Vision-Language Pre-training for Image Captioning and Question Answering

Related tags

Deep LearningVLP
Overview

VLP

This repo hosts the source code for our AAAI2020 work Vision-Language Pre-training (VLP). We have released the pre-trained model on Conceptual Captions dataset and fine-tuned models on COCO Captions and Flickr30k for image captioning and VQA 2.0 for VQA.

Installation

Conda Environment (Option I, Recommended)

  1. Recursively ssh clone the repo to include coco and pythia submodules.
git clone --recursive [email protected]:LuoweiZhou/VLP.git

or clone with https:

git clone --recursive https://github.com/LuoweiZhou/VLP.git

  1. Install CUDA (e.g., 10.0), CUDNN (e.g., v7.5), and Miniconda (either Miniconda2 or 3, version 4.6+).

  2. Run the following commands to set up conda env and install Python packages:

MINICONDA_ROOT=[to your Miniconda root directory] # e.g., /home/[usrname]/miniconda3
cd VLP
conda env create -f misc/vlp.yml --prefix $MINICONDA_ROOT/envs/vlp
conda activate vlp
  1. Finally, cd to the repo root directory and install other dependencies by running:
./setup.sh

To support language evaluation (SPICE), run

cd coco-caption
./get_stanford_models.sh

Docker Image (Option II)

First, install or upgrade to the latest docker (e.g., set <VERSION_STRING> to 5:19.03.2~3-0~ubuntu-xenial). Then pull our docker image:

docker pull luzhou/vlp

Before running the container, you need to declare the environment variable to your data root ($DATA_ROOT, see data prep) and it will be attached as a volume to our container. Finally, install nvidia-container-toolkit and run the docker image in a fresh container:

docker run --gpus all --name vlp_container -it \
     -v $DATA_ROOT:/mnt/dat \
     --shm-size 8G -p 8888:8888 vlp /bin/bash

You can know more about docker commands and usages here.

(Optional) To build the image on your own,

docker build -t vlp .

Data Preparation

Download links for dataset annotations and features: COCO Captions+VQA 2.0 (Part I(95GB), Part II(79GB), download both and run cat COCO0* > COCO.tar.gz), Flickr30k Captions(27GB). If you prefer to download with wget, we attach the commands here. Then, uncompress the downloaded files and place under your data root (denoted as DATA_ROOT).

To prepare for the pre-training, first download and uncompress our pre-processed Conceptual Captions (CC) data(6GB) and place under your data root. Then, download and uncompress the region features from Google Drive (feat(509GB), cls(468GB)) under the CC/region_feat_gvd_wo_bgd/feat_cls_1000_float16 dir. To evaluate CC on caption generation, download the reference file and place it under coco-caption/annotations.

Besides, download and uncompress the detectron fc7 weight files under the code root directory (denoted as CODE_ROOT): GVD Detectron fc7.

(Optional, only for VQA) Download the VQA 2.0 annotation (based on Pythia):

cd $CODE_ROOT/pythia
mkdir -p data && cd data
wget http://dl.fbaipublicfiles.com/pythia/data/vocab.tar.gz
tar xf vocab.tar.gz && rm vocab.tar.gz

wget https://s3.amazonaws.com/cvmlp/vqa/mscoco/vqa/v2_Annotations_Val_mscoco.zip
unzip v2_Annotations_Val_mscoco.zip && rm v2_Annotations_Val_mscoco.zip

mkdir -p imdb && cd imdb
wget https://dl.fbaipublicfiles.com/pythia/data/imdb/vqa.tar.gz
tar xf vqa.tar.gz && rm vqa.tar.gz

(Optional, only for pre-training) Download the UniLM checkpoints and uncompress under your checkpoint root (denoted as CHECKPOINT_ROOT).

Experiment Overview

Most of the experiments in this work are performed on 8x V100 GPUs with distributed data parallel (i.e., set --world_size to 8, --local_rank and --global_rank from 0 to 7 with 8 separate scripts), unless specified otherwise. See below for detailed configurations (also in the Appendix of the paper).

