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| import math | |
| import torch | |
| import torch.nn as nn | |
| #-------------------------------------------------# | |
| # 卷积块 | |
| # Conv2d + BatchNorm2d + LeakyReLU | |
| #-------------------------------------------------# | |
| 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 = nn.LeakyReLU(0.1) | |
| def forward(self, x): | |
| x = self.conv(x) | |
| x = self.bn(x) | |
| x = self.activation(x) | |
| return x | |
| ''' | |
| input | |
| | | |
| BasicConv | |
| ----------------------- | |
| | | | |
| route_group route | |
| | | | |
| BasicConv | | |
| | | | |
| ------------------- | | |
| | | | | |
| route_1 BasicConv | | |
| | | | | |
| -----------------cat | | |
| | | | |
| ---- BasicConv | | |
| | | | | |
| feat cat--------------------- | |
| | | |
| MaxPooling2D | |
| ''' | |
| #---------------------------------------------------# | |
| # CSPdarknet53-tiny的结构块 | |
| # 存在一个大残差边 | |
| # 这个大残差边绕过了很多的残差结构 | |
| #---------------------------------------------------# | |
| class Resblock_body(nn.Module): | |
| def __init__(self, in_channels, out_channels): | |
| super(Resblock_body, self).__init__() | |
| self.out_channels = out_channels | |
| self.conv1 = BasicConv(in_channels, out_channels, 3) | |
| self.conv2 = BasicConv(out_channels//2, out_channels//2, 3) | |
| self.conv3 = BasicConv(out_channels//2, out_channels//2, 3) | |
| self.conv4 = BasicConv(out_channels, out_channels, 1) | |
| self.maxpool = nn.MaxPool2d([2,2],[2,2]) | |
| def forward(self, x): | |
| # 利用一个3x3卷积进行特征整合 | |
| x = self.conv1(x) | |
| # 引出一个大的残差边route | |
| route = x | |
| c = self.out_channels | |
| # 对特征层的通道进行分割，取第二部分作为主干部分。 | |
| x = torch.split(x, c//2, dim = 1)[1] | |
| # 对主干部分进行3x3卷积 | |
| x = self.conv2(x) | |
| # 引出一个小的残差边route_1 | |
| route1 = x | |
| # 对第主干部分进行3x3卷积 | |
| x = self.conv3(x) | |
| # 主干部分与残差部分进行相接 | |
| x = torch.cat([x,route1], dim = 1) | |
| # 对相接后的结果进行1x1卷积 | |
| x = self.conv4(x) | |
| feat = x | |
| x = torch.cat([route, x], dim = 1) | |
| # 利用最大池化进行高和宽的压缩 | |
| x = self.maxpool(x) | |
| return x,feat | |
| class CSPDarkNet(nn.Module): | |
| def __init__(self): | |
| super(CSPDarkNet, self).__init__() | |
| # 首先利用两次步长为2x2的3x3卷积进行高和宽的压缩 | |
| # 416,416,3 -> 208,208,32 -> 104,104,64 | |
| self.conv1 = BasicConv(3, 32, kernel_size=3, stride=2) | |
| self.conv2 = BasicConv(32, 64, kernel_size=3, stride=2) | |
| # 104,104,64 -> 52,52,128 | |
| self.resblock_body1 = Resblock_body(64, 64) | |
| # 52,52,128 -> 26,26,256 | |
| self.resblock_body2 = Resblock_body(128, 128) | |
| # 26,26,256 -> 13,13,512 | |
| self.resblock_body3 = Resblock_body(256, 256) | |
| # 13,13,512 -> 13,13,512 | |
| self.conv3 = BasicConv(512, 512, kernel_size=3) | |
| self.num_features = 1 | |
| # 进行权值初始化 | |
| 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): | |
| # 416,416,3 -> 208,208,32 -> 104,104,64 | |
| x = self.conv1(x) | |
| x = self.conv2(x) | |
| # 104,104,64 -> 52,52,128 | |
| x, _ = self.resblock_body1(x) | |
| # 52,52,128 -> 26,26,256 | |
| x, _ = self.resblock_body2(x) | |
| # 26,26,256 -> x为13,13,512 | |
| # -> feat1为26,26,256 | |
| x, feat1 = self.resblock_body3(x) | |
| # 13,13,512 -> 13,13,512 | |
| x = self.conv3(x) | |
| feat2 = x | |
| return feat1,feat2 | |
| def darknet53_tiny(pretrained, **kwargs): | |
| model = CSPDarkNet() | |
| if pretrained: | |
| model.load_state_dict(torch.load("model_data/CSPdarknet53_tiny_backbone_weights.pth")) | |
| return model | |