Comments (5)
Hi @jasw1001 . Did you find any solution for that? It seems it goes to the customized backward function but I cant debug it. My main problem is that I cant use multi-GPU for this backward function (it jumps out without any specific error). Do you have any idea how to used torch.autograde and skip this backward function?
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It seems that the torch.autograde cannot be used in Dice loss function, because some functions in Dice loss fuction are not supported by torch.autograde. For the debug problem, it's a bug in pytorch, so you may use 'ipdb' or 'pdb' to debug it.
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Thanks @jasw1001 . Have you tried to implement your own version of loss function? I have used 'pdb' but it stuck in a line after
#import pdb
#pdb.set_trace()
and never went through next lines. It is very strange for me.
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Hi @jasw1001 @CSMEDEEP
could you give the steps as to how to start the training? The code uses some preprocessed files from the original Luna16 dataset. How do I run the preprocessing?
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@abhiML hello~ i really want to know how to preprocess the dataset LUNA16 to get the files "normalized_brightened_CT_2_5"etc.. , have you figure it out? thanks a lot~`
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Related Issues (20)
- What augmentation is used? HOT 1
- A comrehensive Readme file? HOT 3
- How to test my segmentation after training?
- ContBatchNorm3d HOT 7
- The implementation of the decoder is different than the architecture posted here.
- how to do cross validation?
- Concatenation operation in InputTransition causing confusion and memory abuse HOT 1
- torchbiomed's problem HOT 2
- hi,i have question about trian
- list index out of range HOT 6
- There are not many bugs in your code. HOT 3
- How can I install torchbiomed HOT 3
- model complie error HOT 3
- Question about up-convolution block HOT 2
- Questions about implementation still actual ?
- there are too
- ε εηΈδΊ
- Fine-grained featrues forwarding
- OutputTransition
- Under which file should the dataset be placed
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