Dataset Batch Size Learning Rate # of Epochs GPUs Time per Epoch
CC 64(x8) 1e-4(x8) 30 8x V100 5hr
COCO 64(x8) 3e-5(x8) 30 8x V100 12min
VQA 2.0 64(x2) 2e-5(x2) 20 2x V100 32min
Flickr30k 64(x8) 3e-5(x8) 30 8x V100 3min
COCO (w/o pre-training) 64(x8) 3e-4(x8) 30 8x V100 12min
COCO (SCST training) 16(x4) 1e-6(x4) 30 4x Titan Xp 3hr

The (x2), (x4), (x8) in the batch size and learning rate results from distributed data parallel. Gradients are accumulated/added across GPUs.

Note that some modules need to be imported manually:

export PYTHONPATH=$CODE_ROOT/pythia:$CODE_ROOT/pythia/pythia/legacy:$CODE_ROOT:$PYTHONPATH

Pre-training

An example code on single-GPU training:

python vlp/run_img2txt_dist.py --output_dir $CHECKPOINT_ROOT/${checkpoint_cc} \
    --model_recover_path $CHECKPOINT_ROOT/bert_save/base_model_pretrained/model_153999_cpu.bin \
    --do_train --learning_rate ${lr} --new_segment_ids --always_truncate_tail --amp \
    --src_file $DATA_ROOT/CC/annotations/dataset_cc.json \
    --dataset cc --split train --file_valid_jpgs $DATA_ROOT/CC/annotations/cc_valid_jpgs.json \
    --local_rank -1 --global_rank -1 --world_size 1 --enable_butd \
    --s2s_prob ${w_s} --bi_prob ${w_b} --image_root $DATA_ROOT/CC/region_feat_gvd_wo_bgd \
    --region_bbox_file bbox/cc_detection_vg_thresh0.2_feat_gvd_checkpoint_trainval.h5 \
    --region_det_file_prefix feat_cls_1000_float16/cc_detection_vg_100dets_gvd_checkpoint_trainval

where lr=1e-4, w_s=0.75, w_b=0.25, and checkpoint_cc is the id of the checkpoint. The pre-trained models are available here.

Fine-tuning

The fine-tuning checkpoints are available at: COCO (CE optim), COCO (CIDEr optim), VQA 2.0 (train on train set only), Flickr30k.

COCO Captions

An example code on single-GPU training:

python vlp/run_img2txt_dist.py --output_dir $CHECKPOINT_ROOT/${checkpoint_coco_ce} \
    --model_recover_path $CHECKPOINT_ROOT/${checkpoint_cc}/model.30.bin \
    --do_train --new_segment_ids --always_truncate_tail --amp \
    --src_file $DATA_ROOT/COCO/annotations/dataset_coco.json \
    --file_valid_jpgs $DATA_ROOT/COCO/annotations/coco_valid_jpgs.json \
    --image_root $DATA_ROOT/COCO/region_feat_gvd_wo_bgd --enable_butd --s2s_prob 1 --bi_prob 0

(Optional) To enable Self-Critical Sequence Training (SCST), set --model_recover_path $CHECKPOINT_ROOT/${checkpoint_coco_ce}/model.28.bin, --max_pred 0, --mask_prob 0, --scst, --learning_rate 1e-6 (note that SCST requires a much smaller lr than the default 3e-5), and --output_dir accordingly. The training takes 30 epochs to converge with each epoch takes roughly 3hr.

An example code on 2-GPU training with distributed data parallel:

