circle loss pytorch
时间: 2023-06-22 19:22:03 浏览: 44
Circle loss是一种损失函数,用于训练人脸识别模型。它的核心思想是将同一类别的样本聚在一起,同时使得不同类别的样本之间距离尽可能远。在PyTorch中,可以使用以下代码实现Circle Loss:
```python
import torch
import torch.nn as nn
import torch.nn.functional as F
class CircleLoss(nn.Module):
def __init__(self, m, s):
super(CircleLoss, self).__init__()
self.m = m
self.s = s
def forward(self, x, y):
# x: feature vector, y: label
# x shape: [batch_size, feature_dim]
# y shape: [batch_size]
batch_size = x.size(0)
feature_dim = x.size(1)
# normalize feature vector
x_norm = F.normalize(x, dim=1)
# calculate similarity matrix
sim_matrix = torch.matmul(x_norm, x_norm.t())
# calculate mask matrix
mask_matrix = torch.zeros_like(sim_matrix)
for i in range(batch_size):
mask_matrix[i, y[i]] = 1
# calculate positive pairs and negative pairs
pos_pairs = sim_matrix * mask_matrix
neg_pairs = sim_matrix * (1 - mask_matrix)
# calculate alpha and margin
alpha = torch.clamp_min(-pos_pairs.detach() + 1 + self.m, min=0)
margin = torch.clamp_min(neg_pairs.detach() + self.m, min=0)
# calculate final loss
delta = 1 - self.m
sp = torch.exp(-self.s * (pos_pairs - delta))
sn = torch.exp(self.s * (margin - neg_pairs))
sp_sum = torch.sum(sp, dim=1, keepdim=True)
loss = torch.log(1 + sp_sum * sn).mean()
return loss
```
其中,m和s是超参数,可以根据具体情况进行调整。在训练过程中,可以将Circle Loss与其他常见的损失函数(如交叉熵损失)进行组合,以提高模型的性能。