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