Would you please show me how to fix this? I changed it like this:
  def forward(self, logits, labels):
        N, C, H, W = logits.size()
        n_pixs = N * H * W
        logits = logits.permute(0, 2, 3, 1).contiguous().view(-1, C)
        with torch.no_grad():
            scores = F.softmax(logits, dim=1).cpu().detach()
            labels = labels.view(-1)
            labels_cpu = labels.cpu().detach()
            invalid_mask = labels_cpu==self.ignore_lb
            labels_cpu[invalid_mask] = 0
            picks = scores[torch.arange(n_pixs), labels_cpu]
            picks[invalid_mask] = 1
            sorteds, inds = torch.sort(picks)
            thresh = self.thresh if sorteds[self.n_min]<self.thresh else sorteds[n_min]
            labels[picks>thresh] = self.ignore_lb
        loss = self.criteria(logits, labels)
        return loss

But the problem still exists