class MyNet(nn.Module): def init(self): super(MyNet, self).init() self.vgg16 = vgg16(pretrained=True) self.resnet18 = resnet18(pretrained=True) self.vgg16.classifier = nn.Identity() self.resnet18.fc = nn.Identity() self.fc = nn.Linear(25600, 2) def forward(self, x): x1 = self.vgg16(x) x2 = self.resnet18(x) x1 = x1.view(x1.size(0), -1) x2 = x2.view(x2.size(0), -1) x = torch.cat((x1, x2), dim=1) x = self.fc(x) return x 将以上代码加入CBAM注意力机制

可以按照以下方式将CBAM注意力机制加入到MyNet模型中：

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

# 导入CBAM注意力机制的模块
from cbam import CBAM

class MyNet(nn.Module):
    def __init__(self):
        super(MyNet, self).__init__()
        
        # 加载预训练的vgg16和resnet18模型
        self.vgg16 = torch.hub.load('pytorch/vision:v0.6.0', 'vgg16', pretrained=True)
        self.resnet18 = torch.hub.load('pytorch/vision:v0.6.0', 'resnet18', pretrained=True)
        
        # 将vgg16和resnet18的全连接层替换成Identity层
        self.vgg16.classifier = nn.Identity()
        self.resnet18.fc = nn.Identity()
        
        # 添加CBAM注意力机制
        self.cbam1 = CBAM(512)
        self.cbam2 = CBAM(512)
        
        # 定义全连接层
        self.fc = nn.Linear(25600, 2)

    def forward(self, x):
        # 使用vgg16和resnet18提取特征，并进行CBAM注意力机制的处理
        x1 = self.vgg16.features(x)
        x2 = self.resnet18.conv1(x)
        x2 = self.resnet18.bn1(x2)
        x2 = self.resnet18.relu(x2)
        x2 = self.resnet18.maxpool(x2)
        x2 = self.resnet18.layer1(x2)
        x2 = self.resnet18.layer2(x2)
        x2 = self.resnet18.layer3(x2)
        x2 = self.resnet18.layer4(x2)
        x2 = self.cbam1(x2)
        x2 = F.adaptive_avg_pool2d(x2, (1, 1))
        x2 = x2.view(x2.size(0), -1)
        x1 = x1.view(x1.size(0), -1)
        x1 = self.cbam2(x1)
        x1 = x1.view(x1.size(0), -1)
        x = torch.cat((x1, x2), dim=1)
        
        # 使用全连接层进行分类
        x = self.fc(x)
        return x

这里我们使用了一个名为CBAM的注意力机制模块，它可以对输入的特征图进行通道注意力和空间注意力处理，从而提高模型的性能。我们将CBAM模块添加到了vgg16和resnet18模型的输出上，并将输出结果进行拼接，然后再通过一个全连接层将其映射到2维输出。需要注意的是，这里我们使用了pytorch提供的预训练模型，所以需要先安装pytorch。