RAANet: Range-Aware Attention Network for LiDAR-based 3D Object Detection with Auxiliary Density Level Estimation
The code base of this work is forked from CenterPoint. The environment and dataset setups are inditity.
- The CUDA and Pytorch version that is used for this work:
'CUDA==10.0',
'torch==1.1.0',
'CUDNN==7.5.0'
Warning: We tried CUDA11.0+Torch1.7.1 on RTX3090, the AP performance is significantly lower than the aforementioned environment setup.
- Installation
git clone https://github.com/anonymous0522/RAAN.git
cd RAAN
Then follow the setup of CenterPoint: INSTALL
- Data Preperation
Currently, we train and evaluate our method on NuScenes dataset.
Please setup the dataset by NUSC from CenterPoint.
- Examples of Training and Evaluation
Distributed Train:
python -m torch.distributed.launch —nproc_per_node=NUM_OF_GPU tools/train.py PATH_TO_CONFIG —work_dir PATH_TO_WORK_DIR
Normal Train:
python tools/train.py PATH_TO_CONFIG —work_dir PATH_TO_WORK_DIR
Load and fine tune:
python3 tools/train.py PATH_TO_CONFIG --work_dir PATH_TO_WORK_DIR --load_from PATH_TO_MODEL
Test with test set:
python tools/dist_test.py PATH_TO_CONFIG —work_dir TPATH_TO_WORK_DIR --checkpoint PATH_TO_MODEL --testset —speed_test
With validation set:
python tools/dist_test.py PATH_TO_CONFIG —work_dir TPATH_TO_WORK_DIR --checkpoint PATH_TO_MODEL —speed_test
With distributed val:
python -m torch.distributed.launch —nproc_per_node=NUM_OF_GPU tools/dist_test.py PATH_TO_CONFIG —work_dir TPATH_TO_WORK_DIR --checkpoint PATH_TO_MODEL --testset —speed_test