Only test on Ubuntu 20.04 with:
- Python >= 3.8 (tested with Python3.8)
- PyTorch >= 1.6 (tested with Pytorch1.6)
- CUDA (tested with cuda10.1)
- Other dependencies described in
requirements.txt
# 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 DILane package
python setup.py build developDownload CULane. Then extract them to $CULANEROOT. Create link to data directory.
cd $CLRNET_ROOT
mkdir -p data
ln -s $CULANEROOT data/CULaneFor CULane, you should have structure like this:
$CULANEROOT/driver_xx_xxframe # data folders x6
$CULANEROOT/laneseg_label_w16 # lane segmentation labels
$CULANEROOT/list # data lists
Download Tusimple. Then extract them to $TUSIMPLEROOT. Create link to data directory.
cd $CLRNET_ROOT
mkdir -p data
ln -s $TUSIMPLEROOT data/tusimpleFor 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 directoryFor training, run
python main.py [configs/path_to_your_config] --gpus [gpu_num]For example, run
python main.py configs/clrnet/resnet18_culane.py --gpus 0For 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/res34_culane.py --validate --load_from culane_res34.pth --gpus 0Currently, this code can output the visualization result when testing, just add --view.
We will get the visualization result in work_dirs/xxx/xxx/visualization.