技术标签： 深度学习 pytorch 神经网络
人脸识别ArcFace损失函数(代码)
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# 实现方式1 class ArcLoss1(nn.Module): def __init__(self, class_num, feature_num, s=10, m=0.1): super().__init__() self.class_num = class_num self.feature_num = feature_num self.s = s self.m = torch.tensor(m) self.w = nn.Parameter(torch.rand(feature_num, class_num)) # 2*10 def forward(self, feature): feature = F.normalize(feature, dim=1) # 128*2 w = F.normalize(self.w, dim=0) # 2*10 cos_theat = torch.matmul(feature, w) / 10 sin_theat = torch.sqrt(1.0 - torch.pow(cos_theat, 2)) cos_theat_m = cos_theat * torch.cos(self.m) - sin_theat * torch.sin(self.m) cos_theat_ = torch.exp(cos_theat * self.s) sum_cos_theat = torch.sum(torch.exp(cos_theat * self.s), dim=1, keepdim=True) - cos_theat_ top = torch.exp(cos_theat_m * self.s) divide = (top / (top + sum_cos_theat)) return divide # 实现方式2 class ArcLoss2(nn.Module): def __init__(self, feature_dim=2, cls_dim=10): super().__init__() self.W = nn.Parameter(torch.randn(feature_dim, cls_dim)) def forward(self, feature, m=1, s=10): x = F.normalize(feature, dim=1) w = F.normalize(self.W, dim=0) cos = torch.matmul(x, w)/10 a = torch.acos(cos) top = torch.exp(s*torch.cos(a+m)) down2 = torch.sum(torch.exp(s*torch.cos(a)), dim=1, keepdim=True)-torch.exp(s*torch.cos(a)) out = torch.log(top/(top+down2)) return out |
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转载请注明原文链接：人脸识别ArcFace损失函数
转载请注明原文链接：人脸识别ArcFace损失函数
