The repository contains code to train and evaluate our YOLinO network for polyline estimation from RGB images. The code was developed for a PhD thesis and is targeted towards evaluating rather productive use.

There are some issues we want to tackle in the future:

• Cleanup code
• Extract experiment coding for clean code structure
• Migrate from Gitlab CI to Github Actions
• Add proper documentation
• Provide parametrization instructions
• Provide params.yaml for Argoverse, Tusimple, CULane, ...

When using this code please cite our publications:

@inproceedings{meyer2021yolino,
  title={YOLinO: Generic Single Shot Polyline Setection in Real Time},
  author={Meyer, Annika and Skudlik, Philipp and Pauls, Jan-Hendrik and Stiller, Christoph},
  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision Workshops},
  pages={2916--2925},
  year={2021}
}

It is recommended to use a virtualenv or conda to wrap your python packages from the rest of your system. Especially tensorflow and pytorch might exist in another version on a server.

I recommend to use conda: https://docs.conda.io/projects/conda/en/latest/user-guide/getting-started.html#starting-conda After installation you should be able to create your conda environment with

conda create --name yolino pip python==3.8

You might want to have autocomplete with conda install argcomplete. Then add eval "$(register-python-argcomplete conda)" to your bash file. For zshell use https://github.com/conda-incubator/conda-zsh-completion/blob/master/_conda.

If you want to use virtualenvwrapper: https://virtualenvwrapper.readthedocs.io/en/latest/install.html After installation you should be able to create your virtualenv with

mkvirtualenv yolino --python=/usr/bin/python3

Make sure your PYTHONPATH is empty. Maybe add export PYTHONPATH= to your virtualenv scripts e.g. ~/.virtualenvs/yolino/bin/postactivate.

mkdir yolino
cd yolino
git clone https://github.com/KIT-MRT/YOLinO.git

conda create --name yolino pip python==3.8
# Alternative: mkvirtualenv yolino --python=/usr/bin/python3

# You should be working in a virtual env now

cd yolino

# If you want a specifiy branch do
git checkout <branch-name>

# Make sure you did not source any mrt or ros stuff; should be empty for most cases!
echo $CMAKE_PREFIX_PATH
echo $PYTHONPATH

# Install requirements
pip install -e .

If you want to use a specific cuda version have a look at https://pytorch.org/get-started/previous-versions/. For CUDA 11.6 it is recommended to use

conda install pytorch==1.12.1 torchvision==0.13.1 torchaudio==0.12.1 cudatoolkit=11.6 -c pytorch -c conda-forge

Download the darknet weights at https://pjreddie.com/media/files/yolov3.weights.

The code expects the dataset files to be accessible at the environment variable fitting the dataset e.g. $DATASET_TUSIMPLE and $DATASET_CULANE, respectively.

So far a second folder is necessary next to the acutal yolino package, where we store parametrization, checkpoints etc. We recommend to use separate folders for dealing with different datasets or configurations. For example, we use tus_po_8p_dn19 as a good start for the tusimple dataset. The name encodes the tusimple dataset with points line representation, 8 predictors without any upsampling on darknet-19. The scripts expect this folder structure by default. Pass --root (path to the yolino folder), --dvc (folder containing your output) and --config (path to a params.yaml) if the structure is different.

├── tus_md_8p_dn19
│   ├── params.yaml
│   ├── default_params.yaml
│   ├── ...
├── tus_po_8p_dn19
│   ├── params.yaml
│   ├── default_params.yaml
│   ├── ...
├── yolino
  ├── setup.py
  ├── src
  │   ├── ...
  ├── ...

We can log automatically to weights and biases, clearml, command line and logging file. With --loggers this can be specified. It is recommended to use only weights and biases. File logging slows down the process as it logs everything.

• Weights and Biases: Setup your account on https://wandb.ai/site and run wandb login in your python environment on your machine.
• Clearml: Setup your account on https://app.clear.ml/ and run clearml-init in your python environment on your machine.

• Use Python3 only!
• On Servers: execute echo "backend: Agg" > ~/.config/matplotlib/matplotlibrc for non interacting matplotlib
• List of requirements can be found in setup.cfg and will be automatically retrieved by pip install -e yolino (see above)
• If the tests fail, make sure you have git lfs setup and all files in test/test_data are fetched properly

• Training in folder e.g. called tus_po_8p_dn19 on e.g. GPU with ID 1 (check nvidia-smi)

  cd tus_po_8p_dn19
  CUDA_VISIBLE_DEVICES="1" python ../yolino/src/yolino/train.py --gpu
  

• Evaluation in folder e.g. called tus_po_8p_dn19 (https://gitlab.mrt.uni-karlsruhe.de/meyer/dvc_experiment_mgmt) on e.g. CPU (provide GPU IDs if you prefer GPU)

  cd tus_po_8p_dn19
  CUDA_VISIBLE_DEVICES="" python ../yolino/src/yolino/eval.py
  

1. Your configuration is set with the given params.yaml from your configuration folder. Use res/default_params.yaml as inspiration.
2. Use proper virtual environment with workon <virtualenv-name> or conda with conda activate <conda-name>.
3. Set your dataset paths properly to e.g. $DATASET_TUSIMPLE.
4. cd tus_po_8p_dn19
5. Execute training command on e.g. gpu with ID=1 CUDA_VISIBLE_DEVICES="1" python ../yolino/src/yolino/train.py --gpu --loggers wb
6. Open wandb site to watch your training. Link will be printed to cmd.

1. Your configuration is already set with the given params.yaml from your configuration folder.
2. Use proper virtual environment with workon <virtualenv-name> or conda with conda activate <conda-name>.
3. Set your dataset paths properly to e.g. $DATASET_TUSIMPLE.
4. cd tus_po_8p_dn19
5. Execute visualization command for e.g. clips/0313-2/100/20.jpg python ../yolino/src/yolino/show.py --explicit clips/0313-2/100/20.jpg. By default all parameters from the params.yaml are taken. You might want to use no augmentation with --augment "". If you wish to skim through the dataset leave the --explicit filename. Make sure to have access to the whole dataset or use --ignore_missing. With --max_n you can limit the number of files loaded.

Use weights and biases: https://docs.wandb.ai/guides/sweeps

• If the tests fail, make sure you have git lfs setup and all files in test/test_data are fetched properly
• If weights and biases complains about duplicate IDs after deleting some runs online, execute wandb sync --clean in your dvc folder and add (temporarily!) id=wandb.util.generate_id() to the initializing of your wandb connection. Run it once and delete that again.
• If installing the packages with pip does not work with conda, try using explicitly the conda pip/python executables in e.g. <CONDA_HOME>/envs/<env_name>/bin/pip

https://www.scc.kit.edu/dienste/horeka.php

pip install virtualenv virtualenvwrapper
python3 -m venv ~/.virtualenvs/diss_yolino_venv
workon diss_yolino_venv
ml load devel/cuda/11.0
pip3 install torch===1.7.1+cu110 torchvision===0.8.2+cu110 torchaudio===0.7.2 -f https://download.pytorch.org/whl/torch_stable.html
pip3 install -e . # inside yolino repo
sbatch run_with_sbatch.sh

The docker for CI jobs is generated every time the master is build and either the Dockerfile or the gitlab-ci.yaml script is changed. For a branch named 'docker' it is always run. If you like to generate the docker file locally use the following commands to build and push

docker build -t gitlab.mrt.uni-karlsruhe.de:21443/mrt/private/meyer/publications/diss/yolino .
docker push gitlab.mrt.uni-karlsruhe.de:21443/mrt/private/meyer/publications/diss/yolino