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43 lines
1.2 KiB
Python
43 lines
1.2 KiB
Python
import torch
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from torch.autograd import Function
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class DiceCoeff(Function):
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"""Dice coeff for individual examples"""
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def forward(self, input, target):
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self.save_for_backward(input, target)
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eps = 0.0001
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self.inter = torch.dot(input.view(-1), target.view(-1))
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self.union = torch.sum(input) + torch.sum(target) + eps
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t = (2 * self.inter.float() + eps) / self.union.float()
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return t
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# This function has only a single output, so it gets only one gradient
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def backward(self, grad_output):
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input, target = self.saved_variables
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grad_input = grad_target = None
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if self.needs_input_grad[0]:
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grad_input = grad_output * 2 * (target * self.union - self.inter) \
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/ (self.union * self.union)
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if self.needs_input_grad[1]:
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grad_target = None
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return grad_input, grad_target
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def dice_coeff(input, target):
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"""Dice coeff for batches"""
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if input.is_cuda:
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s = torch.FloatTensor(1).cuda().zero_()
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else:
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s = torch.FloatTensor(1).zero_()
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for i, c in enumerate(zip(input, target)):
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s = s + DiceCoeff().forward(c[0], c[1])
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return s / (i + 1)
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