class CSPDarkNet(nn.Module):

CSPDarkNet 是一个深度神经网络模型，它是 YOLOv4 目标检测算法的基础模型之一，其核心是 CSP 模块（Cross Stage Partial Network）。它具有以下特点：

1. 使用 CSP 模块分离卷积计算，减少了计算量和参数数量。
2. 采用 DarkNet53 作为主干网络，具有较高的精度和速度。
3. 通过 SPP、PAN 等技术增强了模型的感受野和多尺度特征表达能力。
4. 基于 YOLOv4 的思想，使用 Mish 激活函数和多尺度训练等技术进一步提升了精度。

下面是 CSPDarkNet 的代码实现：

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

class CSPBlock(nn.Module):
    def __init__(self, in_channels, out_channels, n=1, shortcut=True):
        super(CSPBlock, self).__init__()
        self.shortcut = shortcut
        hidden_channels = out_channels // 2
        self.conv1 = nn.Conv2d(in_channels, hidden_channels, 1, bias=False)
        self.bn1 = nn.BatchNorm2d(hidden_channels)
        self.conv2 = nn.Conv2d(in_channels, hidden_channels, 1, bias=False)
        self.bn2 = nn.BatchNorm2d(hidden_channels)
        self.conv3 = nn.Conv2d(hidden_channels, hidden_channels, 3, padding=1, groups=n, bias=False)
        self.bn3 = nn.BatchNorm2d(hidden_channels)
        self.conv4 = nn.Conv2d(hidden_channels, hidden_channels, 1, bias=False)
        self.bn4 = nn.BatchNorm2d(hidden_channels)
        self.conv5 = nn.Conv2d(hidden_channels, hidden_channels, 3, padding=1, groups=n, bias=False)
        self.bn5 = nn.BatchNorm2d(hidden_channels)
        self.conv6 = nn.Conv2d(hidden_channels, out_channels, 1, bias=False)
        self.bn6 = nn.BatchNorm2d(out_channels)
        self.act = nn.LeakyReLU(0.1, inplace=True)

    def forward(self, x):
        if self.shortcut:
            shortcut = x
        else:
            shortcut = 0
        x1 = self.conv1(x)
        x1 = self.bn1(x1)
        x1 = self.act(x1)
        x2 = self.conv2(x)
        x2 = self.bn2(x2)
        x2 = self.act(x2)
        x3 = self.conv3(x2)
        x3 = self.bn3(x3)
        x3 = self.act(x3)
        x4 = self.conv4(x3)
        x4 = self.bn4(x4)
        x4 = self.act(x4)
        x5 = self.conv5(x4)
        x5 = self.bn5(x5)
        x5 = self.act(x5)
        x6 = self.conv6(x5)
        x6 = self.bn6(x6)
        x6 = self.act(x6)
        out = torch.cat([x1, x6], dim=1)
        return out + shortcut

class CSPDarkNet(nn.Module):
    def __init__(self, num_classes=80):
        super(CSPDarkNet, self).__init__()
        self.stem = nn.Sequential(
            nn.Conv2d(3, 32, 3, padding=1, bias=False),
            nn.BatchNorm2d(32),
            nn.LeakyReLU(0.1, inplace=True),
            nn.Conv2d(32, 64, 3, stride=2, padding=1, bias=False),
            nn.BatchNorm2d(64),
            nn.LeakyReLU(0.1, inplace=True)
        )
        self.layer1 = nn.Sequential(
            CSPBlock(64, 64, n=1, shortcut=False),
            *[CSPBlock(64, 64, n=1) for _ in range(1, 3)]
        )
        self.layer2 = nn.Sequential(
            CSPBlock(64, 128, n=2, shortcut=False),
            *[CSPBlock(128, 128, n=2) for _ in range(1, 9)]
        )
        self.layer3 = nn.Sequential(
            CSPBlock(128, 256, n=4, shortcut=False),
            *[CSPBlock(256, 256, n=4) for _ in range(1, 9)]
        )
        self.layer4 = nn.Sequential(
            CSPBlock(256, 512, n=8, shortcut=False),
            *[CSPBlock(512, 512, n=8) for _ in range(1, 5)],
            nn.Conv2d(512, 1024, 1, bias=False),
            nn.BatchNorm2d(1024),
            nn.LeakyReLU(0.1, inplace=True)
        )
        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
        self.fc = nn.Linear(1024, num_classes)

    def forward(self, x):
        x = self.stem(x)
        x = self.layer1(x)
        x = self.layer2(x)
        x = self.layer3(x)
        x = self.layer4(x)
        x = self.avgpool(x)
        x = x.view(x.size(0), -1)
        x = self.fc(x)
        return x