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Getting Started with CenterPoint on nuScenes

Modified from det3d's original document.

Prepare data

Download data and organise as follows

# For nuScenes Dataset         
└── NUSCENES_DATASET_ROOT
       ├── samples       <-- key frames
       ├── sweeps        <-- frames without annotation
       ├── maps          <-- unused
       ├── v1.0-trainval <-- metadata

Create a symlink to the dataset root

mkdir data && cd data
ln -s DATA_ROOT 
mv DATA_ROOT nuScenes # rename to nuScenes

Remember to change the DATA_ROOT to the actual path in your system.

Create data

Data creation should be under the gpu environment.

# nuScenes
python tools/create_data.py nuscenes_data_prep --root_path=NUSCENES_TRAINVAL_DATASET_ROOT --version="v1.0-trainval" --nsweeps=10

In the end, the data and info files should be organized as follows

# For nuScenes Dataset 
└── CenterPoint
       └── data    
              └── nuScenes 
                     ├── samples       <-- key frames
                     ├── sweeps        <-- frames without annotation
                     ├── maps          <-- unused
                     |── v1.0-trainval <-- metadata and annotations
                     |── infos_train_10sweeps_withvelo_filter_True.pkl <-- train annotations
                     |── infos_val_10sweeps_withvelo_filter_True.pkl <-- val annotations
                     |── dbinfos_train_10sweeps_withvelo.pkl <-- GT database info files
                     |── gt_database_10sweeps_withvelo <-- GT database

Train & Evaluate in Command Line

Now we only support training and evaluation with gpu. Cpu only mode is not supported.

Use the following command to start a distributed training using 4 GPUs. The models and logs will be saved to work_dirs/CONFIG_NAME

python -m torch.distributed.launch --nproc_per_node=4 ./tools/train.py CONFIG_PATH

For distributed testing with 4 gpus,

python -m torch.distributed.launch --nproc_per_node=4 ./tools/dist_test.py CONFIG_PATH --work_dir work_dirs/CONFIG_NAME --checkpoint work_dirs/CONFIG_NAME/latest.pth

For testing with one gpu and see the inference time,

python ./tools/dist_test.py CONFIG_PATH --work_dir work_dirs/CONFIG_NAME --checkpoint work_dirs/CONFIG_NAME/latest.pth --speed_test

The pretrained models and configurations are in MODEL ZOO.

Tracking

Please refer to tracking_scripts to reproduce all tracking results. The detection files are provided in the MODEL ZOO

Test Set

Organize your dataset as follows

# For nuScenes Dataset 
└── CenterPoint
       └── data    
              └── nuScenes 
                     ├── samples       <-- key frames
                     ├── sweeps        <-- frames without annotation
                     ├── maps          <-- unused
                     |── v1.0-trainval <-- metadata and annotations
                     |── infos_train_10sweeps_withvelo_filter_True.pkl <-- train annotations
                     |── infos_val_10sweeps_withvelo_filter_True.pkl <-- val annotations
                     |── dbinfos_train_10sweeps_withvelo.pkl <-- GT database info files
                     |── gt_database_10sweeps_withvelo <-- GT database 
                     └── v1.0-test <-- main test folder 
                            ├── samples       <-- key frames
                            ├── sweeps        <-- frames without annotation
                            ├── maps          <-- unused
                            |── v1.0-test <-- metadata and annotations
                            |── infos_test_10sweeps_withvelo.pkl <-- test info

Download the centerpoint_voxel_1440_dcn_flip here, save it into work_dirs/nusc_0075_dcn_flip_track, then run the following commands in the main folder to get detection prediction

python tools/dist_test.py configs/nusc/voxelnet/nusc_centerpoint_voxelnet_0075voxel_dcn_flip.py --work_dir work_dirs/nusc_centerpoint_voxelnet_dcn_0075voxel_flip_testset  --checkpoint work_dirs/nusc_0075_dcn_flip_track/voxelnet_converted.pth  --testset --speed_test

With the generated detection files, you can create the tracking prediction by running

bash tracking_scripts/centerpoint_voxel_1440_dcn_flip_testset.sh