python vlp/run_img2txt_dist.py --output_dir $CHECKPOINT_ROOT/${checkpoint_coco_ce} \
    --model_recover_path $CHECKPOINT_ROOT/${checkpoint_cc}/model.30.bin \
    --do_train --new_segment_ids --always_truncate_tail --amp \
    --src_file $DATA_ROOT/COCO/annotations/dataset_coco.json \
    --file_valid_jpgs $DATA_ROOT/COCO/annotations/coco_valid_jpgs.json \
    --image_root $DATA_ROOT/COCO/region_feat_gvd_wo_bgd --enable_butd --s2s_prob 1 --bi_prob 0 \
    --local_rank 0 --global_rank 0 --world_size 2 &
python vlp/run_img2txt_dist.py --output_dir $CHECKPOINT_ROOT/${checkpoint_coco_ce} \
    --model_recover_path $CHECKPOINT_ROOT/${checkpoint_cc}/model.30.bin \
    --do_train --new_segment_ids --always_truncate_tail --amp \
    --src_file $DATA_ROOT/COCO/annotations/dataset_coco.json \
    --file_valid_jpgs $DATA_ROOT/COCO/annotations/coco_valid_jpgs.json \
    --image_root $DATA_ROOT/COCO/region_feat_gvd_wo_bgd --enable_butd --s2s_prob 1 --bi_prob 0 \
    --local_rank 1 --global_rank 1 --world_size 2

VQA 2.0

An example code on single-GPU training:

python vlp/run_img2txt_dist.py --output_dir $CHECKPOINT_ROOT/${checkpoint_vqa2} \
    --model_recover_path $CHECKPOINT_ROOT/${checkpoint_cc}/model.30.bin \
    --do_train --learning_rate 2e-5 --new_segment_ids --always_truncate_tail --amp \
    --num_train_epochs 20 --enable_butd --s2s_prob 0 --bi_prob 1 \
    --image_root $DATA_ROOT/COCO/region_feat_gvd_wo_bgd
    --tasks vqa2 --src_file $CODE_ROOT/pythia/data/imdb/vqa/imdb_train2014.npy \
    --file_valid_jpgs $DATA_ROOT/COCO/annotations/coco_valid_jpgs.json \
    --mask_prob 0 --max_pred 1

To get the models for leaderboard, we perform the training on both train set and val set (set src_file to imdb_train2014 and imdb_val2014).

Flickr30k Captions

python vlp/run_img2txt_dist.py --output_dir $CHECKPOINT_ROOT/${checkpoint_flickr30k} \
    --model_recover_path $CHECKPOINT_ROOT/${checkpoint_cc}/model.30.bin \
    --do_train --new_segment_ids --always_truncate_tail --amp \
    --image_root $DATA_ROOT/flickr30k/region_feat_gvd_wo_bgd --enable_butd --s2s_prob 1 --bi_prob 0 \
    --dataset flickr30k --region_bbox_file $DATA_ROOT/flickr30k/region_feat_gvd_wo_bgd/flickr30k_detection_vg_thresh0.2_feat_gvd_checkpoint_trainvaltest.h5 \
    --src_file $DATA_ROOT/flickr30k/annotations/dataset_flickr30k.json \
    --file_valid_jpgs $DATA_ROOT/flickr30k/annotations/flickr30k_valid_jpgs.json

Inference and Testing

Here, we list the expected result outcomes from our Unified VLP checkpoints. For image captioning, on Karpathy's test split:

Dataset Method [email protected] METEOR CIDEr SPICE
COCO Unified VLP 36.5 28.4 116.9 21.2
Unified VLP + SCST 39.5 29.3 129.3 23.2
Flickr30k Unified VLP 30.1 23.0 67.4 17.0

For VQA:

Dataset Trained on Eval Split Overall Yes/No Number Other
VQA 2.0 train only Dev 67.4 85.4 50.1 58.3
train+val Test-Dev 70.5 87.2 52.1 60.3
train+val Test-Standard 70.7 87.4 52.1 60.5

Note that results on Test-Dev and Test-Standard are from VQA 2.0 evaluation server. train+val indicates models are trained on both training set and validation set following the practice from early works.

Note: All the evaluation scripts support data parallel. But since we do not use standard PyTorch DataLoader, the data loading speed might be the bottleneck (imagine num_workers is always 0). We recommend to perform single-GPU inference (e.g., CUDA_VISIBLE_DEVICES=0).

