CLEVRER

This repository holds the codes for the paper

CLEVRER: Collision Events for Video Representation and Reasoning, Kexin Yi*, Chuang Gan*, Yunzhu Li, Pushmeet Kohli, Jiajun Wu, Antonio Torralba, Joshua B. Tenenbaum, ICLR, 2020.

[Arxiv Preprint] [Project Website]

Usage Guide

Prerequisites

The codebase is written in Python. There are a few dependencies to run the code.

Run the command to install dependencies.

pip install -r requirements

Code & Data Preparation

Get the code

Use git to clone this repository

git clone https://github.com/chuangg/CLEVRER.git

The code mainly consists of two parts, including the dynamics predictor and the program executor.

• dynamics predictor: we provide the implementation of the model in folder temporal-reasoning.
• programe executor: the code is provided in folder executor.

Get the data

To help reproduce the reported results, we provide all the required data, including visual masks, parsed programs and dynamic predictions.

Data for the program executor

The parsed programes can be found under the path ./executor/data/parsed_program/.

The dynamic predictions can be found here, including two versions:

• with_edge_supervision: the dynamics predictor is trained with supervisions in edges of the graph neural network.
• without_edge_supervision: the dynamics predictor is trained without supervisions in edges of the graph neural network.

Please extract the archieved file you download using tar -zxvf <file_path> and place them under the path executor/data (e.g., executor/data/propnet_preds/with(without)_edge_supervision).

Data for the dynamics predictor

The results of video frame parser (visual masks) can be found here.

Please download videos from project page, and extract video frames.

Before training/testing the dynamics predictor, please make sure that you have extracted the vidoe frames (named format frame_00000.png) and downloaded the visual masks. Please organize the files with the same structure as (or you can modify the frame path in ./temporal-reasoning/data.py and ./temporal-reasoning/eval.py.):

video_frames/
├─sim_00000/frame_00000.png, frame_00001.png,... 
├─sim_00001/frame_00000.png, frame_00001.png,...
├─sim_00002/...
├─...
├─...
└sim_19999/...

processed_proposals/
├─sim_00000.json
├─sim_00001.json
├─sim_00002.json
├─...
├─...
└ sim_19999.json

Note: the index of videos/frames is starting from 0 (i,e,. 00000).

Testing the NS-DR model

Go to the executor folder:

cd ./executor

Evaluation on validation set

Before starting, please check and modify the path of the dynamic predictions in line 22 and 24 in executor/run_oe.py and executor/run_mc.py. Make sure the path is valid.

For open-ended questions:

python run_oe.py --n_progs 1000

For multiple-choice questions:

python run_mc.py --n_progs 1000

Evaluation on test set

Generate predicted answers on test set by running

python get_results.py

An answer prediction file for test set (nsdr_pred.json) will be generated, and you can get the evaluation metric results by uploading this file to the evaluation server here.

Training/Testing the dynamics predictor

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We provide the code for the dynamics predictor to generate dynamics predictions as input of the programe executor.

Training

The training scripts can be found in temporal-reasoning/scripts/, and the main arguments in the scripts including:

• gen_valid_idx: set to 1 at the first training.
• data_dir: the directory of extracted frames.
• label_dir: the directory of the downloaded visual masks.
• resume_epoch/iter: the checkpoint information (set to 0 if no checkpoints.)

The name of scripts train*.sh is self-explained. More details about arguments can be found in ./temporal-reasoning/train.py. Please check the argument values before starting training.

Start training

cd ./temporal-reasoning 
bash scripts/train.sh   

The models and log will be save in a folder with a name like files_CLEVR_pn_pstep2 in temporal-reasoning folder.

Testing

The tesing scripts can also be found in temporal-reasoning/scripts. One training script corresponds to one testing script. The testing script contains serveral main arguments:

• des_dir: the directory for saving the output dynamic predictions.
• st_idx: the starting index for a data split (10000 for validation set; 15000 for testing set).
• ed_idx: the ending index for a data split (15000 for validation set; 20000 for testing set).
• epoch/iter: the checkpoint information.

More details about arguments can be found in ./temporal-reasoning/eval.py. Please check the argument values before starting testing.

Start testing

cd ./temporal-reasoning
bash scripts/eval.sh

The dynamic predictions will be saved under the directory as you set in --des_dir in eval.sh.

You can then feed these dynamic predictions into the program executor and enjoy.

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Citation

Please cite the following paper if you feel this repository useful.

@inproceedings{CLEVRER2020ICLR,
  author    = {Kexin Yi and
               Chuang Gan and
               Yunzhu Li and
               Pushmeet Kohli and
               Jiajun Wu and
               Antonio Torralba and
               Joshua B. Tenenbaum},
  title     = {{CLEVRER:} Collision Events for Video Representation and Reasoning},
  booktitle = {ICLR},
  year      = {2020}
}