Yolov5网络构架实现

import torch
import torch.nn as nnclass SiLU(nn.Module):@staticmethoddef 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 pclass 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, groupssuper(Focus, self).__init__()self.conv = Conv(c1 * 4, c2, k, s, p, g, act)def forward(self, x):# 320, 320, 12 => 320, 320, 64return self.conv(# 640, 640, 3 => 320, 320, 12torch.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 bottleneckdef __init__(self, c1, c2, shortcut=True, g=1, e=0.5):  # ch_in, ch_out, shortcut, groups, expansionsuper(Bottleneck, self).__init__()c_ = int(c2 * e)  # hidden channelsself.cv1 = Conv(c1, c_, 1, 1)self.cv2 = Conv(c_, c2, 3, 1, g=g)self.add = shortcut and c1 == c2def 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 convolutionsdef __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):  # ch_in, ch_out, number, shortcut, groups, expansionsuper(C3, self).__init__()c_ = int(c2 * e)  # hidden channelsself.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-SPPdef __init__(self, c1, c2, k=(5, 9, 13)):super(SPP, self).__init__()c_ = c1 // 2  # hidden channelsself.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, 128Conv(base_channels, base_channels * 2, 3, 2),# 160, 160, 128 -> 160, 160, 128C3(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 = xreturn feat1, feat2, feat3