COCO Captions

python vlp/decode_img2txt.py \
    --model_recover_path $CHECKPOINT_ROOT/${checkpoint_coco_ce}/model.${epoch}.bin \
    --new_segment_ids --batch_size 100 --beam_size ${beam} --enable_butd \
    --image_root $DATA_ROOT/COCO/region_feat_gvd_wo_bgd/ --split ${split} \
    --src_file $DATA_ROOT/COCO/annotations/dataset_coco.json \
    --file_valid_jpgs $DATA_ROOT/COCO/annotations/coco_valid_jpgs.json

where checkpoint_coco_ce indicates checkpoint name, beam=1 for split=val set and 5 for split=test set, and epoch indicates the checkpoint at which epoch.

VQA 2.0

python vlp/eval_vqa2.py \
    --model_recover_path $CHECKPOINT_ROOT/${checkpoint_vqa2}/model.${epoch}.bin \
    --new_segment_ids --enable_butd --image_root $DATA_ROOT/COCO/region_feat_gvd_wo_bgd/ \
    --src_file $CODE_ROOT/pythia/data/imdb/vqa/imdb_${split}.npy --batch_size 50 \
    --file_valid_jpgs $DATA_ROOT/COCO/annotations/coco_valid_jpgs.json --split ${split}

where split could be val2014 or test2015.

Flickr30k Captions

python vlp/decode_img2txt.py \
    --model_recover_path $CHECKPOINT_ROOT/${checkpoint_flickr30k}/model.${epoch}.bin \
    --new_segment_ids --batch_size 100 --beam_size ${beam} --enable_butd \
    --image_root $DATA_ROOT/flickr30k/region_feat_gvd_wo_bgd/ --split ${split} \
    --dataset flickr30k --region_bbox_file $DATA_ROOT/flickr30k/region_feat_gvd_wo_bgd/flickr30k_detection_vg_thresh0.2_feat_gvd_checkpoint_trainvaltest.h5 \
    --src_file $DATA_ROOT/flickr30k/annotations/dataset_flickr30k.json \
    --file_valid_jpgs $DATA_ROOT/flickr30k/annotations/flickr30k_valid_jpgs.json

where beam=1 for split=val set and 5 for split=test set, and epoch indicates the checkpoint at which epoch.

Testing

For all the datasets, checkpoints (by epochs) with the best validation accuracy (CIDEr in captioning and overall accuracy in VQA) are evaluated on the test set (Test-Dev and Test-Standard for VQA 2.0).

Misc

The Detectron-based feature extraction code is available under this repo. You need to download this config file and checkpoint file.

List of download commands (only for OneDrive):

wget -O caption_cc_val.json "https://onedrive.live.com/download?cid=E5364FD183A1F5BB&resid=E5364FD183A1F5BB%212017&authkey=AHy5eiJM75RwPxg"

# data
wget -O COCO00 "https://onedrive.live.com/download?cid=E5364FD183A1F5BB&resid=E5364FD183A1F5BB%212019&authkey=ACn4bwZ0nmZ0nik"
wget -O COCO01 "https://onedrive.live.com/download?cid=E5364FD183A1F5BB&resid=E5364FD183A1F5BB%212018&authkey=AHoTGG-7-6kwoAY"
wget -O flickr30k.tar.gz "https://onedrive.live.com/download?cid=E5364FD183A1F5BB&resid=E5364FD183A1F5BB%212015&authkey=AFZ2iehPM8HREeA"
wget -O CC.tar.gz "https://onedrive.live.com/download?cid=E5364FD183A1F5BB&resid=E5364FD183A1F5BB%213781&authkey=ANA--esfJnWIKIE"

# UniLM checkpoint
wget -O bert_save.tar.gz "https://onedrive.live.com/download?cid=E5364FD183A1F5BB&resid=E5364FD183A1F5BB%212016&authkey=AB5-lxzCkgpfLhg"

# pre-training checkpoints
wget -O cc_g8_lr1e-4_batch512_s0.75_b0.25.tar.gz "https://onedrive.live.com/download?cid=E5364FD183A1F5BB&resid=E5364FD183A1F5BB%212026&authkey=AH98pIVaNS4apSI"

