在pytorch框架yolov4中添加se模块

在PyTorch框架中，要在Yolov4中添加SE模块，可以按照以下步骤进行操作：

1. 导入必要的库和模块

import torch
import torch.nn as nn

2. 定义SE模块

class SEModule(nn.Module):
    def __init__(self, in_channels, reduction=16):
        super(SEModule, 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

3. 在Yolov4中应用SE模块

在Yolov4网络的定义中，可以在每个卷积层之后添加SE模块。例如，在CSPDarknet53中，可以按照以下方式添加SE模块：

class CSPBlock(nn.Module):
    def __init__(self, in_channels, out_channels, num_blocks, use_se=True):
        super(CSPBlock, self).__init__()
        self.downsample_conv = ConvBlock(in_channels, out_channels, kernel_size=3, stride=2)
        self.split_conv = ConvBlock(out_channels, out_channels, kernel_size=1, stride=1)
        self.blocks_conv = nn.Sequential(*[ResidualBlock(out_channels, use_se=use_se) for _ in range(num_blocks)])
        self.concat_conv = ConvBlock(out_channels * 2, out_channels, kernel_size=1, stride=1)

    def forward(self, x):
        x = self.downsample_conv(x)
        x = torch.split(x, x.shape[1] // 2, dim=1)
        x = self.split_conv(x[0]), self.blocks_conv(x[1])
        x = torch.cat(x, dim=1)
        return self.concat_conv(x)

在ResidualBlock中，也可以添加SE模块：

class ResidualBlock(nn.Module):
    def __init__(self, channels, use_se=True):
        super(ResidualBlock, self).__init__()
        self.conv1 = ConvBlock(channels, channels // 2, kernel_size=1, stride=1)
        self.conv2 = ConvBlock(channels // 2, channels, kernel_size=3, stride=1)
        if use_se:
            self.se = SEModule(channels)
        else:
            self.se = None

    def forward(self, x):
        residual = x
        x = self.conv1(x)
        x = self.conv2(x)
        if self.se is not None:
            x = self.se(x)
        x += residual
        return x

通过以上操作，就可以在Yolov4中添加SE模块了。