/RAAN

Primary LanguagePythonMIT LicenseMIT

RAANet: Range-Aware Attention Network for LiDAR-based 3D Object Detection with Auxiliary Density Level Estimation

Installation

The code base of this work is forked from CenterPoint. The environment and dataset setups are inditity.

  1. 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.

  1. Installation
git clone https://github.com/anonymous0522/RAAN.git
cd RAAN

Then follow the setup of CenterPoint: INSTALL

  1. Data Preperation

Currently, we train and evaluate our method on NuScenes dataset.

Please setup the dataset by NUSC from CenterPoint.

  1. 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