoder.binarize.1.running_mean, %decoder.binarize.1.running_var) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/functional.py:1923:0
  %706 : Float(1, 64, 184, 184) = onnx::Relu(%705) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/functional.py:1061:0
  %707 : Float(1, 64, 368, 368) = onnx::ConvTranspose[dilations=[1, 1], group=1, kernel_shape=[2, 2], pads=[0, 0, 0, 0], strides=[2, 2]](%706, %decoder.binarize.3.weight, %decoder.binarize.3.bias) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/modules/conv.py:790:0
  %708 : Float(1, 64, 368, 368) = onnx::BatchNormalization[epsilon=1.0000000000000001e-05, momentum=0.90000000000000002](%707, %decoder.binarize.4.weight, %decoder.binarize.4.bias, %decoder.binarize.4.running_mean, %decoder.binarize.4.running_var) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/functional.py:1923:0
  %709 : Float(1, 64, 368, 368) = onnx::Relu(%708) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/functional.py:1061:0
  %710 : Float(1, 1, 736, 736) = onnx::ConvTranspose[dilations=[1, 1], group=1, kernel_shape=[2, 2], pads=[0, 0, 0, 0], strides=[2, 2]](%709, %decoder.binarize.6.weight, %decoder.binarize.6.bias) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/modules/conv.py:790:0
  %711 : Float(1, 1, 736, 736) = onnx::Sigmoid(%710) # /home/gordon/miniconda3/envs/onnx/lib/python3.6/site-packages/torch/nn/modules/activation.py:271:0
  %712 : Float(1, 736, 736) = onnx::Squeeze[axes=[0]](%711) # /home/gordon/PycharmProjects/GSoC_pytorch2onnx/DB/onnx_model.py:94:0
  %out : Float(736, 736) = onnx::Squeeze[axes=[0]](%712) # /home/gordon/PycharmProjects/GSoC_pytorch2onnx/DB/onnx_model.py:94:0
  return (%out)
```
</details>

- Graph of `DB_pytorch1_7.onnx`:

<details>

```
==> Exporting model to ONNX format at 'DB_pytorch1_7.onnx'
graph(%input : Float(1:1625088, 3:541696, 736:736, 736:1, requires_grad=0, device=cpu),
      %decoder.in5.weight : Float(256:2048, 2048:1, 1:1, 1:1, requires_grad=1, device=cpu),
      %decoder.in4.weight : Float(256:1024, 1024:1, 1:1, 1:1, requires_grad=1, device=cpu),
      %decoder.in3.weight : Float(256:512, 512:1, 1:1, 1:1, requires_grad=1, device=cpu),
      %decoder.in2.weight : Float(256:256, 256:1, 1:1, 1:1, requires_grad=1, device=cpu),
      %decoder.out5.0.weight : Float(64:2304, 256:9, 3:3, 3:1, requires_grad=1, device=cpu),
      %decoder.out4.0.weight : Float(64:2304, 256:9, 3:3, 3:1, requires_grad=1, device=cpu),
      %decoder.out3.0.weight : Float(64:2304, 256:9, 3:3, 3:1, requires_grad=1, device=cpu),
      %decoder.out2.weight : Float(64:2304, 256:9, 3:3, 3:1, requires_grad=1, device=cpu),
      %decoder.binarize.3.weight : Float(64:256, 64:4, 2:2, 2:1, requires_grad=1, device=cpu),
      %decoder.binarize.3.bias : Float(64:1, requires_grad=1, device=cpu),
      %decoder.binarize.4.weight : Float(64:1, requires_grad=1, device=cpu),
      %decoder.binarize.4.bias : Float(64:1, requires_grad=1, device=cpu),
      %decoder.binarize.4.running_mean : Float(64:1, requires_grad=0, device=cpu),
      %decoder.binarize.4.running_var : Float(64:1, requires_grad=0, device=cpu),
      %decoder.binarize.6.weight : Float(64:4, 1:4, 2:2, 2:1, requires_grad=1, device=cpu),
      %decoder.binarize.6.bias : Float(1:1, requires_grad=1, device=cpu),
      %585 : Float(64:147, 3:49, 7:7, 7:1, requires_grad=0, device=cpu),
      %586 : Float(64:1, requires_grad=0, device=cpu),
      %588 : Float(64:64, 64:1, 1:1, 1:1, requires_grad=0, device=cpu),
      %589 : Float(64:1, requires_grad=0, device=cpu),
      %591 : Float(64:576, 64:9, 3:3, 3:1, requires_grad=0, device=cpu),
      %592 : Float(64:1, requires_grad=0, device=cpu),
      %594 : Float(256:64, 64:1, 1:1, 1:1, requires_grad=0, device=cpu),
      %595 : Float(256:1, requires_grad=0, device=cpu),
      %597 : Float(256:64, 64:1, 1:1, 1:1, requires_grad=0, device=cpu),
      %598 : Float(256:1, requires_grad=0, device=cpu),
      %600 : Float(64:256, 256:1, 1:1, 1:1, requires_grad=0, device=cpu),
      %601 : Float(64:1, requires_grad=0, device=cpu),
      %603 : Float(64:576, 64:9, 3:3, 3:1, requires_grad=0, device=cpu),
      %604 : Float(64:1, requires_grad=0, device=cpu),
      %606 : Float(256:64, 64:1, 1:1, 1:1, requires_grad=0, device=cpu),
      %607 : Float(256:1, requires_grad=0, device=cpu),
      %609 : Float(64:256, 256:1, 1:1, 1:1, requires_grad=0, device=cpu),
      %610 : Float(64:1, requires_grad=0, device=cpu),
      %612 : Float(64:576, 64:9, 3:3, 3:1, requires_grad=0, device=cpu),
      %613 : Float(64:1, requires_grad=0, device=cpu),
      %615 : Float(256:64, 64:1, 1:1, 1:1, requires_grad=0, device=cpu),
      %616 : Float(256:1, requires_grad=0, device=cpu),
