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
Code for "Learning Structural Edits via Incremental Tree Transformations" (ICLR'21)

Learning Structural Edits via Incremental Tree Transformations Code for "Learning Structural Edits via Incremental Tree Transformations" (ICLR'21) 1.

NeuLab 40 Dec 23, 2022
Raster Vision is an open source Python framework for building computer vision models on satellite, aerial, and other large imagery sets

Raster Vision is an open source Python framework for building computer vision models on satellite, aerial, and other large imagery sets (including obl

Azavea 1.7k Dec 22, 2022
[Preprint] "Bag of Tricks for Training Deeper Graph Neural Networks A Comprehensive Benchmark Study" by Tianlong Chen*, Kaixiong Zhou*, Keyu Duan, Wenqing Zheng, Peihao Wang, Xia Hu, Zhangyang Wang

Bag of Tricks for Training Deeper Graph Neural Networks: A Comprehensive Benchmark Study Codes for [Preprint] Bag of Tricks for Training Deeper Graph

VITA 101 Dec 29, 2022
Official Implementation of Few-shot Visual Relationship Co-localization

VRC Official implementation of the Few-shot Visual Relationship Co-localization (ICCV 2021) paper project page | paper Requirements Use python = 3.8.

22 Oct 13, 2022
Implementation of Artificial Neural Network Algorithm

Artificial Neural Network This repository contain implementation of Artificial Neural Network Algorithm in several programming languanges and framewor

Resha Dwika Hefni Al-Fahsi 1 Sep 14, 2022
Code of TVT: Transferable Vision Transformer for Unsupervised Domain Adaptation

TVT Code of TVT: Transferable Vision Transformer for Unsupervised Domain Adaptation Datasets: Digit: MNIST, SVHN, USPS Object: Office, Office-Home, Vi

37 Dec 15, 2022
DIRL: Domain-Invariant Representation Learning

DIRL: Domain-Invariant Representation Learning Domain-Invariant Representation Learning (DIRL) is a novel algorithm that semantically aligns both the

Ajay Tanwani 30 Nov 07, 2022
[TNNLS 2021] The official code for the paper "Learning Deep Context-Sensitive Decomposition for Low-Light Image Enhancement"

CSDNet-CSDGAN this is the code for the paper "Learning Deep Context-Sensitive Decomposition for Low-Light Image Enhancement" Environment Preparing pyt

Jiaao Zhang 17 Nov 05, 2022
Simplified interface for TensorFlow (mimicking Scikit Learn) for Deep Learning

SkFlow has been moved to Tensorflow. SkFlow has been moved to http://github.com/tensorflow/tensorflow into contrib folder specifically located here. T

3.2k Dec 29, 2022
Semantic Image Synthesis with SPADE

Semantic Image Synthesis with SPADE New implementation available at imaginaire repository We have a reimplementation of the SPADE method that is more

NVIDIA Research Projects 7.3k Jan 07, 2023
Code for "Offline Meta-Reinforcement Learning with Advantage Weighting" [ICML 2021]

Offline Meta-Reinforcement Learning with Advantage Weighting (MACAW) MACAW code used for the experiments in the ICML 2021 paper. Installing the enviro

Eric Mitchell 28 Jan 01, 2023
Simple and Effective Few-Shot Named Entity Recognition with Structured Nearest Neighbor Learning

structshot Code and data for paper "Simple and Effective Few-Shot Named Entity Recognition with Structured Nearest Neighbor Learning", Yi Yang and Arz

ASAPP Research 47 Dec 27, 2022
PyTorch implementation of ARM-Net: Adaptive Relation Modeling Network for Structured Data.

A ready-to-use framework of latest models for structured (tabular) data learning with PyTorch. Applications include recommendation, CRT prediction, healthcare analytics, and etc.

48 Nov 30, 2022
Build a small, 3 domain internet using Github pages and Wikipedia and construct a crawler to crawl, render, and index.

TechSEO Crawler Build a small, 3 domain internet using Github pages and Wikipedia and construct a crawler to crawl, render, and index. Play with the r

JR Oakes 57 Nov 24, 2022
An implementation of the 1. Parallel, 2. Streaming, 3. Randomized SVD using MPI4Py

PYPARSVD This implementation allows for a singular value decomposition which is: Distributed using MPI4Py Streaming - data can be shown in batches to

Romit Maulik 44 Dec 31, 2022
TensorFlow code for the neural network presented in the paper: "Structural Language Models of Code" (ICML'2020)

SLM: Structural Language Models of Code This is an official implementation of the model described in: "Structural Language Models of Code" [PDF] To ap

73 Nov 06, 2022
Code for CVPR2019 paper《Unequal Training for Deep Face Recognition with Long Tailed Noisy Data》

Unequal-Training-for-Deep-Face-Recognition-with-Long-Tailed-Noisy-Data. This is the code of CVPR 2019 paper《Unequal Training for Deep Face Recognition

Zhong Yaoyao 68 Jan 07, 2023
End-to-End Referring Video Object Segmentation with Multimodal Transformers

End-to-End Referring Video Object Segmentation with Multimodal Transformers This repo contains the official implementation of the paper: End-to-End Re

608 Dec 30, 2022
This repository contains the implementation of the HealthGen model, a generative model to synthesize realistic EHR time series data with missingness

HealthGen: Conditional EHR Time Series Generation This repository contains the implementation of the HealthGen model, a generative model to synthesize

0 Jan 20, 2022
Container : Context Aggregation Network

Container : Context Aggregation Network If you use this code for a paper please cite: @article{gao2021container, title={Container: Context Aggregati

AI2 47 Dec 16, 2022