基于pytorch的arcface人脸识别系统中包含ArcFace函数的models.py文件代码

以下是基于PyTorch的ArcFace人脸识别系统中包含ArcFace函数的models.py文件代码：

import torch
import torch.nn as nn
import torch.nn.functional as F

class ArcFace(nn.Module):
    def __init__(self, in_features, out_features, s=30.0, m=0.50, easy_margin=False):
        super(ArcFace, self).__init__()
        self.in_features = in_features
        self.out_features = out_features
        self.s = s
        self.m = m
        self.weight = nn.Parameter(torch.FloatTensor(out_features, in_features))
        nn.init.xavier_uniform_(self.weight)
        self.easy_margin = easy_margin

    def forward(self, x, label):
        cosine = F.linear(F.normalize(x), F.normalize(self.weight))
        sine = torch.sqrt(1.0 - torch.pow(cosine, 2))
        phi = cosine * self.cos_m - sine * self.sin_m
        if self.easy_margin:
            phi = torch.where(cosine > 0, phi, cosine)
        else:
            phi = torch.where(cosine > self.th, phi, cosine - self.mm)
        one_hot = torch.zeros(cosine.size(), device='cuda')
        one_hot.scatter_(1, label.view(-1, 1).long(), 1)
        output = (one_hot * phi) + ((1.0 - one_hot) * cosine)
        output *= self.s
        return output

    def __repr__(self):
        return self.__class__.__name__ + '(' \
               + 'in_features=' + str(self.in_features) \
               + ', out_features=' + str(self.out_features) \
               + ', s=' + str(self.s) \
               + ', m=' + str(self.m) + ')'

该代码实现了ArcFace函数，其中包括了前向传播（forward）函数和初始化（init）函数。在前向传播函数中，通过输入的x和label计算出cosine和sine，然后利用ArcFace的公式计算出phi，再根据是否启用easy_margin来调整phi的值。最后，根据one_hot和phi的值计算出输出output并乘以s。初始化函数中定义了所需的超参数和权重矩阵，并使用xavier_uniform_函数初始化权重。