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36 lines
1.1 KiB
Python
36 lines
1.1 KiB
Python
import torch
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from torch.nn.modules.loss import _Loss
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from torch.autograd import Function
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import torch.nn.functional as F
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class DiceCoeff(Function):
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def forward(ctx, input, target):
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ctx.save_for_backward(input, target)
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ctx.inter = torch.dot(input, target) + 0.0001
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ctx.union = torch.sum(input) + torch.sum(target) + 0.0001
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t = 2*ctx.inter.float()/ctx.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(ctx, grad_output):
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input, target = ctx.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 * ctx.union + ctx.inter) \
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/ ctx.union * ctx.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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return DiceCoeff().forward(input, target)
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class DiceLoss(_Loss):
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def forward(self, input, target):
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return 1 - dice_coeff(F.sigmoid(input), target)
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