1.CSPdarknet网络结构的实现（python）

CSPdarknet的网络结构如下图所示（该图来自别的博客【目标检测】yolov5模型详解-CSDN博客），是下图最左边的input和下面长方形的网络结构，该结构输出的是1X128X80X80，1X256X40X40和1X512X20X20三个数据。

代码如下：

import cv2
import numpy as np
import torch
import torch.nn as nn


#silu激活函数
class SiLU(nn.Module):
    @staticmethod
    def forward(x):
        return x*torch.sigmoid(x)

def autopad(k, p=None):
    if p is None:
        p = k // 2 if isinstance(k, int) else [x // 2 for x in k]
    return p


#1.单个卷积结构的的conv函数
class Conv(nn.Module):
    def __init__(self,c1,c2,k=1,s=1,p=None,g=1,act=True):
        super(Conv,self).__init__()
        #卷积、标准化、激活函数
        self.conv=nn.Conv2d(c1,c2,k,s,autopad(k,p),groups=g, bias=False)
        self.bn=nn.BatchNorm2d(c2,eps=0.001, momentum=0.03)
        self.act=SiLU() if act is True else (act if isinstance(act, nn.Module) else nn.Identity())

    def forward(self,x):
        return self.act(self.bn(self.conv(x)))#卷积->BatchNorm2d->激活函数->output

    def fuseforward(self,x):
        return self.act(self.conv(x))#这个forward函数不存在BN操作


#2.focus函数
class Focus(nn.Module):
    def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True):  # ch_in, ch_out, kernel, stride, padding, groups
        super(Focus, self).__init__()

        # 通道数变为原来 4 倍
        self.conv = Conv(c1 * 4, c2, k, s, p, g, act)

    def forward(self,x):
        # 320, 320, 12 => 320, 320, 64
        return self.conv(
            # 640, 640, 3 => 320, 320, 12
            torch.cat(
                [
                    x[...,::2,::2],
                    x[...,1::2,::2],
                    x[...,::2,1::2],
                    x[...,1::2,1::2]
                ],1
            )
        )


# 3.此为CSPLayer中的残差结构的定义
class Bottleneck(nn.Module):
    # Standard bottleneck
    def __init__(self, c1, c2, shortcut=True, g=1, e=0.5):  # ch_in, ch_out, shortcut, groups, expansion
        super(Bottleneck, self).__init__()
        c_ = int(c2 * e)  # hidden channels
        self.cv1 = Conv(c1, c_, 1, 1)  # 输入通道数为c1,输出通道数为c_，卷积核大小为1，步长为1，默认为0填充（实际上是图片本身）
        self.cv2 = Conv(c_, c2, 3, 1, g=g)  # 输入通道数为c_,输出通道数为c2，卷积核大小为3，步长为1，默认为0填充
        self.add = shortcut and c1 == c2  ## c1与c2需相同才可进行shortcut运算

    def forward(self,x):

        return x+self.cv2(self.cv1(x)) if self.add else self.cv2(self.cv1(x))


#4.csplayer结构,用残差结构进行特征提取
class C3(nn.Module):
    # CSP Bottleneck with 3 convolutions
    def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):  # ch_in, ch_out, number, shortcut, groups, expansion
        super(C3, self).__init__()
        c_ = int(c2 * e)  # hidden channels
        self.cv1 = Conv(c1, c_, 1, 1)
        self.cv2 = Conv(c1, c_, 1, 1)
        self.cv3 = Conv(2 * c_, c2, 1)  # act=FReLU(c2)
        self.m = nn.Sequential(*[Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)])
        # self.m = nn.Sequential(*[CrossConv(c_, c_, 3, 1, g, 1.0, shortcut) for _ in range(n)])

    def forward(self, x):
        return self.cv3(torch.cat(
            (
                self.m(self.cv1(x)),
                self.cv2(x)
            )
            , dim=1))


#通过不同池化和大小的最大池化进行特征提取，提高网络的感受野
class SPP(nn.Module):
    # Spatial pyramid pooling layer used in YOLOv3-SPP
    def __init__(self, c1, c2, k=(5, 9, 13)):
        super(SPP, self).__init__()
        c_ = c1 // 2  # hidden channels
        self.cv1 = Conv(c1, c_, 1, 1)
        self.cv2 = Conv(c_ * (len(k) + 1), c2, 1, 1)
        self.m = nn.ModuleList([nn.MaxPool2d(kernel_size=x, stride=1, padding=x // 2) for x in k])

    def forward(self, x):
        x = self.cv1(x)
        return self.cv2(torch.cat([x] + [m(x) for m in self.m], 1))


class CSPDarknet(nn.Module):
    def __init__(self, base_channels, base_depth, phi, pretrained):
        super().__init__()
        # -----------------------------------------------#
        #   输入图片是640, 640, 3
        #   初始的基本通道base_channels是64
        # -----------------------------------------------#

