Improving 3D Object Detection with Channel-wise Transformer

pip install -r requirement.txt
python setup.py develop

# Download KITTI and organize it into the following form:
├── data
│   ├── kitti
│   │   │── ImageSets
│   │   │── training
│   │   │   ├──calib & velodyne & label_2 & image_2 & (optional: planes)
│   │   │── testing
│   │   │   ├──calib & velodyne & image_2

# Generatedata infos:
python -m pcdet.datasets.kitti.kitti_dataset create_kitti_infos tools/cfgs/dataset_configs/kitti_dataset.yaml

# Download Waymo and organize it into the following form:
├── data
│   ├── waymo
│   │   │── ImageSets
│   │   │── raw_data
│   │   │   │── segment-xxxxxxxx.tfrecord
|   |   |   |── ...
|   |   |── waymo_processed_data
│   │   │   │── segment-xxxxxxxx/
|   |   |   |── ...
│   │   │── pcdet_gt_database_train_sampled_xx/
│   │   │── pcdet_waymo_dbinfos_train_sampled_xx.pkl

# Install tf 2.1.0
# Install the official waymo-open-dataset by running the following command:
pip3 install --upgrade pip
pip3 install waymo-open-dataset-tf-2-1-0 --user

# Extract point cloud data from tfrecord and generate data infos:
python -m pcdet.datasets.waymo.waymo_dataset --func create_waymo_infos --cfg_file tools/cfgs/dataset_configs/waymo_dataset.yaml

python train.py --cfg_file ${CONFIG_FILE}

# e.g.,
python train.py --cfg_file tools/cfgs/kitti_models/second_ct3d.yaml

bash scripts/dist_train.sh ${NUM_GPUS} --cfg_file ${CONFIG_FILE}
# or 
bash scripts/slurm_train.sh ${PARTITION} ${JOB_NAME} ${NUM_GPUS} --cfg_file ${CONFIG_FILE}

# e.g.,
bash scripts/dist_train.sh 8 --cfg_file tools/cfgs/kitti_models/second_ct3d.yaml

python test.py --cfg_file ${CONFIG_FILE} --ckpt ${CKPT}

# e.g., 
python test.py --cfg_file tools/cfgs/kitti_models/second_ct3d.yaml --ckpt output/kitti_models/second_ct3d/default/kitti_val.pth