Error: RuntimeError: Trying to resize storage that is not resizable

The shapes of different data are not the same, so they cannot be alligned or collated correctly.

What is the shape for different loss functions?

The shape of the input and target for the loss function is very annoying. So I summarize them here.

CrossEntropyLoss

1. shape

   predictions = torch.rand(2, 3, 4)
   target = torch.rand(2, 3)
   print(predictions.shape)
   print(target.shape)
   nn.CrossEntropyLoss(predictions.transpose(1, 2), target)  # the shape should be transposed!
   

2. type

RuntimeError: Expected object of scalar type Long but got scalar type Float for argument #2 'target'

input can be in any format, just targets should be in long.

# Example of target with class indices
loss = nn.CrossEntropyLoss()
input = torch.randn(3, 5, requires_grad=True)
target = torch.empty(3, dtype=torch.long).random_(5)  # target should be long
output = loss(input, target)
output.backward()

# Example of target with class probabilities
input = torch.randn(3, 5, requires_grad=True)
target = torch.randn(3, 5).softmax(dim=1)  # target with probabilities should be converted to between [0,1].
output = loss(input, target)
output.backward()

NLLLoss

Dice loss

Because I used the monai.DiceLoss, the shape should be

error: TypeError: only integer tensors of a single element can be converted to an index

Answer: change x to torch.tensor(x)