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Implement Vnet with 8 slices in 3d instead of 16 slices #52

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AmitAilianiSDC opened this issue Jun 7, 2018 · 4 comments
Open

Implement Vnet with 8 slices in 3d instead of 16 slices #52

AmitAilianiSDC opened this issue Jun 7, 2018 · 4 comments

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@AmitAilianiSDC
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I would appreciate if you can help how to use this code by considering only 8 slices for 3d dataset. As per my understanding I have made changes to following initialization code. I have made changes in InputTransition to split data into 8 channels. I am getting following error. Please let me know how to address this issue.

Code Changes

    self.in_tr = InputTransition(8, elu)
    self.down_tr32 = DownTransition(8, 1, elu)
    self.down_tr64 = DownTransition(16, 2, elu)
    self.down_tr128 = DownTransition(32, 3, elu, dropout=True)
    self.down_tr256 = DownTransition(64, 2, elu, dropout=True)
    self.up_tr256 = UpTransition(128, 128, 2, elu, dropout=True)
    self.up_tr128 = UpTransition(128, 64, 2, elu, dropout=True)
    self.up_tr64 = UpTransition(64, 32, 1, elu)
    self.up_tr32 = UpTransition(32, 16, 1, elu)
    self.out_tr = OutputTransition(16, elu, nll)

Error:

output = model(data)
File "C:\Program Files\Python36\lib\site-packages\torch\nn\modules\module.py", line 224, in call
result = self.forward(*input, **kwargs)
File "C:\Program Files\Python36\lib\site-packages\torch\nn\parallel\data_parallel.py", line 60, in forward
outputs = self.parallel_apply(replicas, inputs, kwargs)
File "C:\Program Files\Python36\lib\site-packages\torch\nn\parallel\data_parallel.py", line 70, in parallel_apply
return parallel_apply(replicas, inputs, kwargs, self.device_ids[:len(replicas)])
File "C:\Program Files\Python36\lib\site-packages\torch\nn\parallel\parallel_apply.py", line 67, in parallel_apply
raise output
File "C:\Program Files\Python36\lib\site-packages\torch\nn\parallel\parallel_apply.py", line 42, in _worker
output = module(*input, **kwargs)
File "C:\Program Files\Python36\lib\site-packages\torch\nn\modules\module.py", line 224, in call
result = self.forward(*input, **kwargs)
File "e:\winPyTorch\vnet.FullLung_nodule_segmentation\vnet.py", line 188, in forward
out256 = self.down_tr256(out128)
File "C:\Program Files\Python36\lib\site-packages\torch\nn\modules\module.py", line 224, in call
result = self.forward(*input, **kwargs)
File "e:\winPyTorch\vnet.FullLung_nodule_segmentation\vnet.py", line 81, in forward
down = self.relu1(self.bn1(self.down_conv(x)))

@faustomilletari
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faustomilletari commented Jun 7, 2018 via email

@AmitAilianiSDC
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thank you for your prompt response. I am new to semantic segmentation. Can you suggest which layer would be appropriate to remove?

@faustomilletari
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faustomilletari commented Jun 7, 2018 via email

@AmitAilianiSDC
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AmitAilianiSDC commented Jun 8, 2018 via email

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