帮我用pytorch搭建一个添加SE注意力模块的vgg16卷积神经网络的代码

好的，我可以回答这个问题。以下是使用 PyTorch 搭建 VGG16 卷积神经网络并添加 SE 注意力模块的代码：

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

class SEBlock(nn.Module):
    def __init__(self, in_channels, reduction_ratio=16):
        super(SEBlock, self).__init__()
        self.avg_pool = nn.AdaptiveAvgPool2d(1)
        self.fc1 = nn.Linear(in_channels, in_channels // reduction_ratio)
        self.fc2 = nn.Linear(in_channels // reduction_ratio, in_channels)
        
    def forward(self, x):
        b, c, _, _ = x.size()
        y = self.avg_pool(x).view(b, c)
        y = F.relu(self.fc1(y))
        y = self.fc2(y).sigmoid().view(b, c, 1, 1)
        return x * y

class VGG16(nn.Module):
    def __init__(self, num_classes=100):
        super(VGG16, self).__init__()
        self.features = nn.Sequential(
            nn.Conv2d(3, 64, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.Conv2d(64, 64, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=2, stride=2),
            nn.Conv2d(64, 128, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.Conv2d(128, 128, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=2, stride=2),
            nn.Conv2d(128, 256, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.Conv2d(256, 256, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.Conv2d(256, 256, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=2, stride=2),
            nn.Conv2d(256, 512, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.Conv2d(512, 512, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.Conv2d(512, 512, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=2, stride=2),
            nn.Conv2d(512, 512, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.Conv2d(512, 512, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.Conv2d(512, 512, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=2, stride=2),
        )
        self.avgpool = nn.AdaptiveAvgPool2d((7, 7))
        self.classifier = nn.Sequential(
            nn.Linear(512 * 7 * 7, 4096),
            nn.ReLU(inplace=True),
            nn.Dropout(),
            nn.Linear(4096, 4096),
            nn.ReLU(inplace=True),
            nn.Dropout(),
            nn.Linear(4096, num_classes),
        )
        self.se_block1 = SEBlock(64)
        self.se_block2 = SEBlock(128)
        self.se_block3 = SEBlock(256)
        self.se_block4 = SEBlock(512)
        self.se_block5 = SEBlock(512)
        
    def forward(self, x):
        x = self.se_block1(self.features(x))
        x = self.se_block2(self.features(x))
        x = self.se_block3(self.features(x))
        x = self.se_block4(self.features(x))
        x = self.se_block5(self.features(x))
        x = self.avgpool(x)
        x = torch.flatten(x, 1)
        x = self.classifier(x)
        return x

这个代码实现了一个带有 SE 注意力模块的 VGG16 卷积神经网络。SE 注意力模块可以增强网络对重要特征的关注，从而提高网络的性能。