      %618 : Float(128:256, 256:1, 1:1, 1:1, requires_grad=0, device=cpu),
      %619 : Float(128:1, requires_grad=0, device=cpu),
      %621 : Float(128:1152, 128:9, 3:3, 3:1, requires_grad=0, device=cpu),
      %622 : Float(128:1, requires_grad=0, device=cpu),
      %624 : Float(512:128, 128:1, 1:1, 1:1, requires_grad=0, device=cpu),
      %625 : Float(512:1, requires_grad=0, device=cpu),
      %627 : Float(512:256, 256:1, 1:1, 1:1, requires_grad=0, device=cpu),
      %628 : Float(512:1, requires_grad=0, device=cpu),
      %630 : Float(128:512, 512:1, 1:1, 1:1, requires_grad=0, device=cpu),
      %631 : Float(128:1, requires_grad=0, device=cpu),
      %633 : Float(128:1152, 128:9, 3:3, 3:1, requires_grad=0, device=cpu),
      %634 : Float(128:1, requires_grad=0, device=cpu),
      %636 : Float(512:128, 128:1, 1:1, 1:1, requires_grad=0, device=cpu),
      %637 : Float(512:1, requires_grad=0, device=cpu),
      %639 : Float(128:512, 512:1, 1:1, 1:1, requires_grad=0, device=cpu),
      %640 : Float(128:1, requires_grad=0, device=cpu),
      %642 : Float(128:1152, 128:9, 3:3, 3:1, requires_grad=0, device=cpu),
      %643 : Float(128:1, requires_grad=0, device=cpu),
      %645 : Float(512:128, 128:1, 1:1, 1:1, requires_grad=0, device=cpu),
      %646 : Float(512:1, requires_grad=0, device=cpu),
      %648 : Float(128:512, 512:1, 1:1, 1:1, requires_grad=0, device=cpu),
      %649 : Float(128:1, requires_grad=0, device=cpu),
      %651 : Float(128:1152, 128:9, 3:3, 3:1, requires_grad=0, device=cpu),
      %652 : Float(128:1, requires_grad=0, device=cpu),
      %654 : Float(512:128, 128:1, 1:1, 1:1, requires_grad=0, device=cpu),
      %655 : Float(512:1, requires_grad=0, device=cpu),
      %657 : Float(256:512, 512:1, 1:1, 1:1, requires_grad=0, device=cpu),
      %658 : Float(256:1, requires_grad=0, device=cpu),
      %660 : Float(256:2304, 256:9, 3:3, 3:1, requires_grad=0, device=cpu),
      %661 : Float(256:1, requires_grad=0, device=cpu),
      %663 : Float(1024:256, 256:1, 1:1, 1:1, requires_grad=0, device=cpu),
      %664 : Float(1024:1, requires_grad=0, device=cpu),
      %666 : Float(1024:512, 512:1, 1:1, 1:1, requires_grad=0, device=cpu),
      %667 : Float(1024:1, requires_grad=0, device=cpu),
      %669 : Float(256:1024, 1024:1, 1:1, 1:1, requires_grad=0, device=cpu),
      %670 : Float(256:1, requires_grad=0, device=cpu),
      %672 : Float(256:2304, 256:9, 3:3, 3:1, requires_grad=0, device=cpu),
      %673 : Float(256:1, requires_grad=0, device=cpu),
      %675 : Float(1024:256, 256:1, 1:1, 1:1, requires_grad=0, device=cpu),
      %676 : Float(1024:1, requires_grad=0, device=cpu),
      %678 : Float(256:1024, 1024:1, 1:1, 1:1, requires_grad=0, device=cpu),
      %679 : Float(256:1, requires_grad=0, device=cpu),
      %681 : Float(256:2304, 256:9, 3:3, 3:1, requires_grad=0, device=cpu),
      %682 : Float(256:1, requires_grad=0, device=cpu),
      %684 : Float(1024:256, 256:1, 1:1, 1:1, requires_grad=0, device=cpu),
      %685 : Float(1024:1, requires_grad=0, device=cpu),
      %687 : Float(256:1024, 1024:1, 1:1, 1:1, requires_grad=0, device=cpu),
      %688 : Float(256:1, requires_grad=0, device=cpu),
      %690 : Float(256:2304, 256:9, 3:3, 3:1, requires_grad=0, device=cpu),
      %691 : Float(256:1, requires_grad=0, device=cpu),
      %693 : Float(1024:256, 256:1, 1:1, 1:1, requires_grad=0, device=cpu),
      %694 : Float(1024:1, requires_grad=0, device=cpu),
      %696 : Float(256:1024, 1024:1, 1:1, 1:1, requires_grad=0, device=cpu),
      %697 : Float(256:1, requires_grad=0, device=cpu),
      %699 : Float(256:2304, 256:9, 3:3, 3:1, requires_grad=0, device=cpu),
      %700 : Float(256:1, requires_grad=0, device=cpu),
      %702 : Float(1024:256, 256:1, 1:1, 1:1, requires_grad=0, device=cpu),
      %703 : Float(1024:1, requires_grad=0, device=cpu),
      %705 : Float(256:1024, 1024:1, 1:1, 1:1, requires_grad=0, device=cpu),
      %706 : Float(256:1, requires_grad=0, device=cpu),
      %708 : Float(256:2304, 256:9, 3:3, 3:1, requires_grad=0, device=cpu),
      %709 : Float(256:1, requires_grad=0, device=cpu),
      %711 : Float(1024:256, 256:1, 1:1, 1:1, requires_grad=0, device=cpu),
      %712 : Float(1024:1, requires_grad=0, device=cpu),
      %714 : Float(512:1024, 1024:1, 1:1, 1:1, requires_grad=0, device=cpu),
      %715 : Float(512:1, requires_grad=0, device=cpu),
      %717 : Float(512:4608, 512:9, 3:3, 3:1, requires_grad=0, device=cpu),
      %718 : Float(512:1, requires_grad=0, device=cpu),
      %720 : Float(2048:512, 512:1, 1:1, 1:1, requires_grad=0, device=cpu),
      %721 : Float(2048:1, requires_grad=0, device=cpu),
