Visual dialog agents with pre-trained vision-and-language encoders.

Related tags

Deep Learningvisual-dialogvision-and-language
Overview

Learning Better Visual Dialog Agents with Pretrained Visual-Linguistic Representation

Or READ-UP: Referring Expression Agent Dialog with Unified Pretraining.

This repo includes the training/testing code for our paper Learning Better Visual Dialog Agents with Pretrained Visual-Linguistic Representation that has been accepted by CVPR 2021.

Please cite the following paper if you use the code in this repository:

@inproceedings{tu2021learning,
  title={Learning Better Visual Dialog Agents with Pretrained Visual-Linguistic Representation},
  author={Tu, Tao and Ping, Qing and Thattai, Govindarajan and Tur, Gokhan and Natarajan, Prem},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={5622--5631},
  year={2021}
}

Repository Setup

Environment

The following environment is recommended:

Instance storage: > 800 GB
pytorch 1.4.0
cuda 10.0

Set up virtual environment and install pytorch:

$ conda create -n read_up python=3.6
$ conda activate read_up
$ git clone https://github.com/amazon-research/read-up.git

# [IMPORTANT] pytorch 1.4.0 have no issue for parallel training
$ conda install pytorch==1.4.0 torchvision==0.5.0 cudatoolkit=10.0 -c pytorch

Install dependencies:

# Install general dependencies
$ sudo apt-get install build-essential libcap-dev
$ pip install -r requirement.txt

# Install vqa-maskrcnn-benchmark (for feature extraction only)
$ git clone https://gitlab.com/vedanuj/vqa-maskrcnn-benchmark.git
$ cd vqa-maskrcnn-benchmark
$ python setup.py build develop

Install Apex for distributed training

# Apex is used for both `faster-rcnn feature extraction` & `distributed training`
$ git clone https://github.com/NVIDIA/apex
$ cd apex
$ pip install -v --no-cache-dir --global-option="--cpp_ext" --global-option="--cuda_ext" ./

Dataset

Meta-data

Download the GuessWhat?! dataset:

$ wget https://florian-strub.com/guesswhat.train.jsonl.gz -P data/
$ wget https://florian-strub.com//guesswhat.valid.jsonl.gz -P data/
$ wget https://florian-strub.com//guesswhat.test.jsonl.gz -P data/

Prepare dict.json:

  1. Set up repo as instructed in https://github.com/GuessWhatGame/guesswhat
  2. Generate the dict.json file:
$ python src/guesswhat/preprocess_data/create_dictionary.py -data_dir data -dict_file dict.json -min_occ 3
  1. Copy dict.json file to read-up repo:
$ cd read-up
$ mkdir tf-pretrained-model
$ cp guesswhat/data/dict.json read-up/tf-pretrained-model/

Dataset for Oracle models

1. Dataset for baseline Oracle + Faster-RCNN visual features.

Under vqa-maskrcnn-benchmark/data/, download RCNN model and COCO images:

# download RCNN model
$ wget https://dl.fbaipublicfiles.com/vilbert-multi-task/detectron_model.pth
$ wget https://dl.fbaipublicfiles.com/vilbert-multi-task/detectron_config.yaml

# download COCO data  
$ wget http://images.cocodataset.org/zips/train2014.zip
$ wget http://images.cocodataset.org/zips/val2014.zip
$ wget http://images.cocodataset.org/zips/test2014.zip
$ unzip -j train2014.zip 
$ unzip -j valid2014.zip 
$ unzip -j test2014.zip

Copy the guesswhat.train/valid/test.jsonl to vqa-maskrcnn-benchmark/data/. Unzip the COCO images into a folder image_dir/COCO_2014/images/, and prepare a npy file for feature extraction later.


$ python bin/prepare_extract_gt_features_gw.py \
    --src vqa-maskrcnn-benchmark/data/guesswhat.train.jsonl \
    --img-dir vqa-maskrcnn-benchmark/image_dir/COCO_2014/images/ \
    --out vqa-maskrcnn-benchmark/image_dir/COCO_2014/npy_files/guesswhat.train.npy

Repeat the same process for val and test. The generated file looks like the following:

{
    {
        'file_name': 'name_of_image_file',
        'file_path': '<path_to_image_file_on_your_disk>',
        'bbox': array([
                        [ x1, y1, width1, height1],
                        [ x2, y2, width2, height2],
                        ...
                    ]),
        'num_box': 2
    },
    ....
}

Extract features from the ground-truth bounding boxes generated before:

$ python bin/extract_features_from_gt.py \
    --model_file vqa-maskrcnn-benchmark/data/detectron_model.pth \
    --config_file vqa-maskrcnn-benchmark/data/detectron_config.yaml \
    --imdb_gt_file vqa-maskrcnn-benchmark/image_dir/COCO_2014/npy_files/guesswhat.train.npy \
    --output_folder data/rcnn/from_gt_gw_xyxy_scale/train

Repeat this process for val and test data.

2. Dataset for our Oracle model.

Download the pretrained VilBERT model (both vanilla and 12-in-1 have similar performance in our experiments).

