标签:Loss num Center dim self batch classes size
center loss来自ECCV2016的一篇论文:A Discriminative Feature Learning Approach for Deep Face Recognition
公式:
其中, x指的是特征,cyi指的是第yi个类别的中心,c会随着模型训练更新,类中心数=类别数;
m表示mini-batch的大小, 因此这个公式就是希望一个batch中的每个样本的feature离feature 的 中心的距离的平方和要越小越好,也就是类内距离要越小越好。
实现代码:
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, use_gpu=True):
super(CenterLoss, self).__init__()
self.num_classes = num_classes
self.feat_dim = feat_dim
self.use_gpu = use_gpu
if self.use_gpu:
self.centers = nn.Parameter(torch.randn(self.num_classes, self.feat_dim).cuda())
else:
self.centers = nn.Parameter(torch.randn(self.num_classes, self.feat_dim))
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))
dist = distmat * mask.float()
loss = dist.clamp(min=1e-12, max=1e+12).sum() / batch_size
return loss
标签:Loss,num,Center,dim,self,batch,classes,size 来源: https://blog.csdn.net/weixin_42486139/article/details/121149924
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