CSP-Darknet53
0. 引言
CSP-Darknet53无论是其作为CV Backbone，还是说它在别的数据集上取得极好的效果。与此同时，它与别的网络的适配能力极强。这些特点都在宣告：CSP-Darknet53的重要性。
关于原理部分的内容请查看这里CV 经典主干网络 (Backbone) 系列: CSPNet
1. 网络结构图
具体网络结构可以参考YOLO V3详解(一)：网络结构介绍中使用的工具来进行操作。具体网址和对应的权重文件下载地址如下：
模型可视化工具：https://lutzroeder.github.io/netron/
cfg文件下载网址：https://github.com/WongKinYiu/CrossStagePartialNetworks
得到的部分网络结构图的如下所示。
1.1 输入部分
1.2 CSP部分结构
1.3 输出部分
2. 代码实现
2.1 代码整体实现
通过代码实现CSP-Darknet53。框架为PyTorch，代码整体框架实现如下所示：
classCsDarkNet53(nn.Module):def__init__(self, num_classes):super(CsDarkNet53, self).__init__()input_channels =32# Networkself.stage1 = Conv2dBatchLeaky(3, input_channels,3,1, activation='mish')self.stage2 = Stage2(input_channels)self.stage3 = Stage3(4*input_channels)self.stage4 = Stage(4*input_channels,8)self.stage5 = Stage(8*input_channels,8)self.stage6 = Stage(16*input_channels,4)self.conv = Conv2dBatchLeaky(32*input_channels,32*input_channels,1,1, activation='mish')self.avgpool = nn.AdaptiveAvgPool2d((1,1))self.fc = nn.Linear(1024, num_classes)for m in self.modules():ifisinstance(m, nn.Conv2d):nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')elifisinstance(m,(nn.BatchNorm2d, nn.GroupNorm)):nn.init.constant_(m.weight,1)nn.init.constant_(m.bias,0)defforward(self, x):stage1 = self.stage1(x)stage2 = self.stage2(stage1)stage3 = self.stage3(stage2)stage4 = self.stage4(stage3)stage5 = self.stage5(stage4)stage6 = self.stage6(stage5)conv = self.conv(stage6)x = self.avgpool(conv)x = x.view(-1,1024)x = self.fc(x)return x
2.2 代码各个阶段实现
在代码中，对各个阶段的具体实现如下所示：
classMish(nn.Module):def__init__(self):super(Mish, self).__init__()defforward(self, x):return x * torch.tanh(F.softplus(x))classConv2dBatchLeaky(nn.Module):"""This convenience layer groups a 2D convolution, a batchnorm and a leaky ReLU."""def__init__(self, in_channels, out_channels, kernel_size, stride, activation='leaky', leaky_slope=0.1):super(Conv2dBatchLeaky, self).__init__()# Parametersself.in_channels = in_channelsself.out_channels = out_channelsself.kernel_size = kernel_sizeself.stride = strideifisinstance(kernel_size,(list,tuple)):self.padding =[int(k/2)for k in kernel_size]else:self.padding =int(kernel_size/2)self.leaky_slope = leaky_slope# self.mish = Mish()# Layerif activation =="leaky":self.layers = nn.Sequential(nn.Conv2d(self.in_channels, self.out_channels, self.kernel_size, self.stride, self.padding, bias=False),nn.BatchNorm2d(self.out_channels),nn.LeakyReLU(self.leaky_slope, inplace=True))elif activation =="mish":self.layers = nn.Sequential(nn.Conv2d(self.in_channels, self.out_channels, self.kernel_size, self.stride, self.padding, bias=False),nn.BatchNorm2d(self.out_channels),Mish())elif activation =="linear":self.layers = nn.Sequential(nn.Conv2d(self.in_channels, self.out_channels, self.kernel_size, self.stride, self.padding, bias=False))def__repr__(self):s ='{name} ({in_channels}, {out_channels}, kernel_size={kernel_size}, stride={stride}, padding={padding}, negative_slope={leaky_slope})'return s.format(name=self.__class__.__name__,**self.__dict__)defforward(self, x):x = self.layers(x)return xclassSmallBlock(nn.Module):def__init__(self, nchannels):super().__init__()self.features = nn.Sequential(Conv2dBatchLeaky(nchannels, nchannels,1,1, activation='mish'),Conv2dBatchLeaky(nchannels, nchannels,3,1, activation='mish'))# conv_shortcut'''参考 https://github.com/bubbliiiing/yolov4-pytorchshortcut后不接任何conv'''# self.active_linear = Conv2dBatchLeaky(nchannels, nchannels, 1, 1, activation='linear')# self.conv_shortcut = Conv2dBatchLeaky(nchannels, nchannels, 1, 1, activation='mish')defforward(self, data):short_cut = data + self.features(data)# active_linear = self.conv_shortcut(short_cut)return short_cut# Stage1 conv [256,256,3]->[256,256,32]classStage2(nn.Module):def__init__(self, nchannels):super().