# fine-tuning checkpoints
wget -O coco_g8_lr3e-5_batch512_ft_from_s0.75_b0.25.tar.gz "https://onedrive.live.com/download?cid=E5364FD183A1F5BB&resid=E5364FD183A1F5BB%212028&authkey=AEjQxFF1FcBK-Aw"
wget -O coco_g4_lr1e-6_batch64_scst.tar.gz "https://onedrive.live.com/download?cid=E5364FD183A1F5BB&resid=E5364FD183A1F5BB%212027&authkey=ACM1UXlFxgfWyt0"
wget -O vqa2_g2_lr2e-5_batch512_ft_from_s0.75_b0.25.tar.gz "https://onedrive.live.com/download?cid=E5364FD183A1F5BB&resid=E5364FD183A1F5BB%212029&authkey=APjfGJd1-nzDO7s"
wget -O flickr30k_g8_lr3e-5_batch512_ft_from_s0.75_b0.25.tar.gz "https://onedrive.live.com/download?cid=E5364FD183A1F5BB&resid=E5364FD183A1F5BB%212030&authkey=AGmfQ0fXcYCQun0"

# Detectron config/model
wget -O e2e_faster_rcnn_X-101-64x4d-FPN_2x-vlp.yaml "https://onedrive.live.com/download?cid=E5364FD183A1F5BB&resid=E5364FD183A1F5BB%212013&authkey=AHIvnE1FcggwiLU"
wget -O e2e_faster_rcnn_X-101-64x4d-FPN_2x-vlp.pkl "https://onedrive.live.com/download?cid=E5364FD183A1F5BB&resid=E5364FD183A1F5BB%212014&authkey=AAHgqN3Y-LXcBvU"

Reference

Please acknowledge the following paper if you use the code:

@article{zhou2019vlp,
  title={Unified Vision-Language Pre-Training for Image Captioning and VQA},
  author={Luowei Zhou, Hamid Palangi, Lei Zhang, Houdong Hu, Jason J. Corso, Jianfeng Gao},
  journal={arXiv preprint arXiv:1909.11059},
  year={2019}
}

Related Projects/Codebase

Acknowledgement

Our code is mainly based on Li Dong et al.'s UniLM repo. Also, a part of the code is based on pytorch-transformers v0.4.0 and ImageCaptioning.pytorch. We thank the authors for their wonderful open-source efforts.

License

This project is licensed under the license found in the LICENSE file in the root directory of this source tree. Portions of the source code are based on the UniLM project and pytorch-transformers v0.4.0 project.

Owner
Luowei Zhou
Senior Researcher @ Microsoft. UMich Ph.D.
Luowei Zhou
GitHub Repository
Happywhale - Whale and Dolphin Identification Silver🥈 Solution (26/1588)

Kaggle-Happywhale Happywhale - Whale and Dolphin Identification Silver 🥈 Solution (26/1588) 竞赛方案思路 图像数据预处理-标志性特征图片裁剪：首先根据开源的标注数据训练YOLOv5x6目标检测模型，将训练集

Franxx 20 Nov 14, 2022
Fusion-DHL: WiFi, IMU, and Floorplan Fusion for Dense History of Locations in Indoor Environments

Fusion-DHL: WiFi, IMU, and Floorplan Fusion for Dense History of Locations in Indoor Environments Paper: arXiv (ICRA 2021) Video : https://youtu.be/CC

Sachini Herath 68 Jan 03, 2023
Dataset Condensation with Contrastive Signals

Dataset Condensation with Contrastive Signals This repository is the official implementation of Dataset Condensation with Contrastive Signals (DCC). T

3 May 19, 2022
A Novel Incremental Learning Driven Instance Segmentation Framework to Recognize Highly Cluttered Instances of the Contraband Items

A Novel Incremental Learning Driven Instance Segmentation Framework to Recognize Highly Cluttered Instances of the Contraband Items This repository co

Taimur Hassan 3 Mar 16, 2022
Spatiotemporal resampling methods for mlr3

mlr3spatiotempcv Package website: release | dev Spatiotemporal resampling methods for mlr3. This package extends the mlr3 package framework with spati

