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SparseBEV

PWC

This is the official PyTorch implementation for our ICCV 2023 paper:

SparseBEV: High-Performance Sparse 3D Object Detection from Multi-Camera Videos<br> Haisong Liu, Yao Teng, Tao Lu, Haiguang Wang, Limin Wang<br>Nanjing University, Shanghai AI Lab

中文解读:https://zhuanlan.zhihu.com/p/654821380

News

Model Zoo

SettingPretrainTraining CostNDS<sub>val</sub>NDS<sub>test</sub>FPSWeights
r50_nuimg_704x256nuImg21h (8x2080Ti)55.6-15.8gdrive
r50_nuimg_704x256_400q_36epnuImg28h (8x2080Ti)55.8-23.5gdrive
r101_nuimg_1408x512nuImg2d8h (8xV100)59.2-6.5gdrive
vov99_dd3d_1600x640_trainval_futureDD3D4d1h (8xA100)84.967.5-gdrive
vit_eva02_1600x640_trainval_futureEVA0211d (8xA100)85.370.2-gdrive

Environment

Install PyTorch 2.0 + CUDA 11.8:

conda create -n sparsebev python=3.8
conda activate sparsebev
conda install pytorch==2.0.0 torchvision==0.15.0 pytorch-cuda=11.8 -c pytorch -c nvidia

or PyTorch 1.10.2 + CUDA 10.2 for older GPUs:

conda create -n sparsebev python=3.8
conda activate sparsebev
conda install pytorch==1.10.2 torchvision==0.11.3 cudatoolkit=10.2 -c pytorch

Install other dependencies:

pip install openmim
mim install mmcv-full==1.6.0
mim install mmdet==2.28.2
mim install mmsegmentation==0.30.0
mim install mmdet3d==1.0.0rc6
pip install setuptools==59.5.0
pip install numpy==1.23.5

Install turbojpeg and pillow-simd to speed up data loading (optional but important):

sudo apt-get update
sudo apt-get install -y libturbojpeg
pip install pyturbojpeg
pip uninstall pillow
pip install pillow-simd==9.0.0.post1

Compile CUDA extensions:

cd models/csrc
python setup.py build_ext --inplace

Prepare Dataset

  1. Download nuScenes from https://www.nuscenes.org/nuscenes and put it in data/nuscenes.
  2. Download the generated info file from Google Drive and unzip it.
  3. Folder structure:
data/nuscenes
├── maps
├── nuscenes_infos_test_sweep.pkl
├── nuscenes_infos_train_sweep.pkl
├── nuscenes_infos_train_mini_sweep.pkl
├── nuscenes_infos_val_sweep.pkl
├── nuscenes_infos_val_mini_sweep.pkl
├── samples
├── sweeps
├── v1.0-test
└── v1.0-trainval

These *.pkl files can also be generated with our script: gen_sweep_info.py.

Training

Download pretrained weights and put it in directory pretrain/:

pretrain
├── cascade_mask_rcnn_r101_fpn_1x_nuim_20201024_134804-45215b1e.pth
├── cascade_mask_rcnn_r50_fpn_coco-20e_20e_nuim_20201009_124951-40963960.pth

Train SparseBEV with 8 GPUs:

torchrun --nproc_per_node 8 train.py --config configs/r50_nuimg_704x256.py

Train SparseBEV with 4 GPUs (i.e the last four GPUs):

export CUDA_VISIBLE_DEVICES=4,5,6,7
torchrun --nproc_per_node 4 train.py --config configs/r50_nuimg_704x256.py

The batch size for each GPU will be scaled automatically. So there is no need to modify the batch_size in config files.

Evaluation

Single-GPU evaluation:

export CUDA_VISIBLE_DEVICES=0
python val.py --config configs/r50_nuimg_704x256.py --weights checkpoints/r50_nuimg_704x256.pth

Multi-GPU evaluation:

export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
torchrun --nproc_per_node 8 val.py --config configs/r50_nuimg_704x256.py --weights checkpoints/r50_nuimg_704x256.pth

Timing

FPS is measured with a single GPU:

export CUDA_VISIBLE_DEVICES=0
python timing.py --config configs/r50_nuimg_704x256.py --weights checkpoints/r50_nuimg_704x256.pth

Visualization

Visualize the predicted bbox:

python viz_bbox_predictions.py --config configs/r50_nuimg_704x256.py --weights checkpoints/r50_nuimg_704x256.pth

Visualize the sampling points (like Fig. 6 in the paper):

python viz_sample_points.py --config configs/r50_nuimg_704x256.py --weights checkpoints/r50_nuimg_704x256.pth

Acknowledgements

Many thanks to these excellent open-source projects: