resnet18+交叉注意力机制代码

以下是使用ResNet18和交叉注意力机制的代码示例：

import torch
import torch.nn as nn

class CrossAttentionBlock(nn.Module):
    def __init__(self, in_channels):
        super(CrossAttentionBlock, self).__init__()
        self.conv1 = nn.Conv2d(in_channels, in_channels, kernel_size=1, stride=1, padding=0)
        self.conv2 = nn.Conv2d(in_channels, in_channels, kernel_size=1, stride=1, padding=0)
        self.softmax = nn.Softmax(dim=-1)
        
    def forward(self, x):
        out = x
        batch_size, channels, height, width = x.size()
        
        # Global average pooling
        f = x.mean(dim=-1).mean(dim=-1).unsqueeze(-1) # (batch_size, channels, 1)
        
        # Query, key, value
        q = self.conv1(x).view(batch_size, -1, width*height).transpose(1, 2) # (batch_size, width*height, channels)
        k = self.conv2(x).view(batch_size, -1, width*height) # (batch_size, channels, width*height)
        v = x.view(batch_size, channels, -1) # (batch_size, channels, width*height)
        
        # Cross-Attention
        qk = torch.bmm(q, k) # (batch_size, width*height, width*height)
        a = self.softmax(qk) # (batch_size, width*height, width*height)
        out = torch.bmm(v, a.transpose(1, 2)) # (batch_size, channels, width*height)
        out = out.view(batch_size, channels, height, width)
        
        # Residual connection
        out = out + x
        
        return out

class ResNet18_CrossAttention(nn.Module):
    def __init__(self, num_classes=10):
        super(ResNet18_CrossAttention, self).__init__()
        self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn1 = nn.BatchNorm2d(64)
        self.relu = nn.ReLU(inplace=True)
        self.layer1 = nn.Sequential(
            nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1, bias=False),
            nn.BatchNorm2d(64),
            nn.ReLU(inplace=True),
            CrossAttentionBlock(64),
            nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1, bias=False),
            nn.BatchNorm2d(64),
            nn.ReLU(inplace=True),
            CrossAttentionBlock(64)
        )
        self.layer2 = nn.Sequential(
            nn.Conv2d(64, 128, kernel_size=3, stride=2, padding=1, bias=False),
            nn.BatchNorm2d(128),
            nn.ReLU(inplace=True),
            CrossAttentionBlock(128),
            nn.Conv2d(128, 128, kernel_size=3, stride=1, padding=1, bias=False),
            nn.BatchNorm2d(128),
            nn.ReLU(inplace=True),
            CrossAttentionBlock(128)
        )
        self.layer3 = nn.Sequential(
            nn.Conv2d(128, 256, kernel_size=3, stride=2, padding=1, bias=False),
            nn.BatchNorm2d(256),
            nn.ReLU(inplace=True),
            CrossAttentionBlock(256),
            nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1, bias=False),
            nn.BatchNorm2d(256),
            nn.ReLU(inplace=True),
            CrossAttentionBlock(256)
        )
        self.layer4 = nn.Sequential(
            nn.Conv2d(256, 512, kernel_size=3, stride=2, padding=1, bias=False),
            nn.BatchNorm2d(512),
            nn.ReLU(inplace=True),
            CrossAttentionBlock(512),
            nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1, bias=False),
            nn.BatchNorm2d(512),
            nn.ReLU(inplace=True),
            CrossAttentionBlock(512)
        )
        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
        self.fc = nn.Linear(512, num_classes)

    def forward(self, x):
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu(x)
        x = self.layer1(x)
        x = self.layer2(x)
        x = self.layer3(x)
        x = self.layer4(x)
        x = self.avgpool(x)
        x = x.view(x.size(0), -1)
        x = self.fc(x)
        return x

这里我们添加了一个名为CrossAttentionBlock的模块，它实现了交叉注意力机制，用于增强ResNet18的特征表示能力。在ResNet18_CrossAttention中，我们将CrossAttentionBlock插入到ResNet18的每个阶段中，以获得更强的特征表示能力。