Official implementation for our AAAI2024 paper: "Semi-Supervised Class-Agnostic Motion Prediction with Pseudo Label Regeneration and BEVMix" [Arxiv]
- Pytorch >= 1.7.1
# git clone this repository
git clone https://github.com/kwwcv/SSMP
cd SSMP
- Download the nuScenes data.
# modified the following paths in gen_data.py, gen_GSdata.py, and data_utils.py
# sys.path.append('root_path/SSMP')
# sys.path.append('root_path/SSMP/nuscenes-devkit/python-sdk/')
-
Run command
python data/gen_data.py
to generate preprocessed BEV data for training, validating, and testing. Refer to MotionNet andpython data/gen_data.py -h
for detailed instructions. -
Install the ground segmentation algorithm following Patchwork++. One can also try removing the ground points by simply setting a threshold along the Z-axis.
# modified the following path in gen_GSdata.py
# patchwork_module_path = "root_path/patchwork-plusplus/build/python_wrapper"
- Run command
python data/gen_GSdata.py
to generate preprocessed ground-removed BEV data for training.
Randomly divide the training data into labeled and unlabeled data sets.
# stage 1: train model with only labeled data
python train_stage1.py --data [bev training folder] --seed [random seed] --log
# stage two: train model with both labeled and unlabeled data
python train_stage2.py --data [bev training folder] --GSdata_root [ground removal bev training folder] \
--resume [stage 1 trained model] --seed [random seed] --if_lr --if_bevmix --log
# Keep the same [random seed] to make sure stage 1 and stage 2 are using the same labeled data set
# when using [random seed] to randomly divide unlabeled and labeled data.
One can also use the same divided labeled and unlabeled data sets as used in the paper to train the model.
# stage 1
python train_stage1.py --data [bev training folder] --preset_semi [split file] --log
# stage 2
python train_stage2.py --data [bev training folder] --GSdata_root [ground removal bev training folder] \
--resume [stage 1 trained model] --preset_semi [split file] --if_lr --if_bevmix --log
Ratio | Path |
---|---|
1% (semi) | SSMP1% |
5% (semi) | SSMP5% |
10% (semi) | SSMP10% |
python test.py --data [bev testing folder] --model [model path] \
--log_path [path to save results] --jitter
@inproceedings{wang2024semi,
title={Semi-supervised Class-Agnostic Motion Prediction with Pseudo Label Regeneration and BEVMix},
author={Wang, Kewei and Wu, Yizheng and Pan, Zhiyu and Li, Xingyi and Xian, Ke and Wang, Zhe and Cao, Zhiguo and Lin, Guosheng},
booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
volume={38},
number={6},
pages={5490--5498},
year={2024}
}
Our project is based on MotionNet
The optimal transport solver is adopted from Self-Point-Flow
This project is licensed under NTU S-Lab License 1.0