45 Nov 21, 2022
PURE: End-to-End Relation Extraction

PURE: End-to-End Relation Extraction This repository contains (PyTorch) code and pre-trained models for PURE (the Princeton University Relation Extrac

Princeton Natural Language Processing 657 Jan 09, 2023
Bayesian Image Reconstruction using Deep Generative Models

Bayesian Image Reconstruction using Deep Generative Models R. Marinescu, D. Moyer, P. Golland For technical inquiries, please create a Github issue. F

Razvan Valentin Marinescu 51 Nov 23, 2022
"Exploring Vision Transformers for Fine-grained Classification" at CVPRW FGVC8

FGVC8 Exploring Vision Transformers for Fine-grained Classification paper presented at the CVPR 2021, The Eight Workshop on Fine-Grained Visual Catego

Marcos V. Conde 19 Dec 06, 2022
Lightwood is Legos for Machine Learning.

Lightwood is like Legos for Machine Learning. A Pytorch based framework that breaks down machine learning problems into smaller blocks that can be glu

MindsDB Inc 312 Jan 08, 2023
My freqtrade strategies

My freqtrade-strategies Hi there! This is repo for my freqtrade-strategies. My name is Ilya Zelenchuk, I'm a lecturer at the SPbU university (https://

171 Dec 05, 2022
dualFace: Two-Stage Drawing Guidance for Freehand Portrait Sketching (CVMJ)

dualFace dualFace: Two-Stage Drawing Guidance for Freehand Portrait Sketching (CVMJ) We provide python implementations for our CVM 2021 paper "dualFac

Haoran XIE 46 Nov 10, 2022
Why Are You Weird? Infusing Interpretability in Isolation Forest for Anomaly Detection

Why, hello there! This is the supporting notebook for the research paper — Why Are You Weird? Infusing Interpretability in Isolation Forest for Anomal

2 Dec 14, 2021
Official code for "Towards An End-to-End Framework for Flow-Guided Video Inpainting" (CVPR2022)

E2FGVI (CVPR 2022) English | 简体中文 This repository contains the official implementation of the following paper: Towards An End-to-End Framework for Flo

Media Computing Group @ Nankai University 537 Jan 07, 2023
Pytorch implementation of the Variational Recurrent Neural Network (VRNN).

VariationalRecurrentNeuralNetwork Pytorch implementation of the Variational RNN (VRNN), from A Recurrent Latent Variable Model for Sequential Data. Th

emmanuel 251 Dec 17, 2022
KakaoBrain KoGPT (Korean Generative Pre-trained Transformer)

KoGPT KoGPT (Korean Generative Pre-trained Transformer) https://github.com/kakaobrain/kogpt https://huggingface.co/kakaobrain/kogpt Model Descriptions

Kakao Brain 799 Dec 28, 2022
Text-to-Music Retrieval using Pre-defined/Data-driven Emotion Embeddings

Text2Music Emotion Embedding Text-to-Music Retrieval using Pre-defined/Data-driven Emotion Embeddings Reference Emotion Embedding Spaces for Matching

Minz Won 50 Dec 05, 2022
Official implementation of "Refiner: Refining Self-attention for Vision Transformers".

RefinerViT This repo is the official implementation of "Refiner: Refining Self-attention for Vision Transformers". The repo is build on top of timm an

101 Dec 29, 2022
Code release for Local Light Field Fusion at SIGGRAPH 2019

Local Light Field Fusion Project | Video | Paper Tensorflow implementation for novel view synthesis from sparse input images. Local Light Field Fusion

1.1k Dec 27, 2022
OpenVisionAPI server

🚀 Quick start An instance of ova-server is free and publicly available here: https://api.openvisionapi.com Checkout ova-client for a quick demo. Inst

Open Vision API 93 Nov 24, 2022
An Empirical Investigation of Model-to-Model Distribution Shifts in Trained Convolutional Filters

CNN-Filter-DB An Empirical Investigation of Model-to-Model Distribution Shifts in Trained Convolutional Filters Paul Gavrikov, Janis Keuper Paper: htt

Paul Gavrikov 18 Dec 30, 2022