      %723 : Float(2048:1024, 1024:1, 1:1, 1:1, requires_grad=0, device=cpu),
      %724 : Float(2048:1, requires_grad=0, device=cpu),
      %726 : Float(512:2048, 2048:1, 1:1, 1:1, requires_grad=0, device=cpu),
      %727 : Float(512:1, requires_grad=0, device=cpu),
      %729 : Float(512:4608, 512:9, 3:3, 3:1, requires_grad=0, device=cpu),
      %730 : Float(512:1, requires_grad=0, device=cpu),
      %732 : Float(2048:512, 512:1, 1:1, 1:1, requires_grad=0, device=cpu),
      %733 : Float(2048:1, requires_grad=0, device=cpu),
      %735 : Float(512:2048, 2048:1, 1:1, 1:1, requires_grad=0, device=cpu),
      %736 : Float(512:1, requires_grad=0, device=cpu),
      %738 : Float(512:4608, 512:9, 3:3, 3:1, requires_grad=0, device=cpu),
      %739 : Float(512:1, requires_grad=0, device=cpu),
      %741 : Float(2048:512, 512:1, 1:1, 1:1, requires_grad=0, device=cpu),
      %742 : Float(2048:1, requires_grad=0, device=cpu),
      %744 : Float(64:2304, 256:9, 3:3, 3:1, requires_grad=0, device=cpu),
      %745 : Float(64:1, requires_grad=0, device=cpu),
      %746 : Float(4:1, requires_grad=0, device=cpu),
      %747 : Float(4:1, requires_grad=0, device=cpu),
      %748 : Float(4:1, requires_grad=0, device=cpu),
      %749 : Float(4:1, requires_grad=0, device=cpu),
      %750 : Float(4:1, requires_grad=0, device=cpu),
      %751 : Float(4:1, requires_grad=0, device=cpu)):
  %584 : Float(1:8667136, 64:135424, 368:368, 368:1, requires_grad=1, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[7, 7], pads=[3, 3, 3, 3], strides=[2, 2]](%input, %585, %586)
  %348 : Float(1:8667136, 64:135424, 368:368, 368:1, requires_grad=1, device=cpu) = onnx::Relu(%584) # /home/gordon/miniconda3/envs/torch/lib/python3.8/site-packages/torch/nn/functional.py:1134:0
  %349 : Float(1:2166784, 64:33856, 184:184, 184:1, requires_grad=1, device=cpu) = onnx::MaxPool[ceil_mode=0, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[2, 2]](%348) # /home/gordon/miniconda3/envs/torch/lib/python3.8/site-packages/torch/nn/functional.py:585:0
  %587 : Float(1:2166784, 64:33856, 184:184, 184:1, requires_grad=1, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%349, %588, %589)
  %352 : Float(1:2166784, 64:33856, 184:184, 184:1, requires_grad=1, device=cpu) = onnx::Relu(%587) # /home/gordon/miniconda3/envs/torch/lib/python3.8/site-packages/torch/nn/functional.py:1134:0
  %590 : Float(1:2166784, 64:33856, 184:184, 184:1, requires_grad=1, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1]](%352, %591, %592)
  %355 : Float(1:2166784, 64:33856, 184:184, 184:1, requires_grad=1, device=cpu) = onnx::Relu(%590) # /home/gordon/miniconda3/envs/torch/lib/python3.8/site-packages/torch/nn/functional.py:1134:0
  %593 : Float(1:8667136, 256:33856, 184:184, 184:1, requires_grad=1, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%355, %594, %595)
  %596 : Float(1:8667136, 256:33856, 184:184, 184:1, requires_grad=1, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%349, %597, %598)
  %360 : Float(1:8667136, 256:33856, 184:184, 184:1, requires_grad=1, device=cpu) = onnx::Add(%593, %596)
  %361 : Float(1:8667136, 256:33856, 184:184, 184:1, requires_grad=1, device=cpu) = onnx::Relu(%360) # /home/gordon/miniconda3/envs/torch/lib/python3.8/site-packages/torch/nn/functional.py:1134:0
  %599 : Float(1:2166784, 64:33856, 184:184, 184:1, requires_grad=1, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%361, %600, %601)
  %364 : Float(1:2166784, 64:33856, 184:184, 184:1, requires_grad=1, device=cpu) = onnx::Relu(%599) # /home/gordon/miniconda3/envs/torch/lib/python3.8/site-packages/torch/nn/functional.py:1134:0
  %602 : Float(1:2166784, 64:33856, 184:184, 184:1, requires_grad=1, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1]](%364, %603, %604)
  %367 : Float(1:2166784, 64:33856, 184:184, 184:1, requires_grad=1, device=cpu) = onnx::Relu(%602) # /home/gordon/miniconda3/envs/torch/lib/python3.8/site-packages/torch/nn/functional.py:1134:0
  %605 : Float(1:8667136, 256:33856, 184:184, 184:1, requires_grad=1, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%367, %606, %607)
  %370 : Float(1:8667136, 256:33856, 184:184, 184:1, requires_grad=1, device=cpu) = onnx::Add(%605, %361)
  %371 : Float(1:8667136, 256:33856, 184:184, 184:1, requires_grad=1, device=cpu) = onnx::Relu(%370) # /home/gordon/miniconda3/envs/torch/lib/python3.8/site-packages/torch/nn/functional.py:1134:0
  %608 : Float(1:2166784, 64:33856, 184:184, 184:1, requires_grad=1, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%371, %609, %610)
  %374 : Float(1:2166784, 64:33856, 184:184, 184:1, requires_grad=1, device=cpu) = onnx::Relu(%608) # /home/gordon/miniconda3/envs/torch/lib/python3.8/site-packages/torch/nn/functional.py:1134:0
