SparseBEV
This is the official PyTorch implementation for paper SparseBEV: High-Performance Sparse 3D Object Detection from Multi-Camera Videos. (ICCV 2023)
ä¸æ–‡è§£è¯»ï¼šhttps://zhuanlan.zhihu.com/p/654821380
Model Zoo
| Setting | Pretrain | Training Cost | NDSval | NDStest | FPS | Weights |
|---|---|---|---|---|---|---|
| r50_nuimg_704x256 | nuImg | 21h (8x2080Ti) | 55.6 | - | 15.8 | gdrive |
| r50_nuimg_704x256_400q_36ep | nuImg | 28h (8x2080Ti) | 55.8 | - | 23.5 | gdrive |
| r101_nuimg_1408x512 | nuImg | 2d8h (8xV100) | 59.2 | - | 6.5 | gdrive |
| vov99_dd3d_1600x640_trainval_future | DD3D | 4d1h (8xA100) | 84.9 | 67.5 | - | gdrive |
- We use
r50_nuimg_704x256for ablation studies andr50_nuimg_704x256_400q_36epfor comparison with others. - We recommend using
r50_nuimg_704x256to validate new ideas since it trains faster and the result is more stable. - FPS is measured with AMD 5800X CPU and RTX 3090 GPU (without
fp16). - The noise is around 0.3 NDS.
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
- Download nuScenes from https://www.nuscenes.org/nuscenes and put it in
data/nuscenes. - Download the generated info file from Google Drive and unzip it.
- Folder structure:
data/nuscenes
├── maps
├── nuscenes_infos_test_sweep.pkl
├── nuscenes_infos_train_sweep.pkl
├── nuscenes_infos_val_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
Acknowledgements
Many thanks to these excellent open-source projects:
- 3D Detection: DETR3D, PETR, BEVFormer, BEVDet, StreamPETR
- 2D Detection: AdaMixer, DN-DETR
- Codebase: MMDetection3D, CamLiFlow