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roadnet_networks.py
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roadnet_networks.py
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#! -*- coding: utf-8 -*-
# Author: Yahui Liu <yahui.liu@unitn.it>
"""
Reference:
RoadNet: Learning to Comprehensively Analyze Road Networks in Complex Urban Scenes
From High-Resolution Remotely Sensed Images.
https://ieeexplore.ieee.org/document/8506600
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
from .networks import get_norm_layer, init_net
class RoadNet(nn.Module):
def __init__(self, in_nc, out_nc, ngf, norm='batch', use_selu=1):
super(RoadNet, self).__init__()
norm_layer = get_norm_layer(norm_type=norm)
#------------road surface segmentation------------#
self.segment_conv1 = nn.Sequential(*self._conv_block(in_nc, ngf, norm_layer, use_selu, num_block=2))
self.side_segment_conv1 = nn.Conv2d(ngf, out_nc, kernel_size=1, stride=1, bias=False)
self.segment_conv2 = nn.Sequential(*self._conv_block(ngf, ngf*2, norm_layer, use_selu, num_block=2))
self.side_segment_conv2 = nn.Conv2d(ngf*2, out_nc, kernel_size=1, stride=1, bias=False)
self.segment_conv3 = nn.Sequential(*self._conv_block(ngf*2, ngf*4, norm_layer, use_selu, num_block=3))
self.side_segment_conv3 = nn.Conv2d(ngf*4, out_nc, kernel_size=1, stride=1, bias=False)
self.segment_conv4 = nn.Sequential(*self._conv_block(ngf*4, ngf*8, norm_layer, use_selu, num_block=3))
self.side_segment_conv4 = nn.Conv2d(ngf*8, out_nc, kernel_size=1, stride=1, bias=False)
self.segment_conv5 = nn.Sequential(*self._conv_block(ngf*8, ngf*8, norm_layer, use_selu, num_block=3))
self.side_segment_conv5 = nn.Conv2d(ngf*8, out_nc, kernel_size=1, stride=1, bias=False)
self.fuse_segment_conv = nn.Conv2d(out_nc*5, out_nc, kernel_size=1, stride=1, bias=False)
ngf2 = ngf//2
#------------road edge detection------------#
self.edge_conv1 = nn.Sequential(*self._conv_block(in_nc+out_nc, ngf2, norm_layer, use_selu, num_block=2))
self.side_edge_conv1 = nn.Conv2d(ngf2, out_nc, kernel_size=1, stride=1, bias=False)
self.edge_conv2 = nn.Sequential(*self._conv_block(ngf2, ngf2*2, norm_layer, use_selu, num_block=2))
self.side_edge_conv2 = nn.Conv2d(ngf2*2, out_nc, kernel_size=1, stride=1, bias=False)
self.edge_conv3 = nn.Sequential(*self._conv_block(ngf2*2, ngf2*4, norm_layer, use_selu, num_block=2))
self.side_edge_conv3 = nn.Conv2d(ngf2*4, out_nc, kernel_size=1, stride=1, bias=False)
self.edge_conv4 = nn.Sequential(*self._conv_block(ngf2*4, ngf2*8, norm_layer, use_selu, num_block=2))
self.side_edge_conv4 = nn.Conv2d(ngf2*8, out_nc, kernel_size=1, stride=1, bias=False)
self.fuse_edge_conv = nn.Conv2d(out_nc*4, out_nc, kernel_size=1, stride=1, bias=False)
#------------road centerline extraction------------#
self.centerline_conv1 = nn.Sequential(*self._conv_block(in_nc+out_nc, ngf2, norm_layer, use_selu, num_block=2))
self.side_centerline_conv1 = nn.Conv2d(ngf2, out_nc, kernel_size=1, stride=1, bias=False)
self.centerline_conv2 = nn.Sequential(*self._conv_block(ngf2, ngf2*2, norm_layer, use_selu, num_block=2))
self.side_centerline_conv2 = nn.Conv2d(ngf2*2, out_nc, kernel_size=1, stride=1, bias=False)
self.centerline_conv3 = nn.Sequential(*self._conv_block(ngf2*2, ngf2*4, norm_layer, use_selu, num_block=2))
self.side_centerline_conv3 = nn.Conv2d(ngf2*4, out_nc, kernel_size=1, stride=1, bias=False)
self.centerline_conv4 = nn.Sequential(*self._conv_block(ngf2*4, ngf2*8, norm_layer, use_selu, num_block=2))
self.side_centerline_conv4 = nn.Conv2d(ngf2*8, out_nc, kernel_size=1, stride=1, bias=False)
self.fuse_centerline_conv = nn.Conv2d(out_nc*4, out_nc, kernel_size=1, stride=1, bias=False)
self.maxpool = nn.MaxPool2d(2, stride=2)
#self.up2 = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True)
#self.up4 = nn.Upsample(scale_factor=4, mode='bilinear', align_corners=True)
#self.up8 = nn.Upsample(scale_factor=8, mode='bilinear', align_corners=True)
#self.up16 = nn.Upsample(scale_factor=16, mode='bilinear', align_corners=True)
def _conv_block(self, in_nc, out_nc, norm_layer, use_selu, num_block=2, kernel_size=3,
stride=1, padding=1, bias=True):
conv = []
for i in range(num_block):
cur_in_nc = in_nc if i == 0 else out_nc
