Goal Inference GAN (GIGAN)

GIGAN is an implementation of Cusumano's Inference strategy that samples using trajectories from SGAN. This project is part of the "Intent Prediction" 2019 INTEL Labs project in collaboration with CIMAT.

Table of contents

1. Installation guide
2. Running samples
3. Library structure
4. Bibliography

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1. Installation guide

First make a clone of the project running:

$ git clone https://github.com/cimat-ris/GoalInferenceGAN.git

And before opening it up we, should run the following scripts:

$ cd GoalInferenceGAN
$ ./scripts/install_repositories.sh
$ ./scripts/install_dataset.sh    

• The first script will clone our cimat-ris/sgan fork that has a couple tweaks for being called as a python library.
• The second script will download datasets to the samples/datasets folder

GIGAN is a PyCharm project, it has the ".idea" folder so it can be opened with it by default. The sgan clone should be at the same folder level as the GoalInferenceGAN for PyCharm to find it correctly.

2. Library structure

GoalInferenceGAN sources consists on the following folders:

• gigan : The python library itself, hence can be used and imported into any other python project using the import command. It's modules are as follows:
  • data : Common data structures for entities such as trajectories, obstacles, agents, etc...
  • extensions : Special code that is meant to extend functionality of dependant python libraries. Related helper functions for such libraries will be here too.
  • models : Prebuilt inference models will be stored here. Though they might be only used for particular datasets, since models depend on the problem, this will be helpful reference for further implementations.
  • utils : Operations too common in the python world, probably copy-pasted from the internet. Might have some actual useful code for mathematical evaluations.
  • visualization : Code that will depend mostly on OpenCV for displaying results and annotated video playback.
• scripts : Shell scripts for UNIX systems (tested on Ubuntu at least) for automatic download and installation of what might be needed in order to run the samples.
• samples : Actual example usage of the GIGAN library. Should contain many executables for real-life problems using the framework we created and give a good idea on how to improve and expand functionality of existing models.
• practice : Mostly useless code, personal tests on third-party code in order to evaluate what we can use for GIGAN in future releases.
• tests : Contains test programs for testing out accuracy and correct results of individual components.
• docs : Automated document-generation using sphynx. Hopefully most of the functions in GIGAN will have proper documentation for it to be generated here.

3. Running samples

Cusumano's Goal Inference model

Inspired by paper [2], the basic inference framework using Metropolis-Hastings runs the model: START, GOAL, PATH, NOISY_PATH] but instead of sampling the PATH on a Rapidly-growing Random Tree it uses SGAN's trained neuron for path generation. Execution is simply by:

$ python samples/gigan_cusumano.py

It should be noted you need to install datasets as stated on 1. Running gigan_cusumano.py should begin a video playback of the frames, performing inference each single frame and drawing results (inferred goal for each pedestrian) on the image. Lines on targets trajectory represent ground-truth and observed data using different colors.

4. Bibliography

• [1] Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networks. Agrim Gupta, Justin Johnson, Fei-Fei Li, Silvio Savarese, Alexandre Alahi. Presented at CVPR 2018. GitHub Project page.

• [2] Probabilistic programs for inferring the goals of autonomous agents. Marco F. Cusumano-Towner, Alexey Radul, David Wingate, Vikash K. Mansinghka arXiv page