import torch 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 def dice_coeff(input, target): """Dice coeff for batches""" if input.is_cuda: s = torch.FloatTensor(1).cuda().zero_() else: s = torch.FloatTensor(1).zero_() for i, c in enumerate(zip(input, target)): s = s + DiceCoeff().forward(c[0], c[1]) return s / (i + 1)