Run python train.py --config config/config_full.yaml. Remember to change the path to the dataset folder in the config file and in the train.py file.
Run python test.py -w <file>. Remember to change the path to the dataset folder in the config file and in the test.py file. If you want to test on the validation set, uncomment lines 41-44 in test.py.
After struggling a bit to install MinkowskiEngine, the procedure below is the one that worked out on my machine (operations to be done in that specific order):
conda create --name hapt3d python=3.9
conda activate hapt3d
pip install torch==1.12.1+cu113 --extra-index-url https://download.pytorch.org/whl/cu113 --no-cache-dir
pip install numpy==1.24.2
pip install setuptools==60.0
pip install pykeops --no-cache-di
pip3 install -U git+https://github.com/NVIDIA/MinkowskiEngine -v --no-deps
pip install pytorch-lightning==1.9.0 --no-deps
pip install fsspec
pip install lightning-utilities
pip install tqdm
pip install pyyaml
pip install torchmetrics==1.4.1
pip install ipdb
pip install open3d
pip install tensorboard
pip install torchmetrics
pip install hdbscan
pip install distinctipy
pip install optuna==3.6.1
pip install optuna-integration
Good luck :)
Alternatively, you could simply use docker. Build it first via make build, then you can train via doing make train and test with make test CHECKPOINT=<file>.