__init__()# stage2 32self.conv1 = Conv2dBatchLeaky(nchannels,2*nchannels,3,2, activation='mish')self.split0 = Conv2dBatchLeaky(2*nchannels,2*nchannels,1,1, activation='mish')self.split1 = Conv2dBatchLeaky(2*nchannels,2*nchannels,1,1, activation='mish')self.conv2 = Conv2dBatchLeaky(2*nchannels, nchannels,1,1, activation='mish')self.conv3 = Conv2dBatchLeaky(nchannels,2*nchannels,3,1, activation='mish')self.conv4 = Conv2dBatchLeaky(2*nchannels,2*nchannels,1,1, activation='mish')defforward(self, data):conv1 = self.conv1(data)split0 = self.split0(conv1)split1 = self.split1(conv1)conv2 = self.conv2(split1)conv3 = self.conv3(conv2)shortcut = split1 + conv3conv4 = self.conv4(shortcut)route = torch.cat([split0, conv4], dim=1)return routeclassStage3(nn.Module):def__init__(self, nchannels):super().__init__()# stage3 128self.conv1 = Conv2dBatchLeaky(nchannels,int(nchannels/2),1,1, activation='mish')self.conv2 = Conv2dBatchLeaky(int(nchannels/2), nchannels,3,2, activation='mish')self.split0 = Conv2dBatchLeaky(nchannels,int(nchannels/2),1,1, activation='mish')self.split1 = Conv2dBatchLeaky(nchannels,int(nchannels/2),1,1, activation='mish')self.block1 = SmallBlock(int(nchannels/2))self.block2 = SmallBlock(int(nchannels/2))self.conv3 = Conv2dBatchLeaky(int(nchannels/2),int(nchannels/2),1,1, activation='mish')defforward(self, data):conv1 = self.conv1(data)conv2 = self.conv2(conv1)split0 = self.split0(conv2)split1 = self.split1(conv2)block1 = self.block1(split1)block2 = self.block2(block1)conv3 = self.conv3(block2)route = torch.cat([split0, conv3], dim=1)return route# Stage4 Stage5 Stage6classStage(nn.Module):def__init__(self, nchannels, nblocks):super().__init__()# stage4 : 128# stage5 : 256# stage6 : 512self.conv1 = Conv2dBatchLeaky(nchannels, nchannels,1,1, activation='mish')self.conv2 = Conv2dBatchLeaky(nchannels,2*nchannels,3,2, activation='mish')self.split0 = Conv2dBatchLeaky(2*nchannels, nchannels,1,1, activation='mish')self.split1 = Conv2dBatchLeaky(2*nchannels, nchannels,1,1, activation='mish')blocks =[]for i inrange(nblocks):blocks.append(SmallBlock(nchannels))self.blocks = nn.Sequential(*blocks)self.conv4 = Conv2dBatchLeaky(nchannels, nchannels,1,1, activation='mish')defforward(self,data):conv1 = self.conv1(data)conv2 = self.conv2(conv1)split0 = self.split0(conv2)split1 = self.split1(conv2)blocks = self.blocks(split1)conv4 = self.conv4(blocks)route = torch.cat([split0, conv4], dim=1)return route
3. 代码测试
下面使用一个小例子来对代码进行测试。
if __name__ =="__main__":use_cuda = torch.cuda.is_available()if use_cuda:device = torch.device("cuda")cudnn.benchmark =Trueelse:device = torch.device("cpu")darknet = CsDarkNet53(num_classes=10)darknet = darknet.cuda()with torch.no_grad():darknet.eval()data = torch.rand(1,3,256,256)data = data.cuda()try:#print(darknet)summary(darknet,(3,256,256))print(darknet(data))except Exception as e:print(e)
代码的输出如下所示：
Total params:26,627,434Trainable params:26,627,434Non-trainable params:0----------------------------------------------------------------Input size (MB):0.75Forward/backward pass size (MB):553.51Params size (MB):101.58Estimated Total Size (MB):655.83----------------------------------------------------------------tensor([[0.1690,0.0798,0.1836,0.2414,0.3855,0.2437,-0.1422,-0.1855,0.1758,-0.2452]], device='cuda:0')
注意：输出中存在框架结构内容，这里没有将其写在博客中
4. 结论
CSP-Darknet53的代码结构结合着对应的代码实现一起看，可以有效帮助大家理解关于原理部分的内容。希望可以帮助到大家！！！
另外，关于代码中存在的一些小的部分可能会在后面进行介绍。
本文转载自: https://blog.csdn.net/qq_36758270/article/details/130395730
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版权归原作者 sjx_alo 所有， 如有侵权，请联系我们删除。