This's an implementation of deepmind Visual Interaction Networks paper using pytorch

Last update: Dec 06, 2022

Overview

Visual-Interaction-Networks

An implementation of Deepmind visual interaction networks in Pytorch.

Introduction

For the purpose of understanding the challenge of relational reasoning. they publised VIN that involves predicting the future in a physical scene. From just a glance, humans can infer not only what objects are where, but also what will happen to them over the upcoming seconds, minutes and even longer in some cases. For example, if you kick a football against a wall, your brain predicts what will happen when the ball hits the wall and how their movements will be affected afterwards (the ball will ricochet at a speed proportional to the kick and - in most cases - the wall will remain where it is).

Architecture

Data

I used [email protected] physics engine to generate the data.

Just run the physics_engine.py

Usage

Main Dependencies

Python 3.5
pytorch 0.3
numpy 1.13.1

RUN

Edit configration file to meet your need.
Run vin.py

References

https://github.com/jaesik817/visual-interaction-networks_tensorflow

This's an implementation of deepmind Visual Interaction Networks paper using pytorch

Related tags

Overview

Visual-Interaction-Networks

Introduction

Architecture

Data

Usage

Main Dependencies

RUN

References

Owner

Mahmoud Gamal Salem

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