greensun0830 / SGNet.pytorch

Pytorch Implementation for Stepwise Goal-Driven Networks for Trajectory Prediction (RA-L/ICRA2022)

Pytorch Implementation for Stepwise Goal-Driven Networks for Trajectory Prediction (RA-L/ICRA2022)

Installation

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Cloning

We use part of the dataloader in Trajectron++, so we include Trajectron++ as a submodule.

git clone --recurse-submodules [e-mail]:ChuhuaW/SGNet.pytorch.git

Environment

• Install conda environment from yml file

conda env create --file SGNet_env.yml

Data

• JAAD and PIE JAAD and PIE can be downloaded from https://github.com/ykotseruba/JAAD and https://github.com/aras62/PIE, respectively. Creating symlinks from the dataset path to ./data

ln -s path/to/dataset/ ./data/

• ETH/UCY We follow Trajectron++ to preprocess data splits for the ETH and UCY datasets in this repository. Please refer to their repository for instruction. After the data is generated, please create symlinks from the dataset path to ./data

ln -s path/to/dataset/ ./data/

Training

Stochastic prediction

• Training on JAAD dataset:

cd SGDNet.Pytorch
python tools/jaad/train_cvae.py --gpu $CUDA_VISIBLE_DEVICES --dataset JAAD --model SGNet_CVAE

• Training on PIE dataset:

cd SGDNet.Pytorch
python tools/pie/train_cvae.py --gpu $CUDA_VISIBLE_DEVICES --dataset PIE --model SGNet_CVAE

• Training on ETH/UCY dataset:

cd SGDNet.Pytorch
python tools/ethucy/train_cvae.py --gpu $CUDA_VISIBLE_DEVICES --dataset ETH --model SGNet_CVAE
python tools/ethucy/train_cvae.py --gpu $CUDA_VISIBLE_DEVICES --dataset HOTEL --model SGNet_CVAE
python tools/ethucy/train_cvae.py --gpu $CUDA_VISIBLE_DEVICES --dataset UNIV --model SGNet_CVAE
python tools/ethucy/train_cvae.py --gpu $CUDA_VISIBLE_DEVICES --dataset ZARA1 --model SGNet_CVAE
python tools/ethucy/train_cvae.py --gpu $CUDA_VISIBLE_DEVICES --dataset ZARA2 --model SGNet_CVAE

Deterministic prediction

• Training on JAAD dataset:

cd SGDNet.Pytorch
python tools/jaad/train_deterministic.py --gpu $CUDA_VISIBLE_DEVICES --dataset JAAD --model SGNet

• Training on PIE dataset:

cd SGDNet.Pytorch
python tools/pie/train_deterministic.py --gpu $CUDA_VISIBLE_DEVICES --dataset PIE --model SGNet

• Training on ETH/UCY dataset:

cd SGDNet.Pytorch
python tools/ethucy/train_deterministic.py --gpu $CUDA_VISIBLE_DEVICES --dataset ETH --model SGNet
python tools/ethucy/train_deterministic.py --gpu $CUDA_VISIBLE_DEVICES --dataset HOTEL --model SGNet
python tools/ethucy/train_deterministic.py --gpu $CUDA_VISIBLE_DEVICES --dataset UNIV --model SGNet
python tools/ethucy/train_deterministic.py --gpu $CUDA_VISIBLE_DEVICES --dataset ZARA1 --model SGNet
python tools/ethucy/train_deterministic.py --gpu $CUDA_VISIBLE_DEVICES --dataset ZARA2 --model SGNet

Evaluation

Stochastic prediction

• Evaluating on JAAD dataset:

cd SGDNet.Pytorch
python tools/jaad/eval_cvae.py --gpu $CUDA_VISIBLE_DEVICES --dataset JAAD --model SGNet_CVAE --checkpoint path/to/checkpoint

• Evaluating on PIE dataset:

cd SGDNet.Pytorch
python tools/pie/eval_cvae.py --gpu $CUDA_VISIBLE_DEVICES --dataset PIE --model SGNet_CVAE --checkpoint path/to/checkpoint

