The main dependencies of the project are the following:
python: 3.10.6
cuda: 11.6
You can set up a conda environment as follows
conda create --name=mask3d python=3.10.6
conda activate mask3d
conda update -n base -c defaults conda
conda install openblas-devel -c anaconda
pip install torch torchvision --extra-index-url https://download.pytorch.org/whl/cu116
pip install torch-scatter -f https://data.pyg.org/whl/torch-1.12.1+cu116.html
pip install ninja==1.10.2.3
pip install pytorch-lightning fire imageio tqdm wandb python-dotenv pyviz3d scipy plyfile scikit-learn trimesh loguru albumentations volumentations
pip install antlr4-python3-runtime==4.8
pip install black==21.4b2
pip install omegaconf==2.0.6 hydra-core==1.0.5 --no-deps
pip install 'git+https://github.com/facebookresearch/detectron2.git@710e7795d0eeadf9def0e7ef957eea13532e34cf' --no-deps
cd third_party/pointnet2 && python setup.py install
git clone https://github.com/NVIDIA/MinkowskiEngine.git
cd MinkowskiEngine
python setup.py install --blas_include_dirs=${CONDA_PREFIX}/include --blas=openblas
Data preparation: create a symbolic link from the dataset directory and to the saved checkpoints in CSCC
cd Mask3D
ln -s /l/users/jameel.hassan/data/scannet/ ./data
ln -s /l/users/jameel.hassan/Mask3D/saved/ ./saved
sh scripts/scannet200/scannet200_val.sh 6
checkpoint: https://drive.google.com/file/d/1fJovEXJySzKtaz-xcJEA6cfsSCIBH9aO/view?usp=sharing
cd Mask3D
python main_instance_segmentation.py \
general.train_mode=false \
general.checkpoint=<path to the checkpoint>