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 math
class 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 out
def 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__()
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_sigmoid
def 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])))
x = self.MultiScaleMapping(x)
x = x.view(x.shape[0], -1)
x = self.FeatureIntegration(x)
out = self.NonLinearRegression(x)
return out
if __name__ == '__main__':
a = torch.randn(1,3,224,224)
net = TVRNet(3,3)
b = net(a)
print(b.shape)
