REVA-QCAV/myloss.py

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#
# myloss.py : implementation of the Dice coeff and the associated loss
#
import torch
import torch.nn.functional as F
from torch.nn.modules.loss import _Loss
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)
self.inter = torch.dot(input, target) + 0.0001
self.union = torch.sum(input) + torch.sum(target) + 0.0001
t = 2*self.inter.float()/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 = Variable(torch.FloatTensor(1).cuda().zero_())
else:
s = Variable(torch.FloatTensor(1).zero_())
for i, c in enumerate(zip(input, target)):
s = s + DiceCoeff().forward(c[0], c[1])
return s / (i+1)
class DiceLoss(_Loss):
def forward(self, input, target):
return 1 - dice_coeff(F.sigmoid(input), target)