  %611 : Float(1:2166784, 64:33856, 184:184, 184:1, requires_grad=1, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1]](%374, %612, %613)
  %377 : Float(1:2166784, 64:33856, 184:184, 184:1, requires_grad=1, device=cpu) = onnx::Relu(%611) # /home/gordon/miniconda3/envs/torch/lib/python3.8/site-packages/torch/nn/functional.py:1134:0
  %614 : Float(1:8667136, 256:33856, 184:184, 184:1, requires_grad=1, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%377, %615, %616)
  %380 : Float(1:8667136, 256:33856, 184:184, 184:1, requires_grad=1, device=cpu) = onnx::Add(%614, %371)
  %381 : Float(1:8667136, 256:33856, 184:184, 184:1, requires_grad=1, device=cpu) = onnx::Relu(%380) # /home/gordon/miniconda3/envs/torch/lib/python3.8/site-packages/torch/nn/functional.py:1134:0
  %617 : Float(1:4333568, 128:33856, 184:184, 184:1, requires_grad=1, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%381, %618, %619)
  %384 : Float(1:4333568, 128:33856, 184:184, 184:1, requires_grad=1, device=cpu) = onnx::Relu(%617) # /home/gordon/miniconda3/envs/torch/lib/python3.8/site-packages/torch/nn/functional.py:1134:0
  %620 : Float(1:1083392, 128:8464, 92:92, 92:1, requires_grad=1, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[2, 2]](%384, %621, %622)
  %387 : Float(1:1083392, 128:8464, 92:92, 92:1, requires_grad=1, device=cpu) = onnx::Relu(%620) # /home/gordon/miniconda3/envs/torch/lib/python3.8/site-packages/torch/nn/functional.py:1134:0
  %623 : Float(1:4333568, 512:8464, 92:92, 92:1, requires_grad=1, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%387, %624, %625)
  %626 : Float(1:4333568, 512:8464, 92:92, 92:1, requires_grad=1, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[2, 2]](%381, %627, %628)
  %392 : Float(1:4333568, 512:8464, 92:92, 92:1, requires_grad=1, device=cpu) = onnx::Add(%623, %626)
  %393 : Float(1:4333568, 512:8464, 92:92, 92:1, requires_grad=1, device=cpu) = onnx::Relu(%392) # /home/gordon/miniconda3/envs/torch/lib/python3.8/site-packages/torch/nn/functional.py:1134:0
  %629 : Float(1:1083392, 128:8464, 92:92, 92:1, requires_grad=1, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%393, %630, %631)
  %396 : Float(1:1083392, 128:8464, 92:92, 92:1, requires_grad=1, device=cpu) = onnx::Relu(%629) # /home/gordon/miniconda3/envs/torch/lib/python3.8/site-packages/torch/nn/functional.py:1134:0
  %632 : Float(1:1083392, 128:8464, 92:92, 92:1, requires_grad=1, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1]](%396, %633, %634)
  %399 : Float(1:1083392, 128:8464, 92:92, 92:1, requires_grad=1, device=cpu) = onnx::Relu(%632) # /home/gordon/miniconda3/envs/torch/lib/python3.8/site-packages/torch/nn/functional.py:1134:0
  %635 : Float(1:4333568, 512:8464, 92:92, 92:1, requires_grad=1, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%399, %636, %637)
  %402 : Float(1:4333568, 512:8464, 92:92, 92:1, requires_grad=1, device=cpu) = onnx::Add(%635, %393)
  %403 : Float(1:4333568, 512:8464, 92:92, 92:1, requires_grad=1, device=cpu) = onnx::Relu(%402) # /home/gordon/miniconda3/envs/torch/lib/python3.8/site-packages/torch/nn/functional.py:1134:0
  %638 : Float(1:1083392, 128:8464, 92:92, 92:1, requires_grad=1, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%403, %639, %640)
  %406 : Float(1:1083392, 128:8464, 92:92, 92:1, requires_grad=1, device=cpu) = onnx::Relu(%638) # /home/gordon/miniconda3/envs/torch/lib/python3.8/site-packages/torch/nn/functional.py:1134:0
  %641 : Float(1:1083392, 128:8464, 92:92, 92:1, requires_grad=1, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1]](%406, %642, %643)
  %409 : Float(1:1083392, 128:8464, 92:92, 92:1, requires_grad=1, device=cpu) = onnx::Relu(%641) # /home/gordon/miniconda3/envs/torch/lib/python3.8/site-packages/torch/nn/functional.py:1134:0
  %644 : Float(1:4333568, 512:8464, 92:92, 92:1, requires_grad=1, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%409, %645, %646)
  %412 : Float(1:4333568, 512:8464, 92:92, 92:1, requires_grad=1, device=cpu) = onnx::Add(%644, %403)
  %413 : Float(1:4333568, 512:8464, 92:92, 92:1, requires_grad=1, device=cpu) = onnx::Relu(%412) # /home/gordon/miniconda3/envs/torch/lib/python3.8/site-packages/torch/nn/functional.py:1134:0
  %647 : Float(1:1083392, 128:8464, 92:92, 92:1, requires_grad=1, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%413, %648, %649)
  %416 : Float(1:1083392, 128:8464, 92:92, 92:1, requires_grad=1, device=cpu) = onnx::Relu(%647) # /home/gordon/miniconda3/envs/torch/lib/python3.8/site-packages/torch/nn/functional.py:1134:0
  %650 : Float(1:1083392, 128:8464, 92:92, 92:1, requires_grad=1, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1]](%416, %651, %652)
