The official implementation of DA, based on the official implementation of Point-MAE. Slight modification.
conda create -n pt python=3.7
conda activate pt
conda install pytorch==1.10.1 torchvision==0.11.2 cudatoolkit=11.3 -c pytorch -c conda-forge
pip install -r requirements.txt
cd ./extensions/chamfer_dist
python setup.py install --user
cd ../../
pip install "git+https://github.com/erikwijmans/Pointnet2_PyTorch.git#egg=pointnet2_ops&subdirectory=pointnet2_ops_lib"
pip install --upgrade https://github.com/unlimblue/KNN_CUDA/releases/download/0.2/KNN_CUDA-0.2-py3-none-any.whl
We use ShapeNet, ScanObjectNN, ModelNet40 and ShapeNetPart in this work. See DATASET.md for details.
All pretrain models are in reserve directory.
.
├── fewshot_way_10_shot_10
│ ├── Adapter
│ ├── Bias
│ ├── DA-Heavy
│ ├── DA-Light
│ ├── Full
│ ├── Head
│ ├── result.csv
│ ├── VPT-Deep
│ └── VPT-Shallow
├── fewshot_way_10_shot_20
│ ├── Adapter
│ ├── Bias
│ ├── DA-Heavy
│ ├── DA-Light
│ ├── Full
│ ├── Head
│ ├── result.csv
│ ├── VPT-Deep
│ └── VPT-Shallow
├── fewshot_way_5_shot_10
│ ├── Adapter
│ ├── Bias
│ ├── DA-Heavy
│ ├── DA-Light
│ ├── Full
│ ├── Head
│ ├── result.csv
│ ├── VPT-Deep
│ └── VPT-Shallow
├── fewshot_way_5_shot_20
│ ├── Adapter
│ ├── Bias
│ ├── DA-Heavy
│ ├── DA-Light
│ ├── Full
│ ├── Head
│ ├── result.csv
│ ├── VPT-Deep
│ └── VPT-Shallow
├── modelnet
│ ├── Adapter
│ ├── Bias
│ ├── DA-Heavy
│ ├── DA-Light
│ ├── Full
│ ├── Head
│ ├── result.csv
│ ├── Scratch
│ ├── VPT-Deep
│ └── VPT-Shallow
├── pretrain.pth
├── scan_hardest
│ ├── Adapter
│ ├── Bias
│ ├── DA-Heavy
│ ├── DA-Light
│ ├── Full
│ ├── Head
│ ├── result.csv
│ ├── Scratch
│ ├── VPT-Deep
│ └── VPT-Shallow
├── scan_objbg
│ ├── Adapter
│ ├── Bias
│ ├── DA-Heavy
│ ├── DA-Light
│ ├── Full
│ ├── Head
│ ├── result.csv
│ ├── Scratch
│ ├── VPT-Deep
│ └── VPT-Shallow
├── scan_objonly
│ ├── Adapter
│ ├── Bias
│ ├── DA-Heavy
│ ├── DA-Light
│ ├── Full
│ ├── Head
│ ├── result.csv
│ ├── Scratch
│ ├── VPT-Deep
│ └── VPT-Shallow
└── shapenetpart
├── Adapter
├── Bias
├── DA-Light
├── DA-Naive
├── Full
├── Head
├── result.csv
├── Scratch
├── VPT-Deep
└── VPT-Shallow
ModelNet (Scan_*), run:
# fine-tune
CUDA_VISIBLE_DEVICES=<GPUs> python main.py --config reserve/modelnet/DA-Light/config.yaml --finetune_model --exp_name <output_file_name> --ckpts reserve/pretrain.pth
# test
CUDA_VISIBLE_DEVICES=<GPUs> python main.py --config reserve/modelnet/DA-Light/config.yaml --test --exp_name <output_file_name> --ckpts reserve/modelnet/DA-Light/ckpt-best.pth
Few-shot learning, run:
# train
CUDA_VISIBLE_DEVICES=<GPUs> python main.py --config reserve/fewshot_way_5_shot_10/DA-Light/fold_0/config.yaml --finetune_model --ckpts reserve/pretrain.pth --exp_name <output_file_name> --way <5 or 10> --shot <10 or 20> --fold <0-9>
Part segmentation on ShapeNetPart, run:
# fine-tune
CUDA_VISIBLE_DEVICES=<GPUs> python main.py --config reserve/shapenetpart/DA-Light/config.yaml --partseg --exp_name <output_file_name> --ckpts reserve/pretrain.pth
# test
CUDA_VISIBLE_DEVICES=<GPUs> python main.py --config reserve/shapenetpart/DA-Light/config.yaml --test_partseg --exp_name <output_file_name> --ckpts reserve/shapenetpart/DA-Light/ckpt-best.pth
Single GPU / Mutiple GPUs (DataParallel)
CUDA_VISIBLE_DEVICES=<GPU> python main.py --config <cfg_path> --exp_name <output_file_name>
Multiple GPUs (DistributedDataParallel)
CUDA_VISIBLE_DEVICES=<GPU> torchrun --nproc_per_node <num_of_gpus> main.py --config <cfg_path> --exp_name <output_file_name> --launcher pytorch
Test
CUDA_VISIBLE_DEVICES=<GPU> python main.py --config cfgs/finetune/modelnet.yaml --test --exp_name <output_file_name>
Our codes are built upon Point-MAE.