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