I can across this implementation of Dice:
from torch.autograd import Function, Variable

class DiceCoeff(Function):
“”“Dice coeff for individual examples”""
def forward(self, input, target):
    self.save_for_backward(input, target)
    eps = 0.0001
    self.inter = torch.dot(input.view(-1), target.view(-1))
    self.union = torch.sum(input) + torch.sum(target) + eps

    t = (2 * self.inter.float() + eps) / self.union.float()
    return t

# This function has only a single output, so it gets only one gradient
def backward(self, grad_output):

    input, target = self.saved_variables
    grad_input = grad_target = None

    if self.needs_input_grad[0]:
        grad_input = grad_output * 2 * (target * self.union - self.inter) \
                     / (self.union * self.union)
    if self.needs_input_grad[1]:
        grad_target = None

    return grad_input, grad_target


Uptill now, my dice score had no backward method, it was just the regular view(-1) and intersection over union. Does that mean that nothing was being learnt as backward method was not implemented?