  %419 : Float(1:1083392, 128:8464, 92:92, 92:1, requires_grad=1, device=cpu) = onnx::Relu(%650) # /home/gordon/miniconda3/envs/torch/lib/python3.8/site-packages/torch/nn/functional.py:1134:0
  %653 : Float(1:4333568, 512:8464, 92:92, 92:1, requires_grad=1, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%419, %654, %655)
  %422 : Float(1:4333568, 512:8464, 92:92, 92:1, requires_grad=1, device=cpu) = onnx::Add(%653, %413)
  %423 : Float(1:4333568, 512:8464, 92:92, 92:1, requires_grad=1, device=cpu) = onnx::Relu(%422) # /home/gordon/miniconda3/envs/torch/lib/python3.8/site-packages/torch/nn/functional.py:1134:0
  %656 : Float(1:2166784, 256:8464, 92:92, 92:1, requires_grad=1, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%423, %657, %658)
  %426 : Float(1:2166784, 256:8464, 92:92, 92:1, requires_grad=1, device=cpu) = onnx::Relu(%656) # /home/gordon/miniconda3/envs/torch/lib/python3.8/site-packages/torch/nn/functional.py:1134:0
  %659 : Float(1:541696, 256:2116, 46:46, 46:1, requires_grad=1, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[2, 2]](%426, %660, %661)
  %429 : Float(1:541696, 256:2116, 46:46, 46:1, requires_grad=1, device=cpu) = onnx::Relu(%659) # /home/gordon/miniconda3/envs/torch/lib/python3.8/site-packages/torch/nn/functional.py:1134:0
  %662 : Float(1:2166784, 1024:2116, 46:46, 46:1, requires_grad=1, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%429, %663, %664)
  %665 : Float(1:2166784, 1024:2116, 46:46, 46:1, requires_grad=1, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[2, 2]](%423, %666, %667)
  %434 : Float(1:2166784, 1024:2116, 46:46, 46:1, requires_grad=1, device=cpu) = onnx::Add(%662, %665)
  %435 : Float(1:2166784, 1024:2116, 46:46, 46:1, requires_grad=1, device=cpu) = onnx::Relu(%434) # /home/gordon/miniconda3/envs/torch/lib/python3.8/site-packages/torch/nn/functional.py:1134:0
  %668 : Float(1:541696, 256:2116, 46:46, 46:1, requires_grad=1, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%435, %669, %670)
  %438 : Float(1:541696, 256:2116, 46:46, 46:1, requires_grad=1, device=cpu) = onnx::Relu(%668) # /home/gordon/miniconda3/envs/torch/lib/python3.8/site-packages/torch/nn/functional.py:1134:0
  %671 : Float(1:541696, 256:2116, 46:46, 46:1, requires_grad=1, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1]](%438, %672, %673)
  %441 : Float(1:541696, 256:2116, 46:46, 46:1, requires_grad=1, device=cpu) = onnx::Relu(%671) # /home/gordon/miniconda3/envs/torch/lib/python3.8/site-packages/torch/nn/functional.py:1134:0
  %674 : Float(1:2166784, 1024:2116, 46:46, 46:1, requires_grad=1, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%441, %675, %676)
  %444 : Float(1:2166784, 1024:2116, 46:46, 46:1, requires_grad=1, device=cpu) = onnx::Add(%674, %435)
  %445 : Float(1:2166784, 1024:2116, 46:46, 46:1, requires_grad=1, device=cpu) = onnx::Relu(%444) # /home/gordon/miniconda3/envs/torch/lib/python3.8/site-packages/torch/nn/functional.py:1134:0
  %677 : Float(1:541696, 256:2116, 46:46, 46:1, requires_grad=1, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%445, %678, %679)
  %448 : Float(1:541696, 256:2116, 46:46, 46:1, requires_grad=1, device=cpu) = onnx::Relu(%677) # /home/gordon/miniconda3/envs/torch/lib/python3.8/site-packages/torch/nn/functional.py:1134:0
  %680 : Float(1:541696, 256:2116, 46:46, 46:1, requires_grad=1, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1]](%448, %681, %682)
  %451 : Float(1:541696, 256:2116, 46:46, 46:1, requires_grad=1, device=cpu) = onnx::Relu(%680) # /home/gordon/miniconda3/envs/torch/lib/python3.8/site-packages/torch/nn/functional.py:1134:0
  %683 : Float(1:2166784, 1024:2116, 46:46, 46:1, requires_grad=1, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%451, %684, %685)
  %454 : Float(1:2166784, 1024:2116, 46:46, 46:1, requires_grad=1, device=cpu) = onnx::Add(%683, %445)
  %455 : Float(1:2166784, 1024:2116, 46:46, 46:1, requires_grad=1, device=cpu) = onnx::Relu(%454) # /home/gordon/miniconda3/envs/torch/lib/python3.8/site-packages/torch/nn/functional.py:1134:0
  %686 : Float(1:541696, 256:2116, 46:46, 46:1, requires_grad=1, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%455, %687, %688)
  %458 : Float(1:541696, 256:2116, 46:46, 46:1, requires_grad=1, device=cpu) = onnx::Relu(%686) # /home/gordon/miniconda3/envs/torch/lib/python3.8/site-packages/torch/nn/functional.py:1134:0
  %689 : Float(1:541696, 256:2116, 46:46, 46:1, requires_grad=1, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1]](%458, %690, %691)
  %461 : Float(1:541696, 256:2116, 46:46, 46:1, requires_grad=1, device=cpu) = onnx::Relu(%689) # /home/gordon/miniconda3/envs/torch/lib/python3.8/site-packages/torch/nn/functional.py:1134:0
