Dice coefficient loss function in PyTorch

Dice_coeff_loss.py

def dice_loss(pred, target):
    """This definition generalize to real valued pred and target vector.
This should be differentiable.

    pred: tensor with first dimension as batch
    target: tensor with first dimension as batch

    """

    smooth = 1.

    # have to use contiguous since they may from a torch.view op
    iflat = pred.contiguous().view(-1)
    tflat = target.contiguous().view(-1)
    intersection = (iflat * tflat).sum()

    A_sum = torch.sum(tflat * iflat)
    B_sum = torch.sum(tflat * tflat)
    
    return 1 - ((2. * intersection + smooth) / (A_sum + B_sum + smooth) )

I think there is some wrong in line 17. It should be $A_sum = torch.sum(iflat*iflat)$.
And i have a question, Do you have any constrain for pred, like$sigmoid(pred)$?

I think there is some wrong in line 17. It should be $A_sum = torch.sum(iflat*iflat)$.
And i have a question, Do you have any constrain for pred, like$sigmoid(pred)$?

The author's code is correct, and there is a detailed discussion here: Dice Loss PR #1249

As mentioned by @youngfly11. Line 17 seems to be wrong

Yes line 17 is wrong

a typo at line 17. It should be something like this:

A_sum = torch.sum(iflat * iflat) 

hey guys, i understand how this can be generalized to multiple classes that have been one-hot encoded - however in pytorch, gt classes for segmentation don't have to be one-hot encoded so how does everyone go about using this gdl for segmentation?

This din't work for me. I ended up with this code:

def dice_loss(pred,target):
    numerator = 2 * torch.sum(pred * target)
    denominator = torch.sum(pred + target)
    return 1 - (numerator + 1) / (denominator + 1)

This is wrong:
A_sum = torch.sum(tflat * iflat)
B_sum = torch.sum(tflat * tflat)
You want to find the sum of pixels/voxels in target and prediction separately for the denominator, not a union. Should be smth like:
A_sum = torch.sum(tflat)
B_sum = torch.sum(iflat)
But pay attention to the dimensions which you are summing it along.

@weiliu620 how to enable autograd fr this?