Social LSTM implementation in PyTorch

Project details

Semester project of Master of Computer Science in EPFL
Student name : Baran Nama
Advisor: Alexandre Alahi
Presentation : https://drive.google.com/file/d/1biC23s1tbsyDETKKBW8PFXWYyyhNEAuI/view?usp=sharing

Implementation details

Baseline implementation: https://github.com/vvanirudh/social-lstm-pytorch
Paper: http://cvgl.stanford.edu/papers/CVPR16_Social_LSTM.pdf
Detailed info about challange and datasets: https://www.aicrowd.com/challenges/trajnet-a-trajectory-forecasting-challenge Made improvements: Please see attached presentation

Documentation

• generator.py : Python script for generating artifical datasets
• helper.py: Python script includes various helper methods
• hyperparameter.py: Pyton script for random best parameter selection for a model
• make_directories.sh: Bash script for creation of file structure
• model.py: Python file includes Social LSTM model definition
• olstm_model.py: Python file includes Occupancy LSTM model definition
• olstm_train.py: Python script for training Occupancy LSTM model
• test.py: Python script for model testing and getting output txt file for submission
• train.py: Python script for training Social LSTM model
• utils.py: Python script for handling input train/test/validation data and batching it
• validation.py: Python script for externally evaluate a trained model by getting validation error
• visualize.py: Python script for visualizing predicted trajectories during train/test/validation sessions
• vlstm_model.py: Python file includes Vanilla LSTM model definition
• vlstm_train: Python script for training Vanilla LSTM model

How to deploy

1. Fork the repository
2. Start train a model >>> python train/olstm_train/vlstm.train.py - -[Parameter set]
3. If necesarry file structure is not exist (which is the initial situation), train script will run make_directories.sh and this command will automatically create file structure
4. Enjoy!

Results

Reference: http://trajnet.stanford.edu/result.php?cid=1