July 2016 Authors: Michael Mathieu, Camille Couprie Update: due to large files that could not be stored on github, the trained models and dataset may be found at: http://perso.esiee.fr/~coupriec/MathieuICLR16TestCode.zip This repository contains: - Test code for the ICLR 2016 paper: [1] Michael Mathieu, Camille Couprie, Yann LeCun: "Deep multi-scale video prediction beyond mean square error". http://arxiv.org/abs/1511.05440 http://cs.nyu.edu/~mathieu/iclr2016.html - Two trained models (using adversarial+l2norm training or adversarial+l1norm+gdl training). - A subset of the UCF101 test dataset [2] with optical flow results to perform an evaluation in moving area as described in [1]. - A training script for the model. Because the Sports1m dataset is hard to get, we cannot provide an easy script to train on it. Instead, we propose a script to train on UCF101, which is significantly smaller. Main files: - For testing: test-frame-prediction-on-ucf-rec_gdl.lua Script to test 2 trained models to predict future frames in video from 4 previous ones on a subset of the UCF101 test dataset. - For training: - For training: train_iclr_model.lua Script to train a model from scratch on the UCF101 dataset. If you want to train on the Sports1m dataset, you will need to download it and write a datareader, similar to datasources/ucf101.lua . Usage: 1- Install torch and the packages (standard packages + nngraph, cudnn.torch, gfx.js) For testing: 2- Uncompress the provided archives. 3- Run the main script : th test-frame-prediction-on-ucf-rec_gdl.lua It generates results (2 predicted images + animated gifs) in a directory named 'AdvGDL'. It also display the average PSNR and SSIM of the 2 first predicted frames following the evaluation presented in [1]. For training: 2- Get the UCF101 dataset (requires unrar, modify the script if you have another .rar extractor): cd datasources python get_datasource.py 3- Get thffpmeg from https://github.com/MichaelMathieu/THFFmpeg 4- Run the training script: th train_iclr_model.lua 5- For visualizing the intermediate results, start the gfx.js server th -lgfx.start And go to http://localhost:8000 in your internet browser. [2]:Khurram Soomro, Amir Roshan Zamir and Mubarak Shah, UCF101: A Dataset of 101 Human Action Classes From Videos in The Wild., CRCV-TR-12-01, November, 2012.