  %692 : Float(1:2166784, 1024:2116, 46:46, 46:1, requires_grad=1, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%461, %693, %694)
  %464 : Float(1:2166784, 1024:2116, 46:46, 46:1, requires_grad=1, device=cpu) = onnx::Add(%692, %455)
  %465 : Float(1:2166784, 1024:2116, 46:46, 46:1, requires_grad=1, device=cpu) = onnx::Relu(%464) # /home/gordon/miniconda3/envs/torch/lib/python3.8/site-packages/torch/nn/functional.py:1134:0
  %695 : Float(1:541696, 256:2116, 46:46, 46:1, requires_grad=1, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%465, %696, %697)
  %468 : Float(1:541696, 256:2116, 46:46, 46:1, requires_grad=1, device=cpu) = onnx::Relu(%695) # /home/gordon/miniconda3/envs/torch/lib/python3.8/site-packages/torch/nn/functional.py:1134:0
  %698 : Float(1:541696, 256:2116, 46:46, 46:1, requires_grad=1, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1]](%468, %699, %700)
  %471 : Float(1:541696, 256:2116, 46:46, 46:1, requires_grad=1, device=cpu) = onnx::Relu(%698) # /home/gordon/miniconda3/envs/torch/lib/python3.8/site-packages/torch/nn/functional.py:1134:0
  %701 : Float(1:2166784, 1024:2116, 46:46, 46:1, requires_grad=1, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%471, %702, %703)
  %474 : Float(1:2166784, 1024:2116, 46:46, 46:1, requires_grad=1, device=cpu) = onnx::Add(%701, %465)
  %475 : Float(1:2166784, 1024:2116, 46:46, 46:1, requires_grad=1, device=cpu) = onnx::Relu(%474) # /home/gordon/miniconda3/envs/torch/lib/python3.8/site-packages/torch/nn/functional.py:1134:0
  %704 : Float(1:541696, 256:2116, 46:46, 46:1, requires_grad=1, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%475, %705, %706)
  %478 : Float(1:541696, 256:2116, 46:46, 46:1, requires_grad=1, device=cpu) = onnx::Relu(%704) # /home/gordon/miniconda3/envs/torch/lib/python3.8/site-packages/torch/nn/functional.py:1134:0
  %707 : Float(1:541696, 256:2116, 46:46, 46:1, requires_grad=1, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1]](%478, %708, %709)
  %481 : Float(1:541696, 256:2116, 46:46, 46:1, requires_grad=1, device=cpu) = onnx::Relu(%707) # /home/gordon/miniconda3/envs/torch/lib/python3.8/site-packages/torch/nn/functional.py:1134:0
  %710 : Float(1:2166784, 1024:2116, 46:46, 46:1, requires_grad=1, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%481, %711, %712)
  %484 : Float(1:2166784, 1024:2116, 46:46, 46:1, requires_grad=1, device=cpu) = onnx::Add(%710, %475)
  %485 : Float(1:2166784, 1024:2116, 46:46, 46:1, requires_grad=1, device=cpu) = onnx::Relu(%484) # /home/gordon/miniconda3/envs/torch/lib/python3.8/site-packages/torch/nn/functional.py:1134:0
  %713 : Float(1:1083392, 512:2116, 46:46, 46:1, requires_grad=1, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%485, %714, %715)
  %488 : Float(1:1083392, 512:2116, 46:46, 46:1, requires_grad=1, device=cpu) = onnx::Relu(%713) # /home/gordon/miniconda3/envs/torch/lib/python3.8/site-packages/torch/nn/functional.py:1134:0
  %716 : Float(1:270848, 512:529, 23:23, 23:1, requires_grad=1, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[2, 2]](%488, %717, %718)
  %491 : Float(1:270848, 512:529, 23:23, 23:1, requires_grad=1, device=cpu) = onnx::Relu(%716) # /home/gordon/miniconda3/envs/torch/lib/python3.8/site-packages/torch/nn/functional.py:1134:0
  %719 : Float(1:1083392, 2048:529, 23:23, 23:1, requires_grad=1, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%491, %720, %721)
  %722 : Float(1:1083392, 2048:529, 23:23, 23:1, requires_grad=1, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[2, 2]](%485, %723, %724)
  %496 : Float(1:1083392, 2048:529, 23:23, 23:1, requires_grad=1, device=cpu) = onnx::Add(%719, %722)
  %497 : Float(1:1083392, 2048:529, 23:23, 23:1, requires_grad=1, device=cpu) = onnx::Relu(%496) # /home/gordon/miniconda3/envs/torch/lib/python3.8/site-packages/torch/nn/functional.py:1134:0
  %725 : Float(1:270848, 512:529, 23:23, 23:1, requires_grad=1, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%497, %726, %727)
  %500 : Float(1:270848, 512:529, 23:23, 23:1, requires_grad=1, device=cpu) = onnx::Relu(%725) # /home/gordon/miniconda3/envs/torch/lib/python3.8/site-packages/torch/nn/functional.py:1134:0
  %728 : Float(1:270848, 512:529, 23:23, 23:1, requires_grad=1, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1]](%500, %729, %730)
  %503 : Float(1:270848, 512:529, 23:23, 23:1, requires_grad=1, device=cpu) = onnx::Relu(%728) # /home/gordon/miniconda3/envs/torch/lib/python3.8/site-packages/torch/nn/functional.py:1134:0
  %731 : Float(1:1083392, 2048:529, 23:23, 23:1, requires_grad=1, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%503, %732, %733)
