Hi
I am trying to integrate dice loss with my unet model, the dice is loss is borrowed from other task.This is what it looks like
class GeneralizedDiceLoss(nn.Module):
"""Computes Generalized Dice Loss (GDL) as described in https://arxiv.org/pdf/1707.03237.pdf
"""
def __init__(self, epsilon=1e-5, weight=None, ignore_index=None, sigmoid_normalization=True):
super(GeneralizedDiceLoss, self).__init__()
self.epsilon = epsilon
self.register_buffer('weight', weight)
self.ignore_index = ignore_index
if sigmoid_normalization:
self.normalization = nn.Sigmoid()
else:
self.normalization = nn.Softmax(dim=1)
def forward(self, input, target):
# get probabilities from logits
input = input.float()
input = self.normalization(input)
assert input.size() == target.size(), "'input' and 'target' must have the same shape"
# mask ignore_index if present
if self.ignore_index is not None:
mask = target.clone().ne_(self.ignore_index)
mask.requires_grad = False
input = input * mask
target = target * mask
input = flatten(input)
target = flatten(target)
target = target.float()
target_sum = target.sum(-1)
class_weights = Variable(1. / (target_sum * target_sum).clamp(min=self.epsilon), requires_grad=False)
intersect = (input * target).sum(-1) * class_weights
if self.weight is not None:
weight = Variable(self.weight, requires_grad=False)
intersect = weight * intersect
intersect = intersect.sum()
denominator = ((input + target).sum(-1) * class_weights).sum()
return 1. - 2. * intersect / denominator.clamp(min=self.epsilon)
def flatten(tensor):
"""Flattens a given tensor such that the channel axis is first.
The shapes are transformed as follows:
(N, C, D, H, W) -> (C, N * D * H * W)
"""
C = tensor.size(1)
# new axis order
axis_order = (1, 0) + tuple(range(2, tensor.dim()))
# Transpose: (N, C, D, H, W) -> (C, N, D, H, W)
transposed = tensor.permute(axis_order)
# Flatten: (C, N, D, H, W) -> (C, N * D * H * W)
return transposed.view(C, -1)
and this is what my training code looks like
for index,(image, mask) in enumerate(train_loader):
image = torch.unsqueeze(image,0).float().cuda()
label = mask.cuda()
# label = label.view(-1,)
output_1, output_2 = model(image)
max_output = torch.argmax(output_2, 1)
loss = loss_function(max_output,label)#max output shape =torch.Size([1, 128, 128, 128]) and labe shape = torch.Size([1, 128, 128, 128])
print(loss)
losses.append(loss.item())
optimizer = optim.Adam(model.parameters())
optimizer.zero_grad()
loss.backward()
but when i run this i get
Traceback (most recent call last):
File "train.py", line 89, in <module>
loss.backward()
File "/home/bubbles/.local/lib/python3.6/site-packages/torch/tensor.py", line 107, in backward
torch.autograd.backward(self, gradient, retain_graph, create_graph)
File "/home/bubbles/.local/lib/python3.6/site-packages/torch/autograd/__init__.py", line 93, in backward
allow_unreachable=True) # allow_unreachable flag
RuntimeError: element 0 of tensors does not require grad and does not have a grad_fn
I am not sure how to move forward, any suggestions would be really helpful .
Thanks in advance