def forward(self, input, target):
"""
Expand to one hot added extra for consistency reasons
"""
# import ipdb;ipdb.set_trace()
target = expand_as_one_hot(target.long(), self.classes)
assert input.dim() == target.dim() == 5, "'input' and 'target' have different number of dims"
if self.skip_index_after is not None:
before_size = target.size()
target = self.skip_target_channels(target, self.skip_index_after)
# print("Target {} after skip index {}".format(before_size, target.size()))
assert input.size() == target.size(), "'input' and 'target' must have the same shape"
# get probabilities from logits
input = self.normalization(input)
# compute per channel Dice coefficient
per_channel_dice = self.dice(input, target, weight=self.weight)
loss = (1. - torch.mean(per_channel_dice))
per_channel_dice = per_channel_dice.detach().cpu().numpy()
# average Dice score across all channels/classes
return loss, per_channel_dice
def forward(self, input, target):
target = expand_as_one_hot(target, self.classes)
assert input.size() == target.size(
), "'input' and 'target' must have the same shape"
l1 = super().forward(input, target)
if self.apply_below_threshold:
mask = target < self.threshold
else:
mask = target >= self.threshold
l1[mask] = l1[mask] * self.weight
return l1.mean()
def forward(self, inputs, targets, weight=None):
assert isinstance(inputs, list)
# if there is just one output head the 'inputs' is going to be a singleton list [tensor]
# and 'targets' is just going to be a tensor (that's how the HDF5Dataloader works)
# so wrap targets in a list in this case
if len(inputs) == 1:
targets = [targets]
assert len(inputs) == len(targets) == len(self.tags_coefficients)
loss = 0
for input, target, alpha in zip(inputs, targets,
self.tags_coefficients):
"""
New code here: add expand for consistency
"""
target = expand_as_one_hot(target, self.classes)
assert input.size() == target.size(
), "'input' and 'target' must have the same shape"
loss += alpha * square_angular_loss(input, target, weight)
return loss
def forward(self, input, target):
target_expanded = expand_as_one_hot(target.long(), self.classes)
assert input.size() == target_expanded.size(), "'input' and 'target' must have the same shape"
loss_1 = self.alpha * self.bce(input, target_expanded)
loss_2, channel_score = self.beta * self.dice(input, target_expanded)
return (loss_1+loss_2) , channel_score