INNER CODE UNIT · Python
distmat
KaiyangZhou/pytorch-center-loss · center_loss.py:32
distmat = torch.pow(x, 2).sum(dim=1, keepdim=True).expand(batch_size, self.num_classes) + \
torch.pow(self.centers, 2).sum(dim=1, keepdim=True).expand(self.num_classes, batch_size).t()
distmat.addmm_(1, -2, x, self.centers.t())
classes = torch.arange(self.num_classes).long()
if self.use_gpu: classes = classes.cuda()
labels = labels.unsqueeze(1).expand(batch_size, self.num_classes)
mask = labels.eq(classes.expand(batch_size, self.num_classes))
dist = distmat * mask.float()
loss = dist.clamp(min=1e-12, max=1e+12).sum() / batch_size
return loss