🌟 We updated the repo with training code! 🌟
Axel Sauer 1, 2,
Nikolay Savinov 1,
Andreas Geiger 1, 3
1 ETH Zurich, 2 TU Munich, 3 MPI for Intelligent Systems and University of Tubingen
If you use this implementation, please cite our CoRL 2018 paper.
@inproceedings{Sauer2018CORL,
author={Sauer, Axel and Savinov, Nikolay and Geiger, Andreas},
title={Conditional Affordance Learning for Driving in Urban Environments},
booktitle={Conference on Robot Learning (CoRL)},
year={2018}
}
# install anaconda if you don't have it yet
wget https://repo.continuum.io/archive/Anaconda3-5.3.0-Linux-x86_64.sh
bash Anaconda3-5.3.0-Linux-x86_64.sh
source ~/.profile
# or use source ~/.bashrc - depending on where anaconda was added to PATH as the result of the installationNow we will:
- create a conda environment named CAL and install all dependencies
- download the binaries for CARLA version 0.8.2 [CARLA releases]
- download the model weights
git clone https://github.com/xl-sr/CAL.git
cd CAL
# create the training environment
conda env create -f requirements.yml
source activate CAL
# run download script
./download_binaries_and_models.sh
In CARLA_0.8.2/ start the server with for example: (see the CARLA documentation for details)
./CarlaUE4.sh Town01 -carla-server -windowed -benchmark -fps=20 -ResX=800 - ResY=600Open a second terminal, and run:
python python_client/driving_benchmark.py -c Town01 -v -n test --corl-2017
If you want to run the CORL 2017 benchmark add '--corl-2017'
python python_client/driving_benchmark.py -c Town01 -v -n test --corl-2017
If you want to continue an experiment, you can add the 'continue-experiment' flag.
cd training/
# download and untar the dataset
wget https://s3.eu-central-1.amazonaws.com/avg-projects/conditional_affordance_learning/dataset.tar.gz
tar -xzvf dataset.tar.gzNow, open training_CAL.ipynb. The notebook walks you through the steps to train a network on the dataset.
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