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文章地址

• An End-to-End Traffic Visibility Regression Algorithm
• 文章通过训练搜集得到的真实道路图像数据集（Actual Road dense image Dataset, ARD），通过专业的能见度计和多人标注，获得可靠的能见度标签数据集。构建网络，进行训练，获得了较好的能见度识别网络。网络包括特征提取​、多尺度映射​、特征融合​、非线性输出（回归范围为[0,1]，需要经过(0,0),(1,1)改用修改的sigmoid函数，相较于ReLU更好）。结构如下​
  

网络各层结构

• 我认为红框位置与之相应的参数不匹配，在Feature Extraction部分Reshape之后得到的特征图大小为4124124。紧接着接了一个卷积层Conv，显示输入是3128128
• 第二处红框，MaxPool的kernel设置为88,特征图没有进行padding，到全连接层的输入变为64117*117，参数不对应
  

代码实现

"""Based on the ideas of the below paper, using PyTorch to build TVRNet.Reference: Qin H, Qin H. An end-to-end traffic visibility regression algorithm[J]. IEEE Access, 2021, 10: 25448-25454.​@weishuo
"""import torch
from torch import nn
import mathclass Inception(nn.Module):def __init__(self, in_planes, out_planes):super(Inception, self).__init__()self.conv1 = nn.Conv2d(in_planes, out_planes, kernel_size=1, padding=0)self.conv3 = nn.Conv2d(in_planes, out_planes, kernel_size=3, padding=1)self.conv5 = nn.Conv2d(in_planes, out_planes, kernel_size=5, padding=2)self.conv7 = nn.Conv2d(in_planes, out_planes, kernel_size=7, padding=3)def forward(self, x):out_1 = self.conv1(x)out_3 = self.conv3(x)out_5 = self.conv5(x)out_7 = self.conv7(x)out = torch.cat((out_1, out_3, out_5, out_7), dim=1)return outdef modify_sigmoid(x):return 1 / (1 + torch.exp(-10*(x-0.5)))class TVRNet(nn.Module):def __init__(self, in_planes, out_planes):super(TVRNet, self).__init__()# (B, 3, 224, 224)  ——>  (B, 3, 220, 220)self.FeatureExtraction_onestep = nn.Sequential(nn.Conv2d(in_planes, 20, kernel_size=5, padding=0),nn.ReLU(inplace=True),)self.FeatureExtraction_maxpool = nn.MaxPool2d((5, 1))self.MultiScaleMapping = nn.Sequential(Inception(4, 16),nn.ReLU(inplace=True),nn.MaxPool2d(kernel_size=8))self.FeatureIntegration = nn.Sequential(nn.Linear(46656, 100),nn.ReLU(inplace=True),nn.Dropout(0.4),nn.Linear(100, out_planes))self.NonLinearRegression = modify_sigmoiddef forward(self, x):x = self.FeatureExtraction_onestep(x)x = x.view((x.shape[0], 1, x.shape[1], -1))x = self.FeatureExtraction_maxpool(x)x = x.view(x.shape[0], x.shape[2], int(math.sqrt(x.shape[3])), int(math.sqrt(x.shape[3])))# print(x.shape)x = self.MultiScaleMapping(x)# print(x.shape)x = x.view(x.shape[0], -1)x = self.FeatureIntegration(x)out = self.NonLinearRegression(x)return outif __name__ == '__main__':a = torch.randn(1,3,224,224)net = TVRNet(3,3)b = net(a)print(b.shape)