Comments (4)
hello @wentj897, can I know which device you are using? From my experience, it can be due to out of memory error, you can reduce memory requirement by:
- Reduce
batch_size
,num_samples
,train_patch_size
for training step - Reduce
sw_batch_size
,val_patch_size
for validation step
You are having a problem with the validation step so I think you should reduce val_patch_size
to a smaller number like 64x64x64
that can fit with your device. Additionally, the training step will use even larger memory so I think you should reduce them also.
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thank you very much for your open source code. I also encountered this problem in the training stage. How do you solve it? My GPU is 3090, cuda11.3.I've tried to reduce batch_size, num_samples,train_patch_size, but it not work.
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path/to/luna/
imgs
segs
Is the file extracted from subset0-subset9 stored in folder imgs?
Is the extracted file seg-luns-luna16 stored in folder segs?
Do they need any other pretreatment?
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Hello @kingjames1155 , sorry for my late reply.
For your first question, if you have the same problem as @wentj897 , actually you are getting the error at the validation step, not the training step. Hence, try to reduce the val_patch_size and sw_batch_size first to see if it can solve the problem.
For your second question, the answer is yes, you just need to extract LUNA dataset as it is. I also point out some error files in the README that you need to remove.
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Related Issues (12)
- RuntimeError for training on hippocampus dataset HOT 4
- RuntimeError: Given groups=1, weight of size [16, 2, 5, 5, 5], expected input[1, 1, 32, 32, 32] to have 2 channels, but got 1 channels instead HOT 3
- a dimension parameter in Convolution HOT 2
- How to predict on LUNA16 using trained model? HOT 1
- dimensionality problem when changing number of layers HOT 2
- How to compute the metrics between testset predictions and true labels? HOT 6
- Getting stuck at validation step HOT 9
- How to enable all gpus? HOT 2
- Qualitative results
- RuntimeError: CUDA error: device-side assert triggered HOT 1
- Complexity of the model
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