  %506 : Float(1:1083392, 2048:529, 23:23, 23:1, requires_grad=1, device=cpu) = onnx::Add(%731, %497)
  %507 : Float(1:1083392, 2048:529, 23:23, 23:1, requires_grad=1, device=cpu) = onnx::Relu(%506) # /home/gordon/miniconda3/envs/torch/lib/python3.8/site-packages/torch/nn/functional.py:1134:0
  %734 : Float(1:270848, 512:529, 23:23, 23:1, requires_grad=1, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%507, %735, %736)
  %510 : Float(1:270848, 512:529, 23:23, 23:1, requires_grad=1, device=cpu) = onnx::Relu(%734) # /home/gordon/miniconda3/envs/torch/lib/python3.8/site-packages/torch/nn/functional.py:1134:0
  %737 : Float(1:270848, 512:529, 23:23, 23:1, requires_grad=1, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1]](%510, %738, %739)
  %513 : Float(1:270848, 512:529, 23:23, 23:1, requires_grad=1, device=cpu) = onnx::Relu(%737) # /home/gordon/miniconda3/envs/torch/lib/python3.8/site-packages/torch/nn/functional.py:1134:0
  %740 : Float(1:1083392, 2048:529, 23:23, 23:1, requires_grad=1, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%513, %741, %742)
  %516 : Float(1:1083392, 2048:529, 23:23, 23:1, requires_grad=1, device=cpu) = onnx::Add(%740, %507)
  %517 : Float(1:1083392, 2048:529, 23:23, 23:1, requires_grad=1, device=cpu) = onnx::Relu(%516) # /home/gordon/miniconda3/envs/torch/lib/python3.8/site-packages/torch/nn/functional.py:1134:0
  %518 : Float(1:135424, 256:529, 23:23, 23:1, requires_grad=1, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%517, %decoder.in5.weight) # /home/gordon/miniconda3/envs/torch/lib/python3.8/site-packages/torch/nn/modules/conv.py:419:0
  %519 : Float(1:541696, 256:2116, 46:46, 46:1, requires_grad=1, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%485, %decoder.in4.weight) # /home/gordon/miniconda3/envs/torch/lib/python3.8/site-packages/torch/nn/modules/conv.py:419:0
  %520 : Float(1:2166784, 256:8464, 92:92, 92:1, requires_grad=1, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%423, %decoder.in3.weight) # /home/gordon/miniconda3/envs/torch/lib/python3.8/site-packages/torch/nn/modules/conv.py:419:0
  %521 : Float(1:8667136, 256:33856, 184:184, 184:1, requires_grad=1, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%381, %decoder.in2.weight) # /home/gordon/miniconda3/envs/torch/lib/python3.8/site-packages/torch/nn/modules/conv.py:419:0
  %525 : Tensor = onnx::Constant[value=[ CPUFloatType{0} ]]()
  %526 : Float(1:541696, 256:2116, 46:46, 46:1, requires_grad=1, device=cpu) = onnx::Resize[coordinate_transformation_mode="asymmetric", cubic_coeff_a=-0.75, mode="nearest", nearest_mode="floor"](%518, %525, %746) # /home/gordon/miniconda3/envs/torch/lib/python3.8/site-packages/torch/nn/functional.py:3132:0
  %527 : Float(1:541696, 256:2116, 46:46, 46:1, requires_grad=1, device=cpu) = onnx::Add(%526, %519) # /home/gordon/PycharmProjects/GSoC_pytorch2onnx/DB/onnx_model.py:67:0
  %531 : Tensor = onnx::Constant[value=[ CPUFloatType{0} ]]()
  %532 : Float(1:2166784, 256:8464, 92:92, 92:1, requires_grad=1, device=cpu) = onnx::Resize[coordinate_transformation_mode="asymmetric", cubic_coeff_a=-0.75, mode="nearest", nearest_mode="floor"](%527, %531, %747) # /home/gordon/miniconda3/envs/torch/lib/python3.8/site-packages/torch/nn/functional.py:3132:0
  %533 : Float(1:2166784, 256:8464, 92:92, 92:1, requires_grad=1, device=cpu) = onnx::Add(%532, %520) # /home/gordon/PycharmProjects/GSoC_pytorch2onnx/DB/onnx_model.py:68:0
  %537 : Tensor = onnx::Constant[value=[ CPUFloatType{0} ]]()
  %538 : Float(1:8667136, 256:33856, 184:184, 184:1, requires_grad=1, device=cpu) = onnx::Resize[coordinate_transformation_mode="asymmetric", cubic_coeff_a=-0.75, mode="nearest", nearest_mode="floor"](%533, %537, %748) # /home/gordon/miniconda3/envs/torch/lib/python3.8/site-packages/torch/nn/functional.py:3132:0
  %539 : Float(1:8667136, 256:33856, 184:184, 184:1, requires_grad=1, device=cpu) = onnx::Add(%538, %521) # /home/gordon/PycharmProjects/GSoC_pytorch2onnx/DB/onnx_model.py:69:0
  %540 : Float(1:33856, 64:529, 23:23, 23:1, requires_grad=1, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1]](%518, %decoder.out5.0.weight) # /home/gordon/miniconda3/envs/torch/lib/python3.8/site-packages/torch/nn/modules/conv.py:419:0
  %544 : Tensor = onnx::Constant[value=[ CPUFloatType{0} ]]()
  %545 : Float(1:2166784, 64:33856, 184:184, 184:1, requires_grad=1, device=cpu) = onnx::Resize[coordinate_transformation_mode="asymmetric", cubic_coeff_a=-0.75, mode="nearest", nearest_mode="floor"](%540, %544, %749) # /home/gordon/miniconda3/envs/torch/lib/python3.8/site-packages/torch/nn/functional.py:3132:0
