【目标检测】理论篇（3）YOLOv5实现

Yolov5网络构架实现

import torch
import torch.nn as nn
 
 
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
 
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__()
        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
            )
        )
 
class Conv(nn.Module):
    def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True):
        super(Conv, self).__init__()
        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)))
 
    def fuseforward(self, x):
        return self.act(self.conv(x))
 
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)
        self.cv2 = Conv(c_, c2, 3, 1, g=g)
        self.add = shortcut and c1 == c2
 
    def forward(self, x):
        return x + self.cv2(self.cv1(x)) if self.add else self.cv2(self.cv1(x))
 
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))
 
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