Similar to <a href="https://github.com/msr-fiddle/pipedream/issues/16" data-hovercard-

Error occurred in profiling about pipedream HOT 3 CLOSED

msr-fiddle commented on July 29, 2024

Error occurred in profiling

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Comments (3)

deepakn94 commented on July 29, 2024

Seems like you are trying to profile your custom implementation of the ResNet model?
If this is the case, you want to patch the flatten method, similar to this: https://github.com/msr-fiddle/pipedream/blob/master/profiler/torchmodules/torchgraph/graph_creator.py#L157. This is to ensure that we can construct the computation DAG for the given model. At a high level, this method needs to unwrap the TensorWrapper object, call torch.flatten, then rewrap the result as a TensorWrapper object.

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gudiandian commented on July 29, 2024

I use my custom implementation of ResNet because of another error:

Traceback (most recent call last):
  File "main.py", line 596, in <module>
    main()
  File "main.py", line 287, in main
    verbose=args.verbose, device="cuda")
  File "../torchmodules/torchsummary/torchsummary.py", line 77, in summary
    model(*model_input)
  File "/root/anaconda3/lib/python3.7/site-packages/torch/nn/modules/module.py", line 574, in __call__
    result = self.forward(*input, **kwargs)
  File "/root/anaconda3/lib/python3.7/site-packages/torchvision/models/resnet.py", line 149, in forward
    x = self.avgpool(x)
  File "/root/anaconda3/lib/python3.7/site-packages/torch/nn/modules/module.py", line 574, in __call__
    result = self.forward(*input, **kwargs)
  File "/root/anaconda3/lib/python3.7/site-packages/torch/nn/modules/pooling.py", line 554, in forward
    self.padding, self.ceil_mode, self.count_include_pad, self.divisor_override)
RuntimeError: Given input size: (2048x1x1). Calculated output size: (2048x-5x-5). Output size is too small

It is said that this might be caught by wrong parameter of avgpool layer in torchvision.

Thanks for your suggestion! This worked for my situation. I will close this issue.

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deepakn94 commented on July 29, 2024

Yup, that's correct -- you need to use a version of the VGG model that wraps tensors with TensorWrappers

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