yolov5c3模块

yolov5c3模块是YOLOv5中的一个模块，它是由C3模块和SPP模块组成的。C3模块是一个卷积块，由三个卷积层组成，每个卷积层都有一个1x1卷积层和一个3x3卷积层。SPP模块是一个空间金字塔池化模块，它可以对不同大小的特征图进行池化，从而提取更多的特征信息。yolov5c3模块的作用是在YOLOv5中提取特征，从而实现目标检测。

以下是yolov5c3模块的代码实现：

import torch.nn as nn

class C3(nn.Module):
    def __init__(self, ch, n):
        super(C3, self).__init__()
        self.conv1 = nn.Conv2d(ch, n, 1, 1)
        self.conv2 = nn.Conv2d(n, ch, 1, 1)
        self.conv3 = nn.Conv2d(ch, n, 1, 1)
        self.conv4 = nn.Conv2d(n, ch, 1, 1)
        self.conv5 = nn.Conv2d(ch, n, 1, 1)
        
    def forward(self, x):
        out = self.conv1(x)
        out = self.conv2(out)
        out1 = self.conv3(out)
        out2 = self.conv4(out1)
        out2 = self.conv5(out2)
        out = torch.cat([out1, out2], dim=1)
        return out

class SPP(nn.Module):
    def __init__(self, ch, pool_sizes=[5, 9, 13]):
        super(SPP, self).__init__()
        self.pool1 = nn.MaxPool2d(pool_sizes[0], 1, padding=pool_sizes[0]//2)
        self.pool2 = nn.MaxPool2d(pool_sizes[1], 1, padding=pool_sizes[1]//2)
        self.pool3 = nn.MaxPool2d(pool_sizes[2], 1, padding=pool_sizes[2]//2)
        self.conv = nn.Conv2d(ch * 4, ch, 1, 1)
        
    def forward(self, x):
        out1 = self.pool1(x)
        out2 = self.pool2(x)
        out3 = self.pool3(x)
        out = torch.cat([x, out1, out2, out3], dim=1)
        out = self.conv(out)
        return out

class YOLOv5C3(nn.Module):
    def __init__(self, ch, n, pool_sizes=[5, 9, 13]):
        super(YOLOv5C3, self).__init__()
        self.c3 = C3(ch, n)
        self.spp = SPP(n*2, pool_sizes)
        
    def forward(self, x):
        out = self.c3(x)
        out = self.spp(out)
        return out