在ResNet50网络的每个stage之后插入Shuffle Attention，给出代码演示并详细解释

在ResNet50网络的每个stage之后插入Shuffle Attention的代码演示：

import torch.nn as nn
from torchvision.models.resnet import ResNet, BasicBlock, Bottleneck

class ShuffleAttention(nn.Module):
    def __init__(self, channels, reduction=16):
        super(ShuffleAttention, self).__init__()
        self.avg_pool = nn.AdaptiveAvgPool2d(1)
        self.max_pool = nn.AdaptiveMaxPool2d(1)
        self.fc1 = nn.Conv2d(channels, channels // reduction, kernel_size=1, padding=0)
        self.relu = nn.ReLU(inplace=True)
        self.fc2 = nn.Conv2d(channels // reduction, channels, kernel_size=1, padding=0)
        self.sigmoid = nn.Sigmoid()

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

class ResNetWithShuffleAttention(ResNet):
    def __init__(self, block, layers, num_classes=1000, zero_init_residual=False):
        super(ResNetWithShuffleAttention, self).__init__(block, layers, num_classes=num_classes, zero_init_residual=zero_init_residual)
        
        self.att1 = ShuffleAttention(256)
        self.att2 = ShuffleAttention(512)
        self.att3 = ShuffleAttention(1024)
        self.att4 = ShuffleAttention(2048)

        # Replace the original blocks with the new blocks
        self.layer1 = self._make_layer(block, 64, layers[0])
        self.layer2 = self._make_layer(block, 128, layers[1], stride=2, dilate=False)
        self.layer3 = self._make_layer(block, 256, layers[2], stride=2, dilate=False)
        self.layer4 = self._make_layer(block, 512, layers[3], stride=2, dilate=False)

    def _make_layer(self, block, planes, blocks, stride=1, dilate=False):
        norm_layer = self._norm_layer
        downsample = None
        previous_dilation = self.dilation
        if dilate:
            self.dilation *= stride
            stride = 1
        if stride != 1 or self.inplanes != planes * block.expansion:
            downsample = nn.Sequential(
                conv1x1(self.inplanes, planes * block.expansion, stride),
                norm_layer(planes * block.expansion),
            )

        layers = []
        layers.append(block(self.inplanes, planes, stride, downsample, self.groups,
                            self.base_width, previous_dilation, norm_layer))
        self.inplanes = planes * block.expansion
        for _ in range(1, blocks):
            layers.append(block(self.inplanes, planes, groups=self.groups,
                                base_width=self.base_width, dilation=self.dilation,
                                norm_layer=norm_layer))

        # Add Shuffle Attention after each stage
        if planes == 64:
            layers.append(self.att1)
        elif planes == 128:
            layers.append(self.att2)
        elif planes == 256:
            layers.append(self.att3)
        elif planes == 512:
            layers.append(self.att4)

        return nn.Sequential(*layers)

在这个代码中，我们首先定义了一个ShuffleAttention模块，然后将其插入到ResNet50网络的每个stage之后。在_ResNetWithShuffleAttention_类中，我们重写了_ResNet_类的_make_layer方法，并在每个stage中添加了ShuffleAttention模块。

在_make_layer方法中，我们首先检查当前的planes参数（即每个stage的输出通道数），然后创建一个包含ShuffleAttention的新层。最后，我们将新层添加到该stage中。

ShuffleAttention模块中，我们使用了AdaptiveAvgPool2d和AdaptiveMaxPool2d来对输入进行全局平均池化和全局最大池化。然后，我们使用两个卷积层和ReLU激活函数来提取特征。最后，我们使用Sigmoid激活函数来产生一个0到1之间的数值，以便控制输入的权重。在forward方法中，我们首先对输入进行全局平均池化和全局最大池化，并将它们连接起来。然后，我们将连接后的特征图通过两个卷积层和ReLU激活函数处理，最后使用Sigmoid激活函数产生一个权重矩阵。最终，我们将原始输入和权重矩阵相乘，从而获得加权后的特征。