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
Efficient and Accurate Arbitrary-Shaped Text Detection with Pixel Aggregation Network

Efficient and Accurate Arbitrary-Shaped Text Detection with Pixel Aggregation Network Paddle-PANet 目录 结果对比 论文介绍 快速安装 结果对比 CTW1500 Method Backbone Fine

7 Aug 08, 2022
Minimalistic PyTorch training loop

Backbone for PyTorch training loop Will try to keep it minimalistic. pip install back from back import Bone Features Progress bar Checkpoints saving/l

Kashin 4 Jan 16, 2020
Rax is a Learning-to-Rank library written in JAX

🦖 Rax: Composable Learning to Rank using JAX Rax is a Learning-to-Rank library written in JAX. Rax provides off-the-shelf implementations of ranking

Google 247 Dec 27, 2022
Image restoration with neural networks but without learning.

Warning! The optimization may not converge on some GPUs. We've personally experienced issues on Tesla V100 and P40 GPUs. When running the code, make s

Dmitry Ulyanov 7.4k Jan 01, 2023
Inhomogeneous Social Recommendation with Hypergraph Convolutional Networks

Inhomogeneous Social Recommendation with Hypergraph Convolutional Networks This is our Pytorch implementation for the paper: Zirui Zhu, Chen Gao, Xu C

Zirui Zhu 3 Dec 30, 2022
Collection of generative models in Pytorch version.

pytorch-generative-model-collections Original : [Tensorflow version] Pytorch implementation of various GANs. This repository was re-implemented with r

Hyeonwoo Kang 2.4k Dec 31, 2022
PyTorch code for Composing Partial Differential Equations with Physics-Aware Neural Networks

FInite volume Neural Network (FINN) This repository contains the PyTorch code for models, training, and testing, and Python code for data generation t

Cognitive Modeling 20 Dec 18, 2022
This is the code repository implementing the paper "TreePartNet: Neural Decomposition of Point Clouds for 3D Tree Reconstruction".

TreePartNet This is the code repository implementing the paper "TreePartNet: Neural Decomposition of Point Clouds for 3D Tree Reconstruction". Depende

刘彦超 34 Nov 30, 2022
ICCV2021, Tokens-to-Token ViT: Training Vision Transformers from Scratch on ImageNet

Tokens-to-Token ViT: Training Vision Transformers from Scratch on ImageNet, ICCV 2021 Update: 2021/03/11: update our new results. Now our T2T-ViT-14 w

YITUTech 1k Dec 31, 2022
Adversarial Graph Augmentation to Improve Graph Contrastive Learning

ADGCL : Adversarial Graph Augmentation to Improve Graph Contrastive Learning Introduction This repo contains the Pytorch [1] implementation of Adversa

susheel suresh 62 Nov 19, 2022
Train a state-of-the-art yolov3 object detector from scratch!

TrainYourOwnYOLO: Building a Custom Object Detector from Scratch This repo let's you train a custom image detector using the state-of-the-art YOLOv3 c

AntonMu 616 Jan 08, 2023
Open source implementation of "A Self-Supervised Descriptor for Image Copy Detection" (SSCD).

A Self-Supervised Descriptor for Image Copy Detection (SSCD) This is the open-source codebase for "A Self-Supervised Descriptor for Image Copy Detecti

Meta Research 68 Jan 04, 2023
This repository implements Douzero's interface to IGCA.

douzero-interface-for-ICGA This repository implements Douzero's interface to ICGA. ./douzero: This directory stores Doudizhu AI projects. ./interface:

zhanggenjin 4 Aug 07, 2022
Proximal Backpropagation - a neural network training algorithm that takes implicit instead of explicit gradient steps

Proximal Backpropagation Proximal Backpropagation (ProxProp) is a neural network training algorithm that takes implicit instead of explicit gradient s

Thomas Frerix 40 Dec 17, 2022
source code of “Visual Saliency Transformer” (ICCV2021)

Visual Saliency Transformer (VST) source code for our ICCV 2021 paper “Visual Saliency Transformer” by Nian Liu, Ni Zhang, Kaiyuan Wan, Junwei Han, an

89 Dec 21, 2022
Official implementation of the paper Do pedestrians pay attention? Eye contact detection for autonomous driving

Do pedestrians pay attention? Eye contact detection for autonomous driving Official implementation of the paper Do pedestrians pay attention? Eye cont

VITA lab at EPFL 26 Nov 02, 2022
HPRNet: Hierarchical Point Regression for Whole-Body Human Pose Estimation

HPRNet: Hierarchical Point Regression for Whole-Body Human Pose Estimation Official PyTroch implementation of HPRNet. HPRNet: Hierarchical Point Regre

Nermin Samet 53 Dec 04, 2022
This is an official implementation for "Swin Transformer: Hierarchical Vision Transformer using Shifted Windows" on Semantic Segmentation.

Swin Transformer for Semantic Segmentation of satellite images This repo contains the supported code and configuration files to reproduce semantic seg

23 Oct 10, 2022
A paper using optimal transport to solve the graph matching problem.

GOAT A paper using optimal transport to solve the graph matching problem. https://arxiv.org/abs/2111.05366 Repo structure .github: Files specifying ho

neurodata 8 Jan 04, 2023
Train/evaluate a Keras model, get metrics streamed to a dashboard in your browser.

Hera Train/evaluate a Keras model, get metrics streamed to a dashboard in your browser. Setting up Step 1. Plant the spy Install the package pip

Keplr 495 Dec 10, 2022