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Social LSTM using PyTorch for Vehicle Data

This code/implementation is edited version of Anirudh Vemula's code. It is edited for vehicle trajectory data . If you are using this code for your work, please cite the original paper and Anirudh Vemula's original code.

Getting Started

The dataset avaliable is normalized between -1 and 1. Also this version of code only for GPU's.

Requirements

How to Run

  • Before running the code, create the required directories by running the script make_directories.sh
  • Unzip the data files inside the data_vehicles folder
  • To train the model run python3 social_lstm/train.py (With default parameters)
  • To test the model run python3 social_lstm/sample.py --epoch=n where n is the epoch at which you want to load the saved model. (Also since we use validation, by the end of training you should see the best epoch)
  • To visualize and plot the grid run python3 social_lstm/visualize.py with default parameters

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  • Python 97.5%
  • Jupyter Notebook 2.3%
  • Shell 0.2%