This repo contains the pytorch implementation for Dynamic Concept Learner (accepted by ICLR 2021).

Overview

DCL-PyTorch

Pytorch implementation for the Dynamic Concept Learner (DCL). More details can be found at the project page.

Framework

Grounding Physical Concepts of Objects and Events Through Dynamic Visual Reasoning
Zhenfang Chen, Jiayuan Mao, Jiajun Wu, Kwan-Yee K. Wong, Joshua B. Tenenbaum, and Chuang Gan

Prerequisites

  • Python 3
  • PyTorch 1.0 or higher, with NVIDIA CUDA Support
  • Other required python packages specified by requirements.txt. See the Installation.

Installation

Install Jacinle: Clone the package, and add the bin path to your global PATH environment variable:

git clone https://github.com/vacancy/Jacinle --recursive
export PATH=<path_to_jacinle>/bin:$PATH

Clone this repository:

git clone https://github.com/zfchenUnique/DCL-Release.git --recursive

Create a conda environment for NS-CL, and install the requirements. This includes the required python packages from both Jacinle NS-CL. Most of the required packages have been included in the built-in anaconda package:

Dataset preparation

  • Download videos, video annotation, questions and answers, and object proposals accordingly from the official website
  • Transform videos into ".png" frames with ffmpeg.
  • Organize the data as shown below.
    clevrer
    ├── annotation_00000-01000
    │   ├── annotation_00000.json
    │   ├── annotation_00001.json
    │   └── ...
    ├── ...
    ├── image_00000-01000
    │   │   ├── 1.png
    │   │   ├── 2.png
    │   │   └── ...
    │   └── ...
    ├── ...
    ├── questions
    │   ├── train.json
    │   ├── validation.json
    │   └── test.json
    ├── proposals
    │   ├── proposal_00000.json
    │   ├── proposal_00001.json
    │   └── ...
    

Fast Evaluation

    git clone https://github.com/zfchenUnique/clevrer_dynamic_propnet.git
    cd clevrer_dynamic_propnet
    sh ./scripts/eval_fast_release_v2.sh 0
   sh scripts/script_test_prp_clevrer_qa.sh 0

Step-by-step Training

  • Step 1: download the proposals from the region proposal network and extract object trajectories for train and val set by
   sh scripts/script_gen_tubes.sh
  • Step 2: train a concept learner with descriptive and explanatory questions for static concepts (i.e. color, shape and material)
   sh scripts/script_train_dcl_stage1.sh 0
  • Step 3: extract static attributes & refine object trajectories extract static attributes
   sh scripts/script_extract_attribute.sh

refine object trajectories

   sh scripts/script_gen_tubes_refine.sh
  • Step 4: extract predictive and counterfactual scenes by
    cd clevrer_dynamic_propnet
    sh ./scripts/train_tube_box_only.sh # train
    sh ./scripts/train_tube.sh # train
    sh ./scripts/eval_fast_release_v2.sh 0 # val
  • Step 5: train DCL with all questions and the refined trajectories
   sh scripts/script_train_dcl_stage2.sh 0

Generalization to CLEVRER-Grounding

    sh ./scripts/script_grounding.sh  0
    jac-crun 0 scripts/script_evaluate_grounding.py

Generalization to CLEVRER-Retrieval

    sh ./scripts/script_retrieval.sh  0
    jac-crun 0 scripts/script_evaluate_retrieval.py

Extension to Tower Blocks

    sh ./scripts/script_train_blocks.sh 0
  • Step 3: download the pretrain model from google drive and evaluate on Tower block QA
    sh ./scripts/script_eval_blocks.sh 0

Others

Citation

If you find this repo useful in your research, please consider citing:

@inproceedings{zfchen2021iclr,
    title={Grounding Physical Concepts of Objects and Events Through Dynamic Visual Reasoning},
    author={Chen, Zhenfang and Mao, Jiayuan and Wu, Jiajun and Wong, Kwan-Yee K and Tenenbaum, Joshua B. and Gan, Chuang},
    booktitle={International Conference on Learning Representations},
    year={2021}
    }
Owner
Zhenfang Chen
Keep it simple.
Zhenfang Chen
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