coordinate attention怎样加在resnet18的什么位置，请给我相关代码

在将注意力机制引入ResNet18之前，需要先定义Coordinate Attention Layer。以下是Coordinate Attention Layer的代码：

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

class CALayer(nn.Module):
    def __init__(self, channel, reduction=16):
        super(CALayer, self).__init__()
        self.avg_pool = nn.AdaptiveAvgPool2d(1)
        self.conv_du = nn.Sequential(
            nn.Conv2d(channel, channel // reduction, 1, padding=0, bias=True),
            nn.ReLU(inplace=True),
            nn.Conv2d(channel // reduction, channel, 1, padding=0, bias=True),
            nn.Sigmoid()
        )

    def forward(self, x):
        y = self.avg_pool(x)
        y = self.conv_du(y)
        return x * y

然后，在ResNet18中添加Coordinate Attention Layer，代码如下：

import torch.nn as nn
import torchvision.models as models

class ResNet18_CA(nn.Module):
    def __init__(self, num_classes=1000):
        super(ResNet18_CA, self).__init__()
        self.resnet18 = models.resnet18(pretrained=True)
        self.ca = CALayer(512)
        self.fc = nn.Linear(512, num_classes)

    def forward(self, x):
        x = self.resnet18.conv1(x)
        x = self.resnet18.bn1(x)
        x = self.resnet18.relu(x)
        x = self.resnet18.maxpool(x)

        x = self.resnet18.layer1(x)
        x = self.resnet18.layer2(x)
        x = self.resnet18.layer3(x)
        x = self.resnet18.layer4(x)

        x = self.ca(x)

        x = self.resnet18.avgpool(x)
        x = x.view(x.size(0), -1)
        x = self.fc(x)
        return x

在这个例子中，我们将Coordinate Attention Layer添加到ResNet18的最后一个残差块中。更具体地说，我们将其添加到ResNet18的第四个残差块中，这个残差块包括两个基本块。在这个代码中，我们只使用了一个Coordinate Attention Layer，它被应用于整个残差块中的输出。