  %546 : Float(1:135424, 64:2116, 46:46, 46:1, requires_grad=1, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1]](%527, %decoder.out4.0.weight) # /home/gordon/miniconda3/envs/torch/lib/python3.8/site-packages/torch/nn/modules/conv.py:419:0
  %550 : Tensor = onnx::Constant[value=[ CPUFloatType{0} ]]()
  %551 : Float(1:2166784, 64:33856, 184:184, 184:1, requires_grad=1, device=cpu) = onnx::Resize[coordinate_transformation_mode="asymmetric", cubic_coeff_a=-0.75, mode="nearest", nearest_mode="floor"](%546, %550, %750) # /home/gordon/miniconda3/envs/torch/lib/python3.8/site-packages/torch/nn/functional.py:3132:0
  %552 : Float(1:541696, 64:8464, 92:92, 92:1, requires_grad=1, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1]](%533, %decoder.out3.0.weight) # /home/gordon/miniconda3/envs/torch/lib/python3.8/site-packages/torch/nn/modules/conv.py:419:0
  %556 : Tensor = onnx::Constant[value=[ CPUFloatType{0} ]]()
  %557 : Float(1:2166784, 64:33856, 184:184, 184:1, requires_grad=1, device=cpu) = onnx::Resize[coordinate_transformation_mode="asymmetric", cubic_coeff_a=-0.75, mode="nearest", nearest_mode="floor"](%552, %556, %751) # /home/gordon/miniconda3/envs/torch/lib/python3.8/site-packages/torch/nn/functional.py:3132:0
  %558 : Float(1:2166784, 64:33856, 184:184, 184:1, requires_grad=1, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1]](%539, %decoder.out2.weight) # /home/gordon/miniconda3/envs/torch/lib/python3.8/site-packages/torch/nn/modules/conv.py:419:0
  %559 : Float(1:8667136, 256:33856, 184:184, 184:1, requires_grad=1, device=cpu) = onnx::Concat[axis=1](%545, %551, %557, %558) # /home/gordon/PycharmProjects/GSoC_pytorch2onnx/DB/onnx_model.py:76:0
  %743 : Float(1:2166784, 64:33856, 184:184, 184:1, requires_grad=1, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1]](%559, %744, %745)
  %562 : Float(1:2166784, 64:33856, 184:184, 184:1, requires_grad=1, device=cpu) = onnx::Relu(%743) # /home/gordon/miniconda3/envs/torch/lib/python3.8/site-packages/torch/nn/functional.py:1134:0
  %563 : Float(1:8667136, 64:135424, 368:368, 368:1, requires_grad=1, device=cpu) = onnx::ConvTranspose[dilations=[1, 1], group=1, kernel_shape=[2, 2], pads=[0, 0, 0, 0], strides=[2, 2]](%562, %decoder.binarize.3.weight, %decoder.binarize.3.bias) # /home/gordon/miniconda3/envs/torch/lib/python3.8/site-packages/torch/nn/modules/conv.py:927:0
  %564 : Float(1:8667136, 64:135424, 368:368, 368:1, requires_grad=1, device=cpu) = onnx::BatchNormalization[epsilon=1.0000000000000001e-05, momentum=0.90000000000000002](%563, %decoder.binarize.4.weight, %decoder.binarize.4.bias, %decoder.binarize.4.running_mean, %decoder.binarize.4.running_var) # /home/gordon/miniconda3/envs/torch/lib/python3.8/site-packages/torch/nn/functional.py:2056:0
  %565 : Float(1:8667136, 64:135424, 368:368, 368:1, requires_grad=1, device=cpu) = onnx::Relu(%564) # /home/gordon/miniconda3/envs/torch/lib/python3.8/site-packages/torch/nn/functional.py:1134:0
  %566 : Float(1:541696, 1:541696, 736:736, 736:1, requires_grad=1, device=cpu) = onnx::ConvTranspose[dilations=[1, 1], group=1, kernel_shape=[2, 2], pads=[0, 0, 0, 0], strides=[2, 2]](%565, %decoder.binarize.6.weight, %decoder.binarize.6.bias) # /home/gordon/miniconda3/envs/torch/lib/python3.8/site-packages/torch/nn/modules/conv.py:927:0
  %567 : Float(1:541696, 1:541696, 736:736, 736:1, requires_grad=1, device=cpu) = onnx::Sigmoid(%566) # /home/gordon/miniconda3/envs/torch/lib/python3.8/site-packages/torch/nn/modules/activation.py:299:0
  %568 : Tensor = onnx::Constant[value={0}]()
  %569 : Tensor = onnx::Shape(%567)
  %570 : Tensor = onnx::Gather[axis=0](%569, %568)
  %571 : Long(1:1, requires_grad=0, device=cpu) = onnx::Constant[value={1}]()
  %572 : Tensor = onnx::Equal(%570, %571)
  %573 : Float(1:541696, 736:736, 736:1, requires_grad=1, device=cpu) = onnx::If(%572) # /home/gordon/PycharmProjects/GSoC_pytorch2onnx/DB/onnx_model.py:94:0
    block0():
      %574 : Tensor = onnx::Squeeze[axes=[0]](%567)
      -> (%574)
    block1():
      %575 : Tensor = onnx::Identity(%567)
      -> (%575)
  %576 : Tensor = onnx::Constant[value={0}]()
  %577 : Tensor = onnx::Shape(%573)
  %578 : Tensor = onnx::Gather[axis=0](%577, %576)
  %579 : Long(1:1, requires_grad=0, device=cpu) = onnx::Constant[value={1}]()
  %580 : Tensor = onnx::Equal(%578, %579)
  %out : Float(736:736, 736:1, requires_grad=1, device=cpu) = onnx::If(%580) # /home/gordon/PycharmProjects/GSoC_pytorch2onnx/DB/onnx_model.py:94:0
    block0():
      %582 : Tensor = onnx::Squeeze[axes=[0]](%573)
      -> (%582)
    block1():
      %583 : Tensor = onnx::Identity(%573)
      -> (%583)
  return (%out)
```

</details>

##### Steps to reproduce

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