BEVDet

News

2023.05.07 Improve the occpancy baseline by enlarging the input size and using long-term temporal fusion.
2023.04.28 Support task of occupancy prediction .
2023.04.27 Equip BEVDet with stereo depth estimation.
2023.04.10 Use single head for multi-class prediction.
2023.01.12 Support TensorRT-INT8.
2022.11.24 A new branch of bevdet codebase, dubbed dev2.0, is released. dev2.0 includes the following features:

support BEVPoolv2, whose inference speed is up to 15.1 times the previous fastest implementation of Lift-Splat-Shoot view transformer. It is also far less memory consumption.
use the origin of ego coordinate system as the center of the receptive field instead of the Lidar's.
support conversion of BEVDet from pytorch to TensorRT.
use the long term temporal fusion as SOLOFusion.
train models without CBGS by default.
use key frame for temporal fusion.
Technique Report BEVPoolv2 in English and Blog in Chinese.

History

Main Results

Nuscenes Detection

Config	mAP	NDS	Latency(ms)	FPS	Model	Log
BEVDet-R50	28.3	35.0	29.1/4.2/33.3	30.7	baidu	baidu
BEVDet-R50-CBGS	31.3	39.8	28.9/4.3/33.2	30.1	baidu	baidu
BEVDet-R50-4D-CBGS	31.4/35.4#	44.7/44.9#	29.1/4.3/33.4	30.0	baidu	baidu
BEVDet-R50-4D-Depth-CBGS	36.1/36.2#	48.3/48.4#	35.7/4.0/39.7	25.2	baidu	baidu
BEVDet-R50-4D-Stereo-CBGS	38.2/38.4#	49.9/50.0#	-	-	baidu	baidu
BEVDet-R50-4DLongterm-CBGS	34.8/35.4#	48.2/48.7#	30.8/4.2/35.0	28.6	baidu	baidu
BEVDet-R50-4DLongterm-Depth-CBGS	39.4/39.9#	51.5/51.9#	38.4/4.0/42.4	23.6	baidu	baidu
BEVDet-R50-4DLongterm-Stereo-CBGS	41.1/41.5#	52.3/52.7#	-	-	baidu	baidu
BEVDet-STBase-4D-Stereo-512x1408-CBGS	47.2#	57.6#	-	-	baidu	baidu

# align previous frame bev feature during the view transformation.

Depth: Depth supervised from Lidar as BEVDepth.

Longterm: cat 8 history frame in temporal modeling. 1 by default.

Stereo: A private implementation that concat cost-volumn with image feature before executing model.view_transformer.depth_net.

The latency includes Network/Post-Processing/Total. Training without CBGS is deprecated.

Nuscenes Occupancy

Config	mIOU	Model	Log
BEVDet-Occ-R50-4D-Stereo-2x	36.1	baidu	baidu
BEVDet-Occ-R50-4D-Stereo-2x-384x704	37.3	baidu	baidu
BEVDet-Occ-R50-4DLongterm-Stereo-2x-384x704	39.3	baidu	baidu
BEVDet-Occ-STBase-4D-Stereo-2x	42.0	baidu	baidu

Inference latency with different backends

Backend	256x704	384x1056	512x1408	640x1760
PyTorch	28.9	49.7	78.7	113.4
TensorRT	14.0	22.8	36.5	53.0
TensorRT-FP16	4.94	7.96	12.4	17.9
TensorRT-INT8	2.93	4.41	6.58	9.19
TensorRT-INT8(Xavier)	25.0	-	-	-

Evaluate with BEVDet-R50-CBGS on a RTX 3090 GPU by default. We omit the postprocessing, which spends up to 5 ms with the PyTorch backend.

Get Started

Installation and Data Preparation

step 1. Please prepare environment as that in Docker.

step 2. Prepare bevdet repo by.

git clone https://github.com/HuangJunJie2017/BEVDet.git
cd BEVDet
pip install -v -e .

step 3. Prepare nuScenes dataset as introduced in nuscenes_det.md and create the pkl for BEVDet by running:

python tools/create_data_bevdet.py

step 4. For Occupancy Prediction task, download (only) the 'gts' from CVPR2023-3D-Occupancy-Prediction and arrange the folder as:

└── nuscenes
    ├── v1.0-trainval (existing)
    ├── sweeps  (existing)
    ├── samples (existing)
    └── gts (new)

Train model

# single gpu
python tools/train.py $config
# multiple gpu
./tools/dist_train.sh $config num_gpu

Test model

# single gpu
python tools/test.py $config $checkpoint --eval mAP
# multiple gpu
./tools/dist_test.sh $config $checkpoint num_gpu --eval mAP

Estimate the inference speed of BEVDet

# with pre-computation acceleration
python tools/analysis_tools/benchmark.py $config $checkpoint --fuse-conv-bn
# 4D with pre-computation acceleration
python tools/analysis_tools/benchmark_sequential.py $config $checkpoint --fuse-conv-bn
# view transformer only
python tools/analysis_tools/benchmark_view_transformer.py $config $checkpoint

Estimate the flops of BEVDet

python tools/analysis_tools/get_flops.py configs/bevdet/bevdet-r50.py --shape 256 704

Visualize the predicted result.

Private implementation. (Visualization remotely/locally)

python tools/test.py $config $checkpoint --format-only --eval-options jsonfile_prefix=$savepath
python tools/analysis_tools/vis.py $savepath/pts_bbox/results_nusc.json

Convert to TensorRT and test inference speed.

1. install mmdeploy from https://github.com/HuangJunJie2017/mmdeploy
2. convert to TensorRT
python tools/convert_bevdet_to_TRT.py $config $checkpoint $work_dir --fuse-conv-bn --fp16 --int8
3. test inference speed
python tools/analysis_tools/benchmark_trt.py $config $engine

Acknowledgement

This project is not possible without multiple great open-sourced code bases. We list some notable examples below.

Beside, there are some other attractive works extend the boundary of BEVDet.

BEVerse for multi-task learning.
BEVStereo for stero depth estimation.

Bibtex

If this work is helpful for your research, please consider citing the following BibTeX entry.

@article{huang2022bevpoolv2,
  title={BEVPoolv2: A Cutting-edge Implementation of BEVDet Toward Deployment},
  author={Huang, Junjie and Huang, Guan},
  journal={arXiv preprint arXiv:2211.17111},
  year={2022}
}

@article{huang2022bevdet4d,
  title={BEVDet4D: Exploit Temporal Cues in Multi-camera 3D Object Detection},
  author={Huang, Junjie and Huang, Guan},
  journal={arXiv preprint arXiv:2203.17054},
  year={2022}
}

@article{huang2021bevdet,
  title={BEVDet: High-performance Multi-camera 3D Object Detection in Bird-Eye-View},
  author={Huang, Junjie and Huang, Guan and Zhu, Zheng and Yun, Ye and Du, Dalong},
  journal={arXiv preprint arXiv:2112.11790},
  year={2021}
}

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bevdet's Introduction