UniRepLKNet_SPPF

### UniRepLKNet_SPPF 结构解析

#### 解析 UniRepLKNet 和 SPPF 的组合结构

`UniRepLKNet_SPPF` 是一种结合了 `UniRepLKNet` 和空间金字塔池化模块 (SPPF) 的网络架构。这种设计旨在增强模型对于不同尺度特征的捕捉能力，从而提高检测精度。

#### 代码实现示例

以下是 Python 中基于 PyTorch 实现的一个简化版 `UniRepLKNet_SPPF` 架构：

import torch.nn as nn


class RepBlock(nn.Module):
    """重复卷积块"""
    
    def __init__(self, in_channels, out_channels, kernel_size=3, stride=1, padding=1):
        super(RepBlock, self).__init__()
        self.conv = nn.Conv2d(in_channels, out_channels, kernel_size, stride, padding)
        self.bn = nn.BatchNorm2d(out_channels)
        self.act = nn.ReLU(inplace=True)

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


class SPPF(nn.Module):
    """空间金字塔池化聚焦层"""

    def __init__(self, c1, c2, k=5):  
        super().__init__()
        c_ = c1 // 2  # hidden channels
        self.cv1 = RepBlock(c1, c_, 1, 1)
        self.cv2 = RepBlock(c_ * 4, c2, 1, 1)
        self.m = nn.MaxPool2d(kernel_size=k, stride=1, padding=k // 2)

    def forward(self, x):
        x = self.cv1(x)
        y1 = self.m(x)
        y2 = self.m(y1)
        return self.cv2(torch.cat([x, y1, y2, self.m(y2)], 1))


class UniRepLKNet(nn.Module):

    def __init__(self, num_classes=80):
        super(UniRepLKNet, self).__init__()

        base_channels = 64
        
        self.backbone = nn.Sequential(
            RepBlock(3, base_channels),
            RepBlock(base_channels, base_channels*2, stride=2), 
            RepBlock(base_channels*2, base_channels*4, stride=2),
            RepBlock(base_channels*4, base_channels*8, stride=2),
            RepBlock(base_channels*8, base_channels*16, stride=2))

        self.sppf = SPPF(base_channels*16, base_channels*16)

        self.head = nn.Linear(base_channels*16, num_classes)

    def forward(self, x):
        x = self.backbone(x)
        x = self.sppf(x)
        x = nn.functional.adaptive_avg_pool2d(x, (1, 1))
        x = x.view(x.size(0), -1)
        return self.head(x)


if __name__ == '__main__':
    model = UniRepLKNet()
    print(model)

此段代码定义了一个简单的 `UniRepLKNet` 模型并集成了 `SPPF` 层来处理输入图像数据[^1]。