        # -----------------------------------------------#
        #   利用focus网络结构进行特征提取
        #   640, 640, 3 -> 320, 320, 12 -> 320, 320, 64
        # -----------------------------------------------#
        self.stem = Focus(3, base_channels, k=3)

        # -----------------------------------------------#
        #   完成卷积之后，320, 320, 64 -> 160, 160, 128
        #   完成CSPlayer之后，160, 160, 128 -> 160, 160, 128
        # -----------------------------------------------#
        self.dark2 = nn.Sequential(
            # 320, 320, 64 -> 160, 160, 128
            Conv(base_channels, base_channels * 2, 3, 2),
            # 160, 160, 128 -> 160, 160, 128
            C3(base_channels * 2, base_channels * 2, base_depth),
        )

        # -----------------------------------------------#
        #   完成卷积之后，160, 160, 128 -> 80, 80, 256
        #   完成CSPlayer之后，80, 80, 256 -> 80, 80, 256
        #                   在这里引出有效特征层80, 80, 256
        #                   进行加强特征提取网络FPN的构建
        # -----------------------------------------------#
        self.dark3 = nn.Sequential(
            Conv(base_channels * 2, base_channels * 4, 3, 2),
            C3(base_channels * 4, base_channels * 4, base_depth * 3),
        )

        # -----------------------------------------------#
        #   完成卷积之后，80, 80, 256 -> 40, 40, 512
        #   完成CSPlayer之后，40, 40, 512 -> 40, 40, 512
        #                   在这里引出有效特征层40, 40, 512
        #                   进行加强特征提取网络FPN的构建
        # -----------------------------------------------#
        self.dark4 = nn.Sequential(
            Conv(base_channels * 4, base_channels * 8, 3, 2),
            C3(base_channels * 8, base_channels * 8, base_depth * 3),
        )

        # -----------------------------------------------#
        #   完成卷积之后，40, 40, 512 -> 20, 20, 1024
        #   完成SPP之后，20, 20, 1024 -> 20, 20, 1024
        #   完成CSPlayer之后，20, 20, 1024 -> 20, 20, 1024
        # -----------------------------------------------#
        self.dark5 = nn.Sequential(
            Conv(base_channels * 8, base_channels * 16, 3, 2),
            SPP(base_channels * 16, base_channels * 16),
            C3(base_channels * 16, base_channels * 16, base_depth, shortcut=False),
        )
        if pretrained:
            url = {
                's': 'https://github.com/bubbliiiing/yolov5-pytorch/releases/download/v1.0/cspdarknet_s_backbone.pth',
                'm': 'https://github.com/bubbliiiing/yolov5-pytorch/releases/download/v1.0/cspdarknet_m_backbone.pth',
                'l': 'https://github.com/bubbliiiing/yolov5-pytorch/releases/download/v1.0/cspdarknet_l_backbone.pth',
                'x': 'https://github.com/bubbliiiing/yolov5-pytorch/releases/download/v1.0/cspdarknet_x_backbone.pth',
            }[phi]
            checkpoint = torch.hub.load_state_dict_from_url(url=url, map_location="cpu", model_dir="./model_data")
            self.load_state_dict(checkpoint, strict=False)
            print("Load weights from ", url.split('/')[-1])

    def forward(self, x):
        x = self.stem(x)
        x = self.dark2(x)
        # -----------------------------------------------#
        #   dark3的输出为80, 80, 256，是一个有效特征层
        # -----------------------------------------------#
        x = self.dark3(x)
        feat1 = x
        # -----------------------------------------------#
        #   dark4的输出为40, 40, 512，是一个有效特征层
        # -----------------------------------------------#
        x = self.dark4(x)
        feat2 = x
        # -----------------------------------------------#
        #   dark5的输出为20, 20, 1024，是一个有效特征层
        # -----------------------------------------------#
        x = self.dark5(x)
        feat3 = x
        return feat1, feat2, feat3



if __name__ == "__main__":

    print("...............开始.................")
    img_path="D:\AI\yq\mubiao_detect\\6_yolov5\\nets\\000.jpg"
    img=cv2.imread(img_path)
    img=cv2.resize(img,(640,640))
    size=img.shape
    np_data=img.reshape((1,size[2],size[0],size[1]))

    #>>>>>>>>>>>>>>>>>>>>array---->tensor,该数据用于神经网络
    datas=torch.tensor(np_data).float()

    #1.构建神经网络
    #(1).conv网络
    csp=CSPDarknet(base_channels=64, base_depth=3, phi="s", pretrained=False)
    #print(csp)
    output=csp(datas)
    print(output[0].shape)
    print(output[1].shape)
    print(output[2].shape)

运行结果如下：

代码的各部分解释看我的另外的博客专栏管理-CSDN创作中心。