EmreTaha / Social Lstm Vehicletrajectory
Licence: gpl-3.0
Social LSTM using PyTorch for Vehicle Data
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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 available is normalized between -1 and 1. Also this version of code is only for GPU's.
Requirements
- Python 3.6
- Seaborn (https://seaborn.pydata.org/)
- PyTorch 0.4 (http://pytorch.org/)
- Numpy
- Matplotlib
- Scipy
- GPU
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_vehiclesfolder - To train the model run
python3 social_lstm/train.py(With default parameters) - To test the model run
python3 social_lstm/sample.py --epoch=nwherenis 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.pywith default parameters
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