Code for "GATraj: A Graph- and Attention-based Multi-Agent Trajectory Prediction Model"
pip install -r requirements.txt
The Default settings are to train on ETH-univ. Data cache and models will be in the subdirectory "./savedata/0/".
git clone https://github.com/mengmengliu1998/GATraj.git
cd GATraj
python train.py --test_set <dataset to train> --num_epochs 1000 --x_encoder_layers 3 --eta_min 1e-5 --batch_size 32\
--learning_rate 5e-4 --randomRotate True --final_mode 20 --neighbor_thred 10\
--using_cuda True --clip 1 --pass_time 2 --ifGaussian False --SR True --input_offset True
Configuration files are also created after the first run, arguments could be modified through configuration files or command line. Priority: command line > configuration files > default values in script.
The datasets are selected on arguments '--test_set'. Five datasets in ETH/UCY are corresponding to the value of [0,1,2,3,4] ([eth, hotel, zara1, zara2, univ]).
This command is to train model for ETH-univ
python train.py --test_set 0 --num_epochs 1000 --x_encoder_layers 3 --eta_min 1e-5 --batch_size 32\
--learning_rate 5e-4 --randomRotate True --final_mode 20 --neighbor_thred 10\
--using_cuda True --clip 1 --pass_time 2 --ifGaussian False --SR True --input_offset True
We provide the trained model weights in the subdirectory "./savedata/". This command is to test model for ETH-univ, just add --phase test --load_model 1000 to the end of this training command.
python train.py --test_set 0 --num_epochs 1000 --x_encoder_layers 3 --eta_min 1e-5 --batch_size 32\
--learning_rate 5e-4 --randomRotate True --final_mode 20 --neighbor_thred 10\
--using_cuda True --clip 1 --pass_time 2 --ifGaussian False --SR True --input_offset True --phase test --load_model 1000
If you find this repo useful, please consider citing our paper
@article{cheng2022gatraj,
title={GATraj: A Graph-and Attention-based Multi-Agent Trajectory Prediction Model},
author={Cheng, Hao and Liu, Mengmeng and Chen, Lin and Broszio, Hellward and Sester, Monika and Yang, Michael Ying},
journal={arXiv preprint arXiv:2209.07857},
year={2022}
}The code base heavily borrows from SR-LSTM
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