基于pytorch的arcface中包含ArcFace函数的models.py文件代码

以下是基于PyTorch的ArcFace模型的models.py文件代码：

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


class Flatten(nn.Module):
    def forward(self, x):
        return x.view(x.size(0), -1)


class ConvBlock(nn.Module):
    def __init__(self, in_channels, out_channels):
        super(ConvBlock, self).__init__()
        self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1)
        self.bn1 = nn.BatchNorm2d(out_channels)
        self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1)
        self.bn2 = nn.BatchNorm2d(out_channels)
        self.conv3 = nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1)
        self.bn3 = nn.BatchNorm2d(out_channels)
        self.relu = nn.ReLU(inplace=True)

    def forward(self, x):
        identity = x

        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)

        out = self.conv2(out)
        out = self.bn2(out)
        out = self.relu(out)

        out = self.conv3(out)
        out = self.bn3(out)
        out += identity
        out = self.relu(out)

        return out


class ResNet(nn.Module):
    def __init__(self, block, layers, num_classes=10):
        super(ResNet, self).__init__()
        self.in_channels = 64

        self.conv1 = nn.Conv2d(3, 64, kernel_size=3, padding=1)
        self.bn1 = nn.BatchNorm2d(64)
        self.relu = nn.ReLU(inplace=True)
        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)

        self.layer1 = self.make_layer(block, 64, layers[0])
        self.layer2 = self.make_layer(block, 128, layers[1], stride=2)
        self.layer3 = self.make_layer(block, 256, layers[2], stride=2)
        self.layer4 = self.make_layer(block, 512, layers[3], stride=2)

        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
        self.flatten = Flatten()
        self.fc = nn.Linear(512 * block.expansion, num_classes)

    def make_layer(self, block, out_channels, blocks, stride=1):
        layers = []

        layers.append(block(self.in_channels, out_channels, stride))
        self.in_channels = out_channels * block.expansion

        for i in range(1, blocks):
            layers.append(block(self.in_channels, out_channels))

        return nn.Sequential(*layers)

    def forward(self, x):
        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)
        out = self.maxpool(out)

        out = self.layer1(out)
        out = self.layer2(out)
        out = self.layer3(out)
        out = self.layer4(out)

        out = self.avgpool(out)
        out = self.flatten(out)
        out = self.fc(out)

        return out


class ArcFaceLoss(nn.Module):
    def __init__(self, embedding_size, num_classes, scale=30, margin=0.5):
        super(ArcFaceLoss, self).__init__()
        self.embedding_size = embedding_size
        self.num_classes = num_classes
        self.scale = scale
        self.margin = margin

        self.weight = nn.Parameter(torch.Tensor(embedding_size, num_classes))
        nn.init.xavier_normal_(self.weight)

    def forward(self, x, labels):
        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
        phi = phi.type_as(cosine)

        one_hot = torch.zeros(cosine.size(), device=x.device)
        one_hot.scatter_(1, labels.view(-1, 1).long(), 1)
        output = (one_hot * phi) + ((1.0 - one_hot) * cosine)
        output *= self.scale

        return output


class ArcFaceNet(nn.Module):
    def __init__(self, block, layers, num_classes, embedding_size=512):
        super(ArcFaceNet, self).__init__()
        self.resnet = ResNet(block, layers, num_classes=num_classes)
        self.arc_face_loss = ArcFaceLoss(embedding_size, num_classes)

    def forward(self, x, labels):
        x = self.resnet(x)
        output = self.arc_face_loss(x, labels)
        return output

其中，`ResNet`是一个标准的ResNet模型，`ArcFaceLoss`是实现ArcFace损失函数的模型，`ArcFaceNet`是将ResNet和ArcFaceLoss结合在一起的模型。这些模型都可以通过在初始化时传递不同的参数来进行修改和调整。