- Prepare Host System (Ubuntu)
sudo add-apt-repository ppa:ubuntu-toolchain-r/test sudo apt-get update sudo apt install g++-9 g++-9 --version # Should Print Version 9.4.0 or higher- Create Conda Environment
./create_env.sh- Install Pytorch
./install_pytorch_precompiled.sh- Compile NeAT
conda activate neat export CONDA=${CONDA_PREFIX:-"$(dirname $(which conda))/../"} export CC=gcc-9 export CXX=g++-9 export CUDAHOSTCXX=g++-9 mkdir build cd build cmake -DCMAKE_PREFIX_PATH="${CONDA}/lib/python3.8/site-packages/torch/;${CONDA}" .. make -j10 - Get Pepper dataset from here: https://repository.kaust.edu.sa/handle/10754/676019
- Extract datasets
- Update the
main()ofnikon2neat.cppto point to the downloaded dataset directory (the output should be into NeAT/scenes) - Preprocess data using our nikon2neat programm:
mkdir scenes cd NeAT export LD_LIBRARY_PATH=~/anaconda3/envs/neat/lib ./build/bin/nikon2neat- Update configuration file in configs/
- Run reconstruction
cd NeAT export LD_LIBRARY_PATH=~/anaconda3/envs/neat/lib ./build/bin/reconstruct configs/pepper.ini- The result will be written to NeAT/Experiments
- Use tensorboard for easy visualization:
conda activate neat cd NeAT tensorboard --logdir Experiments/ --samples_per_plugin images=100