I’m dealing with segmentation now.
I want to use with cross_entropy_loss and dice_loss. The form of input shape is[4,512,512], having value of 0,1 in indices and the output form is [4,2,512,512].

masks_shape --> torch.Size([4, 512, 512])
output_masks_shape --> torch.Size([4, 2,512, 512])

I have got the dice_loss function from another site.
def dice_loss(input, target):
    smooth = 1.
    loss = 0.
    for c in range(n_classes):
           iflat = input[:, c ].view(-1)
           tflat = target[:, c].view(-1)
           intersection = (iflat * tflat).sum()
           
           w = class_weights[c]
           loss += w*(1 - ((2. * intersection + smooth) /
                             (iflat.sum() + tflat.sum() + smooth)))
    return loss

How to reshape input shape [4,512,512] to [4,2,512,512] including indices(0,1)??
And I want to use class_weights because I’m dealing with an unbalanced classes.
How to calculate class_weights??