resnet中间层添加空间注意力的代码

在ResNet中添加空间注意力的代码可以使用CBAM（Convolutional Block Attention Module）来实现。CBAM是一种结合了空间和通道注意力的混合域注意力机制，可以有效提升网络性能。

以下是在ResNet中添加CBAM模块的代码示例：

import torch
import torch.nn as nn

class CBAM(nn.Module):
    def __init__(self, channels, reduction=16):
        super(CBAM, self).__init__()
        self.spatial_attention = SpatialAttention()
        self.channel_attention = ChannelAttention(channels, reduction)

    def forward(self, x):
        x = self.spatial_attention(x)
        x = self.channel_attention(x)
        return x

class SpatialAttention(nn.Module):
    def __init__(self):
        super(SpatialAttention, self).__init__()
        self.conv = nn.Conv2d(2, 1, kernel_size=7, stride=1, padding=3)
        self.sigmoid = nn.Sigmoid()

    def forward(self, x):
        avg_pool = torch.mean(x, dim=1, keepdim=True)
        max_pool = torch.max(x, dim=1, keepdim=True)[0]
        pool = torch.cat([avg_pool, max_pool], dim=1)
        attention = self.conv(pool)
        attention = self.sigmoid(attention)
        x = x * attention
        return x

class ChannelAttention(nn.Module):
    def __init__(self, channels, reduction=16):
        super(ChannelAttention, self).__init__()
        self.avg_pool = nn.AdaptiveAvgPool2d(1)
        self.max_pool = nn.AdaptiveMaxPool2d(1)
        self.fc = nn.Sequential(
            nn.Linear(channels, channels // reduction),
            nn.ReLU(inplace=True),
            nn.Linear(channels // reduction, channels)
        )
        self.sigmoid = nn.Sigmoid()

    def forward(self, x):
        avg_pool = self.avg_pool(x).squeeze()
        max_pool = self.max_pool(x).squeeze()
        avg_attention = self.fc(avg_pool)
        max_attention = self.fc(max_pool)
        attention = self.sigmoid(avg_attention + max_attention).unsqueeze(2).unsqueeze(3)
        x = x * attention
        return x

# 在ResNet中添加CBAM模块
class ResNetCBAM(nn.Module):
    def __init__(self, block, layers, num_classes=1000):
        super(ResNetCBAM, self).__init__()
        self.inplanes = 64
        self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False)
        self.bn1 = nn.BatchNorm2d(64)
        self.relu = nn.ReLU(inplace=True)
        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
        self.layer1 = self._make_layer(block, 64, layers[0])
        self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
        self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
        self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
        self.fc = nn.Linear(512 * block.expansion, num_classes)
        self.cbam = CBAM(512 * block.expansion)

    def _make_layer(self, block, planes, blocks, stride=1):
        downsample = None
        if stride != 1 or self.inplanes != planes * block.expansion:
            downsample = nn.Sequential(
                nn.Conv2d(self.inplanes, planes * block.expansion, kernel_size=1, stride=stride, bias=False),
                nn.BatchNorm2d(planes * block.expansion),
            )

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

        return nn.Sequential(*layers)

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

        x = self.cbam(x)

        return x

# 使用ResNetCBAM模型
model = ResNetCBAM(Bottleneck, [3, 4, 6, 3])

这段代码定义了一个CBAM模块，其中包括空间注意力和通道注意力两部分。然后，在ResNet的每个残差块中添加了CBAM模块。最后，定义了一个使用ResNetCBAM模型的示例。