• Evaluating on ETH/UCY dataset:

cd SGDNet.Pytorch
python tools/ethucy/eval_cvae.py --gpu $CUDA_VISIBLE_DEVICES --dataset ETH --model SGNet_CVAE --checkpoint path/to/checkpoint
python tools/ethucy/eval_cvae.py --gpu $CUDA_VISIBLE_DEVICES --dataset HOTEL --model SGNet_CVAE --checkpoint path/to/checkpoint
python tools/ethucy/eval_cvae.py --gpu $CUDA_VISIBLE_DEVICES --dataset UNIV --model SGNet_CVAE --checkpoint path/to/checkpoint
python tools/ethucy/eval_cvae.py --gpu $CUDA_VISIBLE_DEVICES --dataset ZARA1 --model SGNet_CVAE --checkpoint path/to/checkpoint
python tools/ethucy/eval_cvae.py --gpu $CUDA_VISIBLE_DEVICES --dataset ZARA2 --model SGNet_CVAE --checkpoint path/to/checkpoint

Deterministic prediction

• Evaluating on ETH/UCY dataset:

cd SGDNet.Pytorch
python tools/ethucy/eval_deterministic.py --gpu $CUDA_VISIBLE_DEVICES --dataset ETH --model SGNet --checkpoint path/to/checkpoint
python tools/ethucy/eval_deterministic.py --gpu $CUDA_VISIBLE_DEVICES --dataset HOTEL --model SGNet --checkpoint path/to/checkpoint
python tools/ethucy/eval_deterministic.py --gpu $CUDA_VISIBLE_DEVICES --dataset UNIV --model SGNet --checkpoint path/to/checkpoint
python tools/ethucy/eval_deterministic.py --gpu $CUDA_VISIBLE_DEVICES --dataset ZARA1 --model SGNet --checkpoint path/to/checkpoint
python tools/ethucy/eval_deterministic.py --gpu $CUDA_VISIBLE_DEVICES --dataset ZARA2 --model SGNet --checkpoint path/to/checkpoint

Citation

=======

Cloning

We use part of the dataloader in Trajectron++, so we include Trajectron++ as a submodule.

git clone --recurse-submodules [e-mail]:ChuhuaW/SGNet.pytorch.git

Environment

• Install conda environment from yml file

b6f1b50433a7c3109978174c5e6b3f7d2b1f5000

conda env create --file SGNet_env.yml

<<<<<<< HEAD

Data

• JAAD and PIE JAAD and PIE can be downloaded from https://github.com/ykotseruba/JAAD and https://github.com/aras62/PIE, respectively. Creating symlinks from the dataset path to ./data

ln -s path/to/dataset/ ./data/

• ETH/UCY We follow Trajectron++ to preprocess data splits for the ETH and UCY datasets in this repository. Please refer to their repository for instruction. After the data is generated, please create symlinks from the dataset path to ./data

ln -s path/to/dataset/ ./data/

Training

Stochastic prediction

• Training on JAAD dataset:

cd SGDNet.Pytorch
python tools/jaad/train_cvae.py --gpu $CUDA_VISIBLE_DEVICES --dataset JAAD --model SGNet_CVAE

• Training on PIE dataset:

cd SGDNet.Pytorch
python tools/pie/train_cvae.py --gpu $CUDA_VISIBLE_DEVICES --dataset PIE --model SGNet_CVAE

• Training on ETH/UCY dataset:

cd SGDNet.Pytorch
python tools/ethucy/train_cvae.py --gpu $CUDA_VISIBLE_DEVICES --dataset ETH --model SGNet_CVAE
python tools/ethucy/train_cvae.py --gpu $CUDA_VISIBLE_DEVICES --dataset HOTEL --model SGNet_CVAE
python tools/ethucy/train_cvae.py --gpu $CUDA_VISIBLE_DEVICES --dataset UNIV --model SGNet_CVAE
python tools/ethucy/train_cvae.py --gpu $CUDA_VISIBLE_DEVICES --dataset ZARA1 --model SGNet_CVAE
python tools/ethucy/train_cvae.py --gpu $CUDA_VISIBLE_DEVICES --dataset ZARA2 --model SGNet_CVAE

Deterministic prediction

• Training on JAAD dataset:

cd SGDNet.Pytorch
python tools/jaad/train_deterministic.py --gpu $CUDA_VISIBLE_DEVICES --dataset JAAD --model SGNet

