I have implemented Unet with custom loss function : Dice loss.

while training, I receive the following message :

`/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:32: UserWarning: Mixed memory format inputs detected while calling the operator. The operator will output contiguous tensor even if some of the inputs are in channels_last format. (Triggered internally at /pytorch/aten/src/ATen/native/TensorIterator.cpp:918.)`

this warning appear only when calling the Dice loss function. this does not happens when I am calling pytorch function torch.nn.CrossEntropyLoss().

this is my Dice implementation (copied from some webpage):

```
def calculate_dice_loss(logits,true, eps = 1e-7):
"""Computes the Sørensen–Dice loss.
Note that PyTorch optimizers minimize a loss. In this
case, we would like to maximize the dice loss so we
return the negated dice loss.
Args:
true: a tensor of shape [Batch size x 512 x 512].
logits: a tensor of shape [Batch size x numLabels x 512 x 512]. Corresponds to
the raw output or logits of the model.
eps: added to the denominator for numerical stability.
Returns:
dice_loss: the Sørensen–Dice loss.
"""
num_classes = logits.shape[1]
true = true.unsqueeze(1) # now true: a tensor of shape [Batch size x 1 x 512 x 512].
true_1_hot = torch.eye(num_classes)[true.squeeze(1)]
true_1_hot = true_1_hot.permute(0, 3, 1, 2).float()
probas = F.softmax(logits, dim = 1)
true_1_hot = true_1_hot.type(logits.type())
dims = (0,) + tuple(range(2, true.ndimension()))
intersection = torch.sum(probas * true_1_hot, dims)
cardinality = torch.sum(probas + true_1_hot, dims)
dice_loss = (2. * intersection / (cardinality + eps)).mean()
return (1 - dice_loss)
```

and this is how I call the loss function while training:

```
# loss values
CrossEntropy_Loss = CrossEntropy_criterion(logits, true_labels)
dice_loss = calculate_dice_loss(logits,true_labels)
loss = dice_loss
# Back propagation
unet.zero_grad()
loss.backward()
optimizer.step()
loss_list.append(loss.item())
```