yolov5的代码注释

以下是YOLOv5的代码注释示例：

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

class YOLOv5(nn.Module):
    def __init__(self, num_classes=80):
        super(YOLOv5, self).__init__()

        # 初始化网络结构
        self.conv1 = nn.Conv2d(3, 32, kernel_size=3, stride=1, padding=1)
        self.conv2 = nn.Conv2d(32, 64, kernel_size=3, stride=2, padding=1)
        self.conv3 = nn.Conv2d(64, 128, kernel_size=3, stride=2, padding=1)
        self.conv4 = nn.Conv2d(128, 256, kernel_size=3, stride=2, padding=1)
        self.conv5 = nn.Conv2d(256, 512, kernel_size=3, stride=2, padding=1)
        self.conv6 = nn.Conv2d(512, 1024, kernel_size=3, stride=2, padding=1)
        
        # 其他网络层...

        # 最后的卷积层输出
        self.final_conv = nn.Conv2d(1024, num_classes, kernel_size=1)

    def forward(self, x):
        x = F.relu(self.conv1(x))
        x = F.relu(self.conv2(x))
        x = F.relu(self.conv3(x))
        x = F.relu(self.conv4(x))
        x = F.relu(self.conv5(x))
        x = F.relu(self.conv6(x))
        
        # 其他网络层的前向传播...

        x = self.final_conv(x)
        
        return x

以上是一个简化的YOLOv5模型的代码注释示例。具体的代码内容会根据实际情况有所变化，这里只是提供了一个基本的框架。