def forward(self, input, target):
for i in range(target.shape[0]):
if not torch.sum(target[i]).data.cpu().numpy() > 1:
target[i] = -1
input = input.squeeze(1)
target = target.squeeze(1)
loss = lovasz_hinge(input, target, per_image=True, ignore=-1)
return loss
def forward(self, input, target):
input = input.squeeze(1)
target = target.squeeze(1)
loss = lovasz_hinge(input, target, per_image=True)
return loss
def forward(self, input, target):
loss = L.lovasz_hinge(input, target)
return loss
def criterion(logits, targets):
logits = logits.squeeze(1)
targets = targets.squeeze(1)
loss = lovasz_hinge(logits, targets, per_image=True, ignore=None)
return loss
