yolov5网络结构代码

以下是YOLOv5网络结构的代码示例：

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

class Conv(nn.Module):
    def __init__(self, in_channels, out_channels, kernel_size=1, stride=1, padding=None, groups=1, bias=False):
        super(Conv, self).__init__()
        if padding is None:
            padding = kernel_size // 2 if stride == 1 else 0
        self.conv = nn.Conv2d(in_channels, out_channels, kernel_size, stride, padding, groups=groups, bias=bias)
        self.bn = nn.BatchNorm2d(out_channels)
        self.activation = nn.ReLU()

    def forward(self, x):
        x = self.conv(x)
        x = self.bn(x)
        x = self.activation(x)
        return x

class Bottleneck(nn.Module):
    def __init__(self, in_channels, out_channels, shortcut=True, groups=1):
        super(Bottleneck, self).__init__()
        mid_channels = int(in_channels * 0.5)
        self.conv1 = Conv(in_channels, mid_channels, kernel_size=1)
        self.conv2 = Conv(mid_channels, in_channels, kernel_size=3, padding=1)
        self.conv3 = Conv(in_channels, mid_channels, kernel_size=1)
        self.conv4 = Conv(mid_channels, out_channels, kernel_size=3, padding=1)
        self.shortcut = shortcut and in_channels == out_channels
        if self.shortcut:
            self.shortcut_conv = Conv(in_channels, out_channels, kernel_size=1)
        
    def forward(self, x):
        identity = x
        x = self.conv1(x)
        x = self.conv2(x)
        x = self.conv3(x)
        x = self.conv4(x)
        
        if self.shortcut:
            identity = self.shortcut_conv(identity)
        
        x += identity
        return x

class YOLOv5(nn.Module):
    def __init__(self, num_classes=80):
        super(YOLOv5, self).__init__()
        self.backbone = nn.Sequential(
            Conv(3, 32, kernel_size=3, stride=1),
            Bottleneck(32, 64),
            nn.MaxPool2d(2, 2),
            Bottleneck(64, 128),
            nn.MaxPool2d(2, 2),
            Bottleneck(128, 256),
            nn.MaxPool2d(2, 2),
            Bottleneck(256, 512),
            nn.MaxPool2d(2, 2),
            Bottleneck(512, 1024),
        )
        
        self.neck = nn.Sequential(
            Conv(1024, 512, kernel_size=1),
            Conv(512, 1024, kernel_size=3, padding=1),
            Conv(1024, 512, kernel_size=1),
            Conv(512, 1024, kernel_size=3, padding=1),
            Conv(1024, 512, kernel_size=1),
        )

        self.head = nn.Sequential(
            Conv(512, 1024, kernel_size=3, padding=1),
            nn.Conv2d(1024, num_classes, kernel_size=1)
        )

    def forward(self, x):
        x = self.backbone(x)
        x = self.neck(x)
        x = self.head(x)
        x = F.adaptive_avg_pool2d(x, (1, 1))
        x = torch.flatten(x, 1)
        return x

model = YOLOv5()

请注意，这只是YOLOv5的基本网络结构示例，可能与实际的YOLOv5网络结构代码有所不同。实际的YOLOv5网络结构可能包含其他组件和层。这里的代码仅提供了一个大致的框架。