• Training on PIE dataset:

cd SGDNet.Pytorch
python tools/pie/train_deterministic.py --gpu $CUDA_VISIBLE_DEVICES --dataset PIE --model SGNet

• Training on ETH/UCY dataset:

cd SGDNet.Pytorch
python tools/ethucy/train_deterministic.py --gpu $CUDA_VISIBLE_DEVICES --dataset ETH --model SGNet
python tools/ethucy/train_deterministic.py --gpu $CUDA_VISIBLE_DEVICES --dataset HOTEL --model SGNet
python tools/ethucy/train_deterministic.py --gpu $CUDA_VISIBLE_DEVICES --dataset UNIV --model SGNet
python tools/ethucy/train_deterministic.py --gpu $CUDA_VISIBLE_DEVICES --dataset ZARA1 --model SGNet
python tools/ethucy/train_deterministic.py --gpu $CUDA_VISIBLE_DEVICES --dataset ZARA2 --model SGNet

Evaluation

Stochastic prediction

• Evaluating on JAAD dataset:

cd SGDNet.Pytorch
python tools/jaad/eval_cvae.py --gpu $CUDA_VISIBLE_DEVICES --dataset JAAD --model SGNet_CVAE --checkpoint path/to/checkpoint

• Evaluating on PIE dataset:

cd SGDNet.Pytorch
python tools/pie/eval_cvae.py --gpu $CUDA_VISIBLE_DEVICES --dataset PIE --model SGNet_CVAE --checkpoint path/to/checkpoint

• Evaluating on ETH/UCY dataset:

cd SGDNet.Pytorch
python tools/ethucy/eval_cvae.py --gpu $CUDA_VISIBLE_DEVICES --dataset ETH --model SGNet_CVAE --checkpoint path/to/checkpoint
python tools/ethucy/eval_cvae.py --gpu $CUDA_VISIBLE_DEVICES --dataset HOTEL --model SGNet_CVAE --checkpoint path/to/checkpoint
python tools/ethucy/eval_cvae.py --gpu $CUDA_VISIBLE_DEVICES --dataset UNIV --model SGNet_CVAE --checkpoint path/to/checkpoint
python tools/ethucy/eval_cvae.py --gpu $CUDA_VISIBLE_DEVICES --dataset ZARA1 --model SGNet_CVAE --checkpoint path/to/checkpoint
python tools/ethucy/eval_cvae.py --gpu $CUDA_VISIBLE_DEVICES --dataset ZARA2 --model SGNet_CVAE --checkpoint path/to/checkpoint

Deterministic prediction

• Evaluating on ETH/UCY dataset:

cd SGDNet.Pytorch
python tools/ethucy/eval_deterministic.py --gpu $CUDA_VISIBLE_DEVICES --dataset ETH --model SGNet --checkpoint path/to/checkpoint
python tools/ethucy/eval_deterministic.py --gpu $CUDA_VISIBLE_DEVICES --dataset HOTEL --model SGNet --checkpoint path/to/checkpoint
python tools/ethucy/eval_deterministic.py --gpu $CUDA_VISIBLE_DEVICES --dataset UNIV --model SGNet --checkpoint path/to/checkpoint
python tools/ethucy/eval_deterministic.py --gpu $CUDA_VISIBLE_DEVICES --dataset ZARA1 --model SGNet --checkpoint path/to/checkpoint
python tools/ethucy/eval_deterministic.py --gpu $CUDA_VISIBLE_DEVICES --dataset ZARA2 --model SGNet --checkpoint path/to/checkpoint

Citation

@ARTICLE{9691856,
  author={Wang, Chuhua and Wang, Yuchen and Xu, Mingze and Crandall, David J.},
  journal={IEEE Robotics and Automation Letters}, 
  title={Stepwise Goal-Driven Networks for Trajectory Prediction}, 
  year={2022}}

b6f1b50433a7c3109978174c5e6b3f7d2b1f5000

- Rank 3rd on nuScences prediction task at 6th AI Driving Olympics, ICRA 2021

The source code and pretrained models will be made availble. Stay tuned.