Anaconda is highly recommended, and the code is tested under:
- Python = 3.6
- Numpy = 1.19
- Pytorch = 1.7
- Pandas = 1.1
- tqdm = 4.49
The datasets adopted in our work are accessible at:
- METR-LA: https://github.com/liyaguang/DCRNN.
- PeMS-Bay: https://github.com/liyaguang/DCRNN.
- NREL: https://www.nrel.gov/grid/solar-power-data.html.
- Beijing-Air: https://www.biendata.xyz/competition/kdd_2018/data. (Register is required)
Key arguments are listed below:
| setting | default | values | help |
|---|---|---|---|
| --name | DualSTN | DualSTN | name of the experiment. It partially decides checkpoint file name. |
| --gpu_ids | -1 | int | index of GPU. Multiple GPUs are not supported. |
| --file_time | N.A. | str | time when the model was trained. specify during evaluation. |
| --dataset_mode | metrla | metrla, nrel, bjair, pemsbay | choose what dataset is used. |
| --epoch | N.A. | int | which epoch to load during evaluation. |
Here are the command for training the model on METR-LA. The framework will automatically evaluate the best checkpoint on the validation/testing set and save all related results to a .pkl file.
python train.py --name DualSTN --dataset_mode metrla --gpu_ids 0
Alternatively you can use train.sh to train the model with different initialization seeds.
train.sh DualSTN metrla 0 2030
Assume the timestamp of running the code is 20210725T160246, the framework will store configurations and checkpoints at
DualSTN/
│
└───checkpoints/
│
└───metrla/
│
└──DualSTN_20210725T160246
For other functions like continue training, please refer the code at (base_options.py)
We provide the pretrained checkpoints for METR-LA, PeMS-Bay, NREL, and Beijing-Air. The checkpoints are stored at checkpoints. The checkpoints are named as DualSTN_{dataset}_{timestamp}. For example, the checkpoint for METR-LA is named as DualSTN_metrla_20210725T160246. The checkpoints are trained with the same hyperparameters as in the paper.
This framework is largely based on CycleGAN. Some parts of the code are adopted from IGNNK. Thanks for their great works! :)