[人工智能]pytorch centerloss代码注释

代码是网上找的，运行出来效果不错

class CenterLoss(nn.Module):
    """Center loss.

    Reference:
    Wen et al. A Discriminative Feature Learning Approach for Deep Face Recognition. ECCV 2016.

    Args:
        num_classes (int): number of classes.
        feat_dim (int): feature dimension.
    """

    def __init__(self, num_classes=10, feat_dim=2 ):
        super(CenterLoss, self).__init__()
        self.num_classes = num_classes
        self.feat_dim = feat_dim

        self.centers = nn.Parameter(torch.randn(self.num_classes, self.feat_dim).cuda())

    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()) # x + (-2) * x * center.t  -->  [batchsize,num_classes]
        #上面部分是计算欧几里得距离的公式(x-center)^2的展开

        classes = torch.arange(self.num_classes).long()
        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

其中distmat为每个样本在每个类别的距离，长为batchsize，宽为classnum。
模拟各个环节的计算结果可以更好的理解该代码。

label为样本的真实标签。

通过下列式子可以得到一个mask矩阵，有助于之后取出每个样本真实类别的距离。


可见，将距离矩阵中的真实距离取出来了

之后将所有值加起来求平均值就是要求的loss值了。