conv += [nn.Conv2d(cur_in_nc, out_nc, kernel_size=kernel_size, stride=stride,
padding=padding, bias=bias)]
if use_selu:
conv += [nn.SeLU(True)]
else:
conv += [norm_layer(out_nc), nn.ReLU(True)]
return conv
def _segment_forward(self, x):
"""
predict road surface segmentation
:param: x, image tensor, [N, C, H, W]
"""
h,w = x.size()[2:]
# main stream features
conv1 = self.segment_conv1(x)
conv2 = self.segment_conv2(self.maxpool(conv1))
conv3 = self.segment_conv3(self.maxpool(conv2))
conv4 = self.segment_conv4(self.maxpool(conv3))
conv5 = self.segment_conv5(self.maxpool(conv4))
# side output features
side_output1 = self.side_segment_conv1(conv1)
side_output2 = self.side_segment_conv2(conv2)
side_output3 = self.side_segment_conv3(conv3)
side_output4 = self.side_segment_conv4(conv4)
side_output5 = self.side_segment_conv5(conv5)
# upsampling side output features
side_output2 = F.interpolate(side_output2, size=(h, w), mode='bilinear', align_corners=True) #self.up2(side_output2)
side_output3 = F.interpolate(side_output3, size=(h, w), mode='bilinear', align_corners=True) #self.up4(side_output3)
side_output4 = F.interpolate(side_output4, size=(h, w), mode='bilinear', align_corners=True) #self.up8(side_output4)
side_output5 = F.interpolate(side_output5, size=(h, w), mode='bilinear', align_corners=True) #self.up16(side_output5)
fused = self.fuse_segment_conv(torch.cat([
side_output1,
side_output2,
side_output3,
side_output4,
side_output5], dim=1))
return [side_output1, side_output2, side_output3, side_output4, side_output5, fused]
def _edge_forward(self, x):
"""
predict road edge
:param: x, [image tensor, predicted segmentation tensor], [N, C+1, H, W]
"""
h, w = x.size()[2:]
# main stream features
conv1 = self.edge_conv1(x)
conv2 = self.edge_conv2(self.maxpool(conv1))
conv3 = self.edge_conv3(self.maxpool(conv2))
conv4 = self.edge_conv4(self.maxpool(conv3))
# side output features
side_output1 = self.side_edge_conv1(conv1)
side_output2 = self.side_edge_conv2(conv2)
side_output3 = self.side_edge_conv3(conv3)
side_output4 = self.side_edge_conv4(conv4)
# upsampling side output features
side_output2 = F.interpolate(side_output2, size=(h, w), mode='bilinear', align_corners=True) #self.up2(side_output2)
side_output3 = F.interpolate(side_output3, size=(h, w), mode='bilinear', align_corners=True) #self.up4(side_output3)
side_output4 = F.interpolate(side_output4, size=(h, w), mode='bilinear', align_corners=True) #self.up8(side_output4)
fused = self.fuse_edge_conv(torch.cat([
side_output1,
side_output2,
side_output3,
side_output4], dim=1))
return [side_output1, side_output2, side_output3, side_output4, fused]
def _centerline_forward(self, x):
"""
predict road edge
:param: x, [image tensor, predicted segmentation tensor], [N, C+1, H, W]
"""
h,w = x.size()[2:]
# main stream features
conv1 = self.centerline_conv1(x)
conv2 = self.centerline_conv2(self.maxpool(conv1))
conv3 = self.centerline_conv3(self.maxpool(conv2))
conv4 = self.centerline_conv4(self.maxpool(conv3))
# side output features
side_output1 = self.side_centerline_conv1(conv1)
side_output2 = self.side_centerline_conv2(conv2)
side_output3 = self.side_centerline_conv3(conv3)
side_output4 = self.side_centerline_conv4(conv4)
# upsampling side output features
side_output2 = F.interpolate(side_output2, size=(h, w), mode='bilinear', align_corners=True) #self.up2(side_output2)
side_output3 = F.interpolate(side_output3, size=(h, w), mode='bilinear', align_corners=True) #self.up4(side_output3)
side_output4 = F.interpolate(side_output4, size=(h, w), mode='bilinear', align_corners=True) #self.up8(side_output4)
fused = self.fuse_centerline_conv(torch.cat([
side_output1,
side_output2,
side_output3,
side_output4], dim=1))
return [side_output1, side_output2, side_output3, side_output4, fused]
def forward(self, x):
segments = self._segment_forward(x)
x_ = torch.cat([x, segments[-1]], dim=1)
edges = self._edge_forward(x_)
centerlines = self._centerline_forward(x_)
return segments, edges, centerlines
def define_roadnet(in_nc,
out_nc,
ngf,
norm='batch',
use_selu=1,
init_type='xavier',
init_gain=0.02,
gpu_ids=[]):
net = RoadNet(in_nc, out_nc, ngf, norm, use_selu)
return init_net(net, init_type, init_gain, gpu_ids)