lihui-gu / clrtnet

Introduction

I investigated and summerized the curent mainstream lane detection model, improved and adjusted the model based on CLRNet, built a SpaceFormer with spatial attention mechanism to obtain context information, completed the construction of a new network structure CLRTNet, and test it on tusimple Dataset. The final result is very close to the accuracy level of CLRNet.

Installation

Prerequisites

Only test on Ubuntu18.04 and 20.04 with:

• Python >= 3.8 (tested with Python3.8)
• PyTorch >= 1.6 (tested with Pytorch1.6)
• CUDA (tested with cuda10.2)
• Other dependencies described in requirements.txt

Clone this repository

Clone this code to your workspace. We call this directory as $CLRNET_ROOT

git clone https://github.com/Turoad/clrnet

Create a conda virtual environment and activate it (conda is optional)

conda create -n clrnet python=3.8 -y
conda activate clrnet

Install dependencies

# Install pytorch firstly, the cudatoolkit version should be same in your system.

conda install pytorch torchvision cudatoolkit=10.1 -c pytorch

# Or you can install via pip
pip install torch==1.8.0 torchvision==0.9.0

# Install python packages
python setup.py build develop

Data preparation Tusimple

Download Tusimple. Then extract them to $TUSIMPLEROOT. Create link to data directory.

cd $CLRNET_ROOT
mkdir -p data
ln -s $TUSIMPLEROOT data/tusimple

For Tusimple, you should have structure like this:

$TUSIMPLEROOT/clips # data folders
$TUSIMPLEROOT/lable_data_xxxx.json # label json file x4
$TUSIMPLEROOT/test_tasks_0627.json # test tasks json file
$TUSIMPLEROOT/test_label.json # test label json file

For Tusimple, the segmentation annotation is not provided, hence we need to generate segmentation from the json annotation.

python tools/generate_seg_tusimple.py --root $TUSIMPLEROOT
# this will generate seg_label directory

Getting Started

Training

For training, run

python main.py [configs/path_to_your_config] --gpus [gpu_num]

For example, run

python main.py configs/clrnet/clr_resnet18_culane.py --gpus 0

Validation

For testing, run

python main.py [configs/path_to_your_config] --[test|validate] --load_from [path_to_your_model] --gpus [gpu_num]

For example, run

python main.py configs/clrnet/clr_dla34_culane.py --validate --load_from culane_dla34.pth --gpus 0

Currently, this code can output the visualization result when testing, just add --view. We will get the visualization result in work_dirs/xxx/xxx/visualization.

Loss Optimization strategy

Use dynamic weight adjust strategy, see more at global_var.py.