# download vanilla pretrained model
$ cd vilbert-pretrained-model
$ wget https://dl.fbaipublicfiles.com/vilbert-multi-task/pretrained_model.bin

# download 12-in-1 pretrained model
$ wget https://dl.fbaipublicfiles.com/vilbert-multi-task/multi_task_model.bin

Download the features for COCO:

$ wget https://dl.fbaipublicfiles.com/vilbert-multi-task/datasets/coco/features_100/COCO_trainval_resnext152_faster_rcnn_genome.lmdb/data.mdb && mv data.mdb COCO_trainval_resnext152_faster_rcnn_genome.lmdb/
$ wget https://dl.fbaipublicfiles.com/vilbert-multi-task/datasets/coco/features_100/COCO_test_resnext152_faster_rcnn_genome.lmdb/data.mdb && mv data.mdb COCO_test_resnext152_faster_rcnn_genome.lmdb/

Dataset for Q-Gen models

1. Dataset for baseline Q-Gen model [1]

$ wget www.florian-strub.com/github/ft_vgg_img.zip
$ unzip ft_vgg_img.zip -d img/

2. Dataset for VDST Q-Gen model [2]

$ python bin/extract_features.py \
    --model_file vqa-maskrcnn-benchmark/data/detectron_model.pth \
    --config_file vqa-maskrcnn-benchmark/data/detectron_config.yaml \
    --image_dir vqa-maskrcnn-benchmark/image_dir/COCO_2014/images/ \
    --output_folder data/rcnn/from_rcnn/ \
    --batch_size 8

Dataset for Guesser models

1. Dataset for baseline Guesser model[1]

$ cd data/vilbert-multi-task
$ wget https://dl.fbaipublicfiles.com/vilbert-multi-task/datasets.tar.gz
$ tar -I pigz -xvf datasets.tar.gz datasets/guesswhat/

Model Training & Evaluation

Oracle

To train our Oracle model:

$ python -m torch.distributed.launch \
    --nproc_per_node=4 \
    --nnodes=1 \
    --node_rank=0 \
    main.py \
    --command train-oracle-vilbert \
    --config config_files/oracle_vilbert.yaml \
    --n-jobs 8

To evaluate our Oracle model:

$ python main.py \
    --command test-oracle-vilbert \
    --config config_files/oracle_vilbert.yaml \
    --load ckpt/oracle_vilbert-sd0/epoch-3.pth

This repo also implements other Oracle models:

  • Baseline Oracle model [1]
  • Baseline Oracle model + Faster-RCNN visual features (our ablation model)

To train and evaluate this model, run the main.py with corresponding config file and command.

Guesser

To train our Guesser model:

$ python main.py \
    --command train-guesser-vilbert \
    --config config_files/guesser_vilbert.yaml \
    --n-jobs 8

To evaluate our Guesser model:

$ python main.py \
    --command test-guesser-vilbert \
    --config config_files/guesser_vilbert.yaml \
    --n-jobs 8 \
    --load ckpt/guesser_vilbert-sd0/best.pth

This repo also implements other Guesser models:

  • Baseline Guesser model [1]

To train and evaluate this model, run the main.py with corresponding config file and command.

Q-Gen

To train our Q-Gen model:

# Distributed training
$ python -m torch.distributed.launch \
    --nproc_per_node=4 \
    --nnodes=1 \
    --node_rank=0 \
    main.py \
    --command train-qgen-vilbert \
    --config config_files/qgen_vilbert.yaml \
    --n-jobs 8 

# Non-distributed training
$ python main.py \
    --command train-qgen-vilbert \
    --config config_files/qgen_vilbert.yaml \
    --n-jobs 8

To evalaute our Q-Gen model:

$ python main.py \
    --command test-self-play-all-vilbert \
    --config config_files/self_play_all_vilbert.yaml \
    --n-jobs 8

This repo also implements other Q-Gen models:

  • Baseline Q-Gen model [1]
  • VDST Q-Gen model [2]

To train and evaluate these models, run the main.py with corresponding config file and command.

References

[1] Strub, F., De Vries, H., Mary, J., Piot, B., Courvile, A., & Pietquin, O. (2017, August). End-to-end optimization of goal-driven and visually grounded dialogue systems. In Proceedings of the 26th International Joint Conference on Artificial Intelligence (pp. 2765-2771).

[2] Pang, W., & Wang, X. (2020, April). Visual dialogue state tracking for question generation. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 34, No. 07, pp. 11831-11838).

