yolov8的主干网络代码

这是一个简化的 YOLOv4 主干网络代码示例，用于解释网络的结构和实现思路：

import torch
import torch.nn as nn

# 定义主干网络的卷积块
def conv_block(in_channels, out_channels, kernel_size, stride, padding):
    return nn.Sequential(
        nn.Conv2d(in_channels, out_channels, kernel_size, stride, padding),
        nn.BatchNorm2d(out_channels),
        nn.LeakyReLU(0.1, inplace=True)
    )

class YOLOv8Backbone(nn.Module):
    def __init__(self):
        super(YOLOv8Backbone, self).__init__()

        # 第一个卷积块
        self.conv1 = conv_block(3, 32, 3, 1, 1)
        self.pool1 = nn.MaxPool2d(2, 2)

        # 第二个卷积块
        self.conv2 = conv_block(32, 64, 3, 1, 1)
        self.pool2 = nn.MaxPool2d(2, 2)

        # 第三个卷积块
        self.conv3 = conv_block(64, 128, 3, 1, 1)
        self.conv4 = conv_block(128, 64, 1, 1, 0)
        self.conv5 = conv_block(64, 128, 3, 1, 1)
        self.pool3 = nn.MaxPool2d(2, 2)

        # 第四个卷积块
        self.conv6 = conv_block(128, 256, 3, 1, 1)
        self.conv7 = conv_block(256, 128, 1, 1, 0)
        self.conv8 = conv_block(128, 256, 3, 1, 1)
        self.pool4 = nn.MaxPool2d(2, 2)

        # 第五个卷积块
        self.conv9 = conv_block(256, 512, 3, 1, 1)
        self.conv10 = conv_block(512, 256, 1, 1, 0)
        self.conv11 = conv_block(256, 512, 3, 1, 1)
        self.conv12 = conv_block(512, 256, 1, 1, 0)
        self.conv13 = conv_block(256, 512, 3, 1, 1)

        # 第六个卷积块
        self.conv14 = conv_block(512, 1024, 3, 1, 1)
        self.conv15 = conv_block(1024, 512, 1, 1, 0)
        self.conv16 = conv_block(512, 1024, 3, 1, 1)
        self.conv17 = conv_block(1024, 512, 1, 1, 0)
        self.conv18 = conv_block(512, 1024, 3, 1, 1)

    def forward(self, x):
        x = self.conv1(x)
        x = self.pool1(x)

        x = self.conv2(x)
        x = self.pool2(x)

        x = self.conv3(x)
        x = self.conv4(x)
        x = self.conv5(x)
        x = self.pool3(x)

        x = self.conv6(x)
        x = self.conv7(x)
        x = self.conv8(x)
        x = self.pool4(x)

        x = self.conv9(x)
        x = self.conv10(x)
        x = self.conv11(x)
        x = self.conv12(x)
        conv13_output = self.conv13(x)

        x = self.conv14(conv13_output)
        x = self.conv15(x)
        x = self.conv16(x)
        x = self.conv17(x)
        conv18_output = self.conv18(x)

        return conv13_output, conv18_output

这段代码是 YOLOv8 的主干网络代码示例，它使用了一系列卷积块和池化层来提取图像特征。具体的网络结构和参数可以根据实际需求进行调整和修改。注意这只是一个简化的示例，真正的 YOLOv8 网络结构可能更为复杂。