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

Model name	Avarage error	Final error	Mean error
Social LSTM	1.3865	2.098	0.675
Occupancy LSTM	2.1105	3.12	1.101
Vanilla LSTM	2.107	3.114	1.1

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