Hi Guys,
There is a famous trick in u-net architecture to use custom weight maps to increase accuracy.Below are the details of it-
Now, by asking here and at multiple other place, I get to know about 2 approaches.I want to know which one is correct or is there any other right approach which is more correct?
1)First is to use torch.nn.Functional method in the training loop-
loss = torch.nn.functional.cross_entropy(output, target, w)
where w will be the calculated custom weight.
2)Second is to use reduction='none' in the calling of loss function outside the training loop
criterion = torch.nn.CrossEntropy(reduction='none')
and then in the training loop multiplying with the custom weight-
gt # Ground truth, format torch.long
pd # Network output
W # per-element weighting based on the distance map from UNet
loss = criterion(pd, gt)
loss = W*loss # Ensure that weights are scaled appropriately
loss = torch.sum(loss.flatten(start_dim=1), axis=0) # Sums the loss per image
loss = torch.mean(loss) # Average across a batch
What’s the right way to do things in PyTorch in this case