能够向我示例在ResNet50的代码中添加注意力机制吗

当你在ResNet50中添加注意力机制时，你需要添加两个组件：注意力模块和修改后的ResNet50。以下是一个示例代码：

import torch.nn as nn
import torch

class ChannelAttention(nn.Module):
    def __init__(self, in_planes, ratio=16):
        super(ChannelAttention, self).__init__()
        self.avg_pool = nn.AdaptiveAvgPool2d(1)
        self.max_pool = nn.AdaptiveMaxPool2d(1)

        self.fc1   = nn.Conv2d(in_planes, in_planes // 16, 1, bias=False)
        self.relu1 = nn.ReLU()
        self.fc2   = nn.Conv2d(in_planes // 16, in_planes, 1, bias=False)

        self.sigmoid = nn.Sigmoid()

    def forward(self, x):
        avg_out = self.fc2(self.relu1(self.fc1(self.avg_pool(x))))
        max_out = self.fc2(self.relu1(self.fc1(self.max_pool(x))))
        out = avg_out + max_out
        return self.sigmoid(out)

class ResidualBlockWithAttention(nn.Module):
    def __init__(self, in_channels, out_channels, stride=1):
        super().__init__()

        self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=stride, padding=1, bias=False)
        self.bn1 = nn.BatchNorm2d(out_channels)

        self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn2 = nn.BatchNorm2d(out_channels)

        self.ca = ChannelAttention(out_channels)

        if stride != 1 or in_channels != out_channels:
            self.shortcut = nn.Sequential(
                nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=stride, bias=False),
                nn.BatchNorm2d(out_channels)
            )
        else:
            self.shortcut = nn.Sequential()

    def forward(self, x):
        out = self.conv1(x)
        out = self.bn1(out)
        out = nn.ReLU()(out)

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

        out = self.ca(out) * out
        out += self.shortcut(x)

        out = nn.ReLU()(out)

        return out

class ResNet50WithAttention(nn.Module):
    def __init__(self, num_classes=1000):
        super().__init__()

        self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False)
        self.bn1 = nn.BatchNorm2d(64)
        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)

        self.layer1 = nn.Sequential(
            ResidualBlockWithAttention(64, 256, stride=1),
            ResidualBlockWithAttention(256, 256),
            ResidualBlockWithAttention(256, 256)
        )

        self.layer2 = nn.Sequential(
            ResidualBlockWithAttention(256, 512, stride=2),
            ResidualBlockWithAttention(512, 512),
            ResidualBlockWithAttention(512, 512),
            ResidualBlockWithAttention(512, 512)
        )

        self.layer3 = nn.Sequential(
            ResidualBlockWithAttention(512, 1024, stride=2),
            ResidualBlockWithAttention(1024, 1024),
            ResidualBlockWithAttention(1024, 1024),
            ResidualBlockWithAttention(1024, 1024),
            ResidualBlockWithAttention(1024, 1024),
            ResidualBlockWithAttention(1024, 1024)
        )

        self.layer4 = nn.Sequential(
            ResidualBlockWithAttention(1024, 2048, stride=2),
            ResidualBlockWithAttention(2048, 2048),
            ResidualBlockWithAttention(2048, 2048),
        )

        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
        self.fc = nn.Linear(2048, num_classes)

    def forward(self, x):
        x = self.conv1(x)
        x = self.bn1(x)
        x = nn.ReLU()(x)
        x = self.maxpool(x)

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

        x = self.avgpool(x)
        x = torch.flatten(x, 1)
        x = self.fc(x)

        return x

在这个示例中，我们定义了两个类。第一个是ChannelAttention类，它采用平均池化和最大池化来计算通道注意力，并将两者相加以得到最终的通道注意力。第二个是ResidualBlockWithAttention类，它是一个修改后的残差块，其中添加了通道注意力模块。第三个是ResNet50WithAttention类，它是一个修改后的ResNet50，其中包含具有注意力机制的ResidualBlockWithAttention。

在ResNet50WithAttention类中，我们首先定义了一个标准的ResNet50架构，然后将每个残差块替换为具有注意力机制的ResidualBlockWithAttention。这个新的ResNet50WithAttention类就可以用于训练你的图像分类模型了。