GRAN

PyTorch implementation of [Efficient Graph Generation with Graph Recurrent Attention Networks, tested for generating street networks

Run the Dataset creation from osmnx.ipynb notebook for creating custom datasets.

Dependencies

Python 3, PyTorch(1.2.0)

Other dependencies can be installed via

pip install -r requirements.txt

Run Demos

Train

• To run the training of experiment X where X is gran_Cities.yaml:

  python run_exp.py -c config/X.yaml

Note:

• Please check the folder config for a full list of configuration yaml files.
• Most hyperparameters in the configuration yaml file are self-explanatory.

Test

• After training, you can specify the test_model field of the configuration yaml file with the path of your best model snapshot, e.g.,

  test_model: exp/gran_grid/xxx/model_snapshot_best.pth

• To run the test of experiments X:

  python run_exp.py -c config/X.yaml -t

Note:

• Please check the evaluation to set up.

@inproceedings{liao2019gran,
  title={Efficient Graph Generation with Graph Recurrent Attention Networks}, 
  author={Liao, Renjie and Li, Yujia and Song, Yang and Wang, Shenlong and Nash, Charlie and Hamilton, William L. and Duvenaud, David and Urtasun, Raquel and Zemel, Richard}, 
  booktitle={NeurIPS},
  year={2019}
}