densenet加入注意力机制代码

这里给出使用Pytorch实现DenseNet-121加入SE模块的代码示例：

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

class SELayer(nn.Module):
    def __init__(self, in_channels, reduction=16):
        super(SELayer, self).__init__()
        self.avg_pool = nn.AdaptiveAvgPool2d(1)
        self.fc = nn.Sequential(
            nn.Linear(in_channels, in_channels // reduction, bias=False),
            nn.ReLU(inplace=True),
            nn.Linear(in_channels // reduction, in_channels, bias=False),
            nn.Sigmoid()
        )
        
    def forward(self, x):
        b, c, _, _ = x.size()
        y = self.avg_pool(x).view(b, c)
        y = self.fc(y).view(b, c, 1, 1)
        return x * y

class Bottleneck(nn.Module):
    def __init__(self, in_channels, growth_rate):
        super(Bottleneck, self).__init__()
        self.bn1 = nn.BatchNorm2d(in_channels)
        self.conv1 = nn.Conv2d(in_channels, 4 * growth_rate, kernel_size=1, bias=False)
        self.bn2 = nn.BatchNorm2d(4 * growth_rate)
        self.conv2 = nn.Conv2d(4 * growth_rate, growth_rate, kernel_size=3, padding=1, bias=False)
        self.se = SELayer(4 * growth_rate)
        
    def forward(self, x):
        out = self.conv1(F.relu(self.bn1(x)))
        out = self.conv2(F.relu(self.bn2(out)))
        out = self.se(out)
        out = torch.cat([out, x], 1)
        return out

class DenseNet(nn.Module):
    def __init__(self, growth_rate=32, block_config=(6, 12, 24, 16), num_classes=1000):
        super(DenseNet, self).__init__()
        self.features = nn.Sequential(
            nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False),
            nn.BatchNorm2d(64),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
        )
        in_channels = 64
        for i, num_blocks in enumerate(block_config):
            for j in range(num_blocks):
                block = Bottleneck(in_channels, growth_rate)
                self.features.add_module('block%d_%d' % (i+1, j+1), block)
                in_channels += growth_rate
            if i != len(block_config) - 1:
                self.features.add_module('transition%d' % (i+1), nn.Sequential(
                    nn.BatchNorm2d(in_channels),
                    nn.Conv2d(in_channels, in_channels // 2, kernel_size=1, bias=False),
                    nn.AvgPool2d(kernel_size=2, stride=2)
                ))
        self.features.add_module('norm5', nn.BatchNorm2d(in_channels))
        self.classifier = nn.Linear(in_channels, num_classes)
        
    def forward(self, x):
        features = self.features(x)
        out = F.relu(features, inplace=True)
        out = F.adaptive_avg_pool2d(out, (1, 1)).view(features.size(0), -1)
        out = self.classifier(out)
        return out

def densenet121_se(pretrained=False, **kwargs):
    model = DenseNet(growth_rate=32, block_config=(6, 12, 24, 16), **kwargs)
    if pretrained:
        model.load_state_dict(torch.load('densenet121_se.pth'), strict=False)
    return model

在上述代码中，我们定义了一个SELayer类，用于实现SE模块。在Bottleneck类中，我们将SE模块添加到了Bottleneck块中。最后，我们定义了densenet121_se函数，用于创建DenseNet-121模型，并加载预训练的权重（如果需要）。