yolov7主干网络具体代码

以下是YOLOv7主干网络的具体代码（使用PyTorch实现）：

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

class CSPBlock(nn.Module):
    def __init__(self, in_channels, out_channels, num_blocks, split=False):
        super(CSPBlock, self).__init__()
        self.split = split
        self.block = nn.Sequential(
            nn.Conv2d(in_channels, out_channels, 1, bias=False),
            nn.BatchNorm2d(out_channels),
            nn.ReLU(inplace=True),
            nn.Conv2d(out_channels, out_channels, 3, padding=1, groups=out_channels, bias=False),
            nn.BatchNorm2d(out_channels),
            nn.ReLU(inplace=True),
            nn.Conv2d(out_channels, out_channels, 1, bias=False),
            nn.BatchNorm2d(out_channels),
            nn.ReLU(inplace=True),
        )
        self.skip = nn.Sequential(
            nn.Conv2d(in_channels, out_channels, 1, bias=False),
            nn.BatchNorm2d(out_channels),
            nn.ReLU(inplace=True),
        )
        self.blocks = nn.Sequential(
            *[nn.Sequential(
                nn.Conv2d(out_channels, out_channels, 1, bias=False),
                nn.BatchNorm2d(out_channels),
                nn.ReLU(inplace=True),
                self.block,
            ) for _ in range(num_blocks)]
        )

    def forward(self, x):
        y = self.skip(x)
        x = self.block(x)
        x = self.blocks(x)
        x = torch.cat([x, y], dim=1)
        return x if self.split else F.interpolate(x, scale_factor=2)
        
class CSPDarknet53(nn.Module):
    def __init__(self, num_classes=1000):
        super(CSPDarknet53, self).__init__()
        self.stem = nn.Sequential(
            nn.Conv2d(3, 32, 3, padding=1, bias=False),
            nn.BatchNorm2d(32),
            nn.ReLU(inplace=True),
            nn.Conv2d(32, 64, 3, stride=2, padding=1, bias=False),
            nn.BatchNorm2d(64),
            nn.ReLU(inplace=True),
            CSPBlock(64, 64, num_blocks=1),
            nn.Conv2d(64, 128, 3, stride=2, padding=1, bias=False),
            nn.BatchNorm2d(128),
            nn.ReLU(inplace=True),
            CSPBlock(128, 128, num_blocks=2),
            nn.Conv2d(128, 256, 3, stride=2, padding=1, bias=False),
            nn.BatchNorm2d(256),
            nn.ReLU(inplace=True),
            CSPBlock(256, 256, num_blocks=8),
            nn.Conv2d(256, 512, 3, stride=2, padding=1, bias=False),
            nn.BatchNorm2d(512),
            nn.ReLU(inplace=True),
            CSPBlock(512, 512, num_blocks=8),
            nn.Conv2d(512, 1024, 3, stride=2, padding=1, bias=False),
            nn.BatchNorm2d(1024),
            nn.ReLU(inplace=True),
            CSPBlock(1024, 1024, num_blocks=4, split=True),
        )
        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
        self.fc = nn.Linear(1024, num_classes)

    def forward(self, x):
        x = self.stem(x)
        x = self.avgpool(x)
        x = x.view(x.size(0), -1)
        x = self.fc(x)
        return x

其中，`CSPBlock`表示CSP块，`CSPDarknet53`表示CSPDarknet53主干网络。在`CSPDarknet53`中，采用了一系列卷积、归一化和激活操作，以及多个CSP块来提取特征。最后，通过全局平均池化和全连接层将特征映射到目标类别。