import math from collections import OrderedDict import torch import torch.nn as nn import torch.nn.functional as F #-------------------------------------------------# # MISH激活函数 #-------------------------------------------------# class Mish(nn.Module): def __init__(self): super(Mish, self).__init__() def forward(self, x): return x * torch.tanh(F.softplus(x)) #---------------------------------------------------# # 卷积块 -> 卷积 + 标准化 + 激活函数 # Conv2d + BatchNormalization + Mish #---------------------------------------------------# class BasicConv(nn.Module): def __init__(self, in_channels, out_channels, kernel_size, stride=1): super(BasicConv, self).__init__() self.conv = nn.Conv2d(in_channels, out_channels, kernel_size, stride, kernel_size//2, bias=False) self.bn = nn.BatchNorm2d(out_channels) self.activation = Mish() def forward(self, x): x = self.conv(x) x = self.bn(x) x = self.activation(x) return x #---------------------------------------------------# # CSPdarknet的结构块的组成部分 # 内部堆叠的残差块 #---------------------------------------------------# class Resblock(nn.Module): def __init__(self, channels, hidden_channels=None): super(Resblock, self).__init__() if hidden_channels is None: hidden_channels = channels self.block = nn.Sequential( BasicConv(channels, hidden_channels, 1), BasicConv(hidden_channels, channels, 3) ) def forward(self, x): return x + self.block(x) #--------------------------------------------------------------------# # CSPdarknet的结构块 # 首先利用ZeroPadding2D和一个步长为2x2的卷积块进行高和宽的压缩 # 然后建立一个大的残差边shortconv、这个大残差边绕过了很多的残差结构 # 主干部分会对num_blocks进行循环，循环内部是残差结构。 # 对于整个CSPdarknet的结构块，就是一个大残差块+内部多个小残差块 #--------------------------------------------------------------------# class Resblock_body(nn.Module): def __init__(self, in_channels, out_channels, num_blocks, first): super(Resblock_body, self).__init__() #----------------------------------------------------------------# # 利用一个步长为2x2的卷积块进行高和宽的压缩 #----------------------------------------------------------------# self.downsample_conv = BasicConv(in_channels, out_channels, 3, stride=2) if first: #--------------------------------------------------------------------------# # 然后建立一个大的残差边self.split_conv0、这个大残差边绕过了很多的残差结构 #--------------------------------------------------------------------------# self.split_conv0 = BasicConv(out_channels, out_channels, 1) #----------------------------------------------------------------# # 主干部分会对num_blocks进行循环，循环内部是残差结构。 #----------------------------------------------------------------# self.split_conv1 = BasicConv(out_channels, out_channels, 1) self.blocks_conv = nn.Sequential( Resblock(channels=out_channels, hidden_channels=out_channels//2), BasicConv(out_channels, out_channels, 1) ) self.concat_conv = BasicConv(out_channels*2, out_channels, 1) else: #--------------------------------------------------------------------------# # 然后建立一个大的残差边self.split_conv0、这个大残差边绕过了很多的残差结构 #--------------------------------------------------------------------------# self.split_conv0 = BasicConv(out_channels, out_channels//2, 1) #----------------------------------------------------------------# # 主干部分会对num_blocks进行循环，循环内部是残差结构。 #----------------------------------------------------------------# self.split_conv1 = BasicConv(out_channels, out_channels//2, 1) self.blocks_conv = nn.Sequential( *[Resblock(out_channels//2) for _ in range(num_blocks)], BasicConv(out_channels//2, out_channels//2, 1) ) self.concat_conv = BasicConv(out_channels, out_channels, 1) def forward(self, x): x = self.downsample_conv(x) x0 = self.split_conv0(x) x1 = self.split_conv1(x) x1 = self.blocks_conv(x1) #------------------------------------# # 将大残差边再堆叠回来 #------------------------------------# x = torch.cat([x1, x0], dim=1) #------------------------------------# # 最后对通道数进行整合 #------------------------------------# x = self.concat_conv(x) return x #---------------------------------------------------# # CSPdarknet53 的主体部分 # 输入为一张416x416x3的图片 # 输出为三个有效特征层 #---------------------------------------------------# class CSPDarkNet(nn.Module): def __init__(self, layers): super(CSPDarkNet, self).__init__() self.inplanes = 32 # 416,416,3 -> 416,416,32 self.conv1 = BasicConv(3, self.inplanes, kernel_size=3, stride=1) self.feature_channels = [64, 128, 256, 512, 1024] self.stages = nn.ModuleList([ # 416,416,32 -> 208,208,64 Resblock_body(self.inplanes, self.feature_channels[0], layers[0], first=True), # 208,208,64 -> 104,104,128 Resblock_body(self.feature_channels[0], self.feature_channels[1], layers[1], first=False), # 104,104,128 -> 52,52,256 Resblock_body(self.feature_channels[1], self.feature_channels[2], layers[2], first=False), # 52,52,256 -> 26,26,512 Resblock_body(self.feature_channels[2], self.feature_channels[3], layers[3], first=False), # 26,26,512 -> 13,13,1024 Resblock_body(self.feature_channels[3], self.feature_channels[4], layers[4], first=False) ]) self.num_features = 1 for m in self.modules(): if isinstance(m, nn.Conv2d): n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels m.weight.data.normal_(0, math.sqrt(2. / n)) elif isinstance(m, nn.BatchNorm2d): m.weight.data.fill_(1) m.bias.data.zero_() def forward(self, x): x = self.conv1(x) x = self.stages[0](x) x = self.stages[1](x) out3 = self.stages[2](x) out4 = self.stages[3](out3) out5 = self.stages[4](out4) return out3, out4, out5 def darknet53(pretrained): model = CSPDarkNet([1, 2, 8, 8, 4]) if pretrained: model.load_state_dict(torch.load("model_data/CSPdarknet53_backbone_weights.pth")) return model