Structural RNN using PyTorch

This code/implementation is available for research purposes. If you are using this code for your work, please cite the following paper

Anirudh Vemula, Katharina Muelling and Jean Oh. Social Attention : Modeling Attention in Human Crowds. Submitted to the International Conference on Robotics and Automation (ICRA) 2018.

Or use the following BibTeX entry

@ARTICLE{2017arXiv171004689V,
   author = {{Vemula}, A. and {Muelling}, K. and {Oh}, J.},
    title = "{Social Attention: Modeling Attention in Human Crowds}",
  journal = {ArXiv e-prints},
archivePrefix = "arXiv",
   eprint = {1710.04689},
 primaryClass = "cs.RO",
 keywords = {Computer Science - Robotics, Computer Science - Learning},
     year = 2017,
    month = oct,
   adsurl = {http://adsabs.harvard.edu/abs/2017arXiv171004689V},
  adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}

Author : Anirudh Vemula

Affiliation : Robotics Institute, Carnegie Mellon University

License : GPL v3

Requirements

• Python 3
• Seaborn (https://seaborn.pydata.org/)
• PyTorch (http://pytorch.org/)
• Numpy
• Matplotlib
• Scipy

How to Run

• Before running the code, create the required directories by running the script make_directories.sh
• To train the model run python srnn/train.py (See the code to understand all the arguments that can be given to the command)
• To test the model run python srnn/sample.py --epoch=n where n is the epoch at which you want to load the saved model. (See the code to understand all the arguments that can be given to the command)