pytorh两个向量距离的平方(center loss)

即 center-loss:
https://github.com/KaiyangZhou/pytorch-center-loss/blob/master/center_loss.py

def forward(self, x, labels):
        """
        Args:
            x: feature matrix with shape (batch_size, feat_dim).
            labels: ground truth labels with shape (batch_size).
        """
        batch_size = x.size(0)
        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))
     	# mask : [batch_size,num_classes]
      
        dist = []
        for i in range(batch_size):
            value = distmat[i][mask[i]]   ## 选出 classes正确的 ，取出其距离值
            value = value.clamp(min=1e-12, max=1e+12) # for numerical stability
            dist.append(value)
        dist = torch.cat(dist)
        loss = dist.mean()    # 求均值？ 原论文只求和，没有均值。

        return loss

https://pytorch.org/docs/stable/torch.html#torch.addmm

>>> M = torch.randn(2, 3)
>>> mat1 = torch.randn(2, 3)
>>> mat2 = torch.randn(3, 3)
>>> torch.addmm(M, mat1, mat2)
tensor([[-4.8716,  1.4671, -1.3746],
        [ 0.7573, -3.9555, -2.8681]])

torch cat:

>>> x = torch.randn(2, 3)
>>> x
tensor([[ 0.6580, -1.0969, -0.4614],
        [-0.1034, -0.5790,  0.1497]])
>>> torch.cat((x, x, x), 0)
tensor([[ 0.6580, -1.0969, -0.4614],
        [-0.1034, -0.5790,  0.1497],
        [ 0.6580, -1.0969, -0.4614],
        [-0.1034, -0.5790,  0.1497],
        [ 0.6580, -1.0969, -0.4614],
        [-0.1034, -0.5790,  0.1497]])
>>> torch.cat((x, x, x), 1)
tensor([[ 0.6580, -1.0969, -0.4614,  0.6580, -1.0969, -0.4614,  0.6580,
         -1.0969, -0.4614],
        [-0.1034, -0.5790,  0.1497, -0.1034, -0.5790,  0.1497, -0.1034,
         -0.5790,  0.1497]])

• https://zhuanlan.zhihu.com/p/38235452
  • 总而言之，center就像一个参数一样，先随机初始化，然后再每个迭代后在当前类别中更新一次。
  • ？？ center 随机初始化？？有没有大神解释下……