This is the original pytorch implementation of Graph WaveNet in the following paper: [Graph WaveNet for Deep Spatial-Temporal Graph Modeling, IJCAI 2019] (https://arxiv.org/abs/1906.00121).
- python 3
- pytorch
- scipy
- numpy
- pandas
- pyaml
Step1: Download METR-LA and PEMS-BAY data from Google Drive or Baidu Yun links provided by DCRNN.
Step2: Follow DCRNN's scripts to preprocess data.
# Create data directories
mkdir -p data/{METR-LA,PEMS-BAY}
# METR-LA
python -m scripts.generate_training_data --output_dir=data/METR-LA --traffic_df_filename=data/metr-la.h5
# PEMS-BAY
python -m scripts.generate_training_data --output_dir=data/PEMS-BAY --traffic_df_filename=data/pems-bay.h5
Train models configured in Table 3 of the paper.
ep=100
dv=cuda:0
mkdir experiment
mkdir experiment/metr
#identity
expid=1
python train.py --device $dv --gcn_bool --adjtype identity --epoch $ep --expid $expid --save ./experiment/metr/metr > ./experiment/metr/train-$expid.log
rm ./experiment/metr/metr_epoch*
#forward-only
expid=2
python train.py --device $dv --gcn_bool --adjtype transition --epoch $ep --expid $expid --save ./experiment/metr/metr > ./experiment/metr/train-$expid.log
rm ./experiment/metr/metr_epoch*
#adaptive-only
expid=3
python train.py --device $dv --gcn_bool --adjtype transition --aptonly --addaptadj --randomadj --epoch $ep --expid $expid --save ./experiment/metr/metr > ./experiment/metr/train-$expid.log
rm ./experiment/metr/metr_epoch*
#forward-backward
expid=4
python train.py --device $dv --gcn_bool --adjtype doubletransition --epoch $ep --expid $expid --save ./experiment/metr/metr > ./experiment/metr/train-$expid.log
rm ./experiment/metr/metr_epoch*
#forward-backward-adaptive
expid=5
python train.py --device $dv --gcn_bool --adjtype doubletransition --addaptadj --randomadj --epoch $ep --expid $expid --save ./experiment/metr/metr > ./experiment/metr/train-$expid.log
rm ./experiment/metr/metr_epoch*
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