mirror of
https://github.com/Laurent2916/REVA-QCAV.git
synced 2024-11-12 16:18:25 +00:00
36 lines
1.1 KiB
Python
36 lines
1.1 KiB
Python
|
import torch
|
||
|
|
from torch.nn.modules.loss import _Loss
|
||
|
|
from torch.autograd import Function
|
||
|
|
import torch.nn.functional as F
|
||
|
|
|
||
|
|
class DiceCoeff(Function):
|
||
|
|
|
||
|
|
def forward(ctx, input, target):
|
||
|
|
ctx.save_for_backward(input, target)
|
||
|
|
ctx.inter = torch.dot(input, target) + 0.0001
|
||
|
|
ctx.union = torch.sum(input) + torch.sum(target) + 0.0001
|
||
|
|
|
||
|
|
t = 2*ctx.inter.float()/ctx.union.float()
|
||
|
|
return t
|
||
|
|
|
||
|
|
# This function has only a single output, so it gets only one gradient
|
||
|
|
def backward(ctx, grad_output):
|
||
|
|
|
||
|
|
input, target = ctx.saved_variables
|
||
|
|
grad_input = grad_target = None
|
||
|
|
|
||
|
|
if self.needs_input_grad[0]:
|
||
|
|
grad_input = grad_output * 2 * (target * ctx.union + ctx.inter) \
|
||
|
|
/ ctx.union * ctx.union
|
||
|
|
if self.needs_input_grad[1]:
|
||
|
|
grad_target = None
|
||
|
|
|
||
|
|
return grad_input, grad_target
|
||
|
|
|
||
|
|
def dice_coeff(input, target):
|
||
|
|
return DiceCoeff().forward(input, target)
|
||
|
|
|
||
|
|
class DiceLoss(_Loss):
|
||
|
|
def forward(self, input, target):
|
||
|
|
return 1 - dice_coeff(F.sigmoid(input), target)
|