Owner
GitHub Repository
Real-time 3D multi-person detection made easy with OpenPose and the ZED

OpenPose ZED This sample show how to simply use the ZED with OpenPose, the deep learning framework that detects the skeleton from a single 2D image. T

blanktec 5 Nov 06, 2020
(CVPR 2022 - oral) Multi-View Depth Estimation by Fusing Single-View Depth Probability with Multi-View Geometry

Multi-View Depth Estimation by Fusing Single-View Depth Probability with Multi-View Geometry Official implementation of the paper Multi-View Depth Est

Bae, Gwangbin 138 Dec 28, 2022
ParaGen is a PyTorch deep learning framework for parallel sequence generation

ParaGen is a PyTorch deep learning framework for parallel sequence generation. Apart from sequence generation, ParaGen also enhances various NLP tasks, including sequence-level classification, extrac

Bytedance Inc. 169 Dec 22, 2022
SAS: Self-Augmentation Strategy for Language Model Pre-training

SAS: Self-Augmentation Strategy for Language Model Pre-training This repository

Alibaba 5 Nov 02, 2022
Implementation of ViViT: A Video Vision Transformer

ViViT: A Video Vision Transformer Unofficial implementation of ViViT: A Video Vision Transformer. Notes: This is in WIP. Model 2 is implemented, Model

Rishikesh (ऋषिकेश) 297 Jan 06, 2023
"Graph Neural Controlled Differential Equations for Traffic Forecasting", AAAI 2022

Graph Neural Controlled Differential Equations for Traffic Forecasting Setup Python environment for STG-NCDE Install python environment $ conda env cr

Jeongwhan Choi 55 Dec 28, 2022
Torch-based tool for quantizing high-dimensional vectors using additive codebooks

Trainable multi-codebook quantization This repository implements a utility for use with PyTorch, and ideally GPUs, for training an efficient quantizer

Daniel Povey 41 Jan 07, 2023
Custom studies about block sparse attention.

Block Sparse Attention 研究总结 本人近半年来对Block Sparse Attention（块稀疏注意力）的研究总结（持续更新中）。按时间顺序，主要分为如下三部分： PyTorch 自定义 CUDA 算子——以矩阵乘法为例 基于 Triton 的 Block Sparse A

Chen Kai 2 Jan 09, 2022
It is the assignment for COMP 576 in Rice University

COMP-576 It is the assignment for COMP 576 in Rice University There are two programming assignments and one Final Project. Assignment 1: It is a MLP a

Maojie Tang 1 Nov 25, 2021
[ACMMM 2021, Oral] Code release for "Elastic Tactile Simulation Towards Tactile-Visual Perception"

EIP: Elastic Interaction of Particles Code release for "Elastic Tactile Simulation Towards Tactile-Visual Perception", in ACMMM (Oral) 2021. By Yikai

Yikai Wang 37 Dec 20, 2022
sktime companion package for deep learning based on TensorFlow

NOTE: sktime-dl is currently being updated to work correctly with sktime 0.6, and wwill be fully relaunched over the summer. The plan is Refactor and

sktime 573 Jan 05, 2023
Real-time multi-object tracker using YOLO v5 and deep sort

This repository contains a two-stage-tracker. The detections generated by YOLOv5, a family of object detection architectures and models pretrained on the COCO dataset, are passed to a Deep Sort algor

Mike 3.6k Jan 05, 2023
PyTorch implementation of the supervised learning experiments from the paper Model-Agnostic Meta-Learning (MAML)

pytorch-maml This is a PyTorch implementation of the supervised learning experiments from the paper Model-Agnostic Meta-Learning (MAML): https://arxiv

Kate Rakelly 516 Jan 05, 2023
source code the paper Fast and Robust Iterative Closet Point.

Fast-Robust-ICP This repository includes the source code the paper Fast and Robust Iterative Closet Point. Authors: Juyong Zhang, Yuxin Yao, Bailin De

yaoyuxin 320 Dec 28, 2022
A minimalist environment for decision-making in autonomous driving

highway-env A collection of environments for autonomous driving and tactical decision-making tasks An episode of one of the environments available in

Edouard Leurent 1.6k Jan 07, 2023
UniFormer - official implementation of UniFormer

UniFormer This repo is the official implementation of "Uniformer: Unified Transf

SenseTime X-Lab 573 Jan 04, 2023
OpenMMLab Video Perception Toolbox. It supports Video Object Detection (VID), Multiple Object Tracking (MOT), Single Object Tracking (SOT), Video Instance Segmentation (VIS) with a unified framework.

English | 简体中文 Documentation: https://mmtracking.readthedocs.io/ Introduction MMTracking is an open source video perception toolbox based on PyTorch.

OpenMMLab 2.7k Jan 08, 2023
Can we visualize a large scientific data set with a surrogate model? We're building a GAN for the Earth's Mantle Convection data set to see if we can!

EarthGAN - Earth Mantle Surrogate Modeling Can a surrogate model of the Earth’s Mantle Convection data set be built such that it can be readily run in

Tim 0 Dec 09, 2021
Back to the Feature: Learning Robust Camera Localization from Pixels to Pose (CVPR 2021)

Back to the Feature with PixLoc We introduce PixLoc, a neural network for end-to-end learning of camera localization from an image and a 3D model via

Computer Vision and Geometry Lab 610 Jan 05, 2023
Code release for BlockGAN: Learning 3D Object-aware Scene Representations from Unlabelled Images

BlockGAN Code release for BlockGAN: Learning 3D Object-aware Scene Representations from Unlabelled Images BlockGAN: Learning 3D Object-aware Scene Rep

41 May 18, 2022