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)

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

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

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

Pre-trained UniLM: https://github.com/microsoft/unilm
GVD (captioing+grounding): https://github.com/facebookresearch/grounded-video-description
Video DenseCap: https://github.com/salesforce/densecap
MT-DNN: https://github.com/namisan/mt-dnn

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.

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

Related tags

Overview

VLP

Installation

Conda Environment (Option I, Recommended)

Docker Image (Option II)

Data Preparation

Experiment Overview

Pre-training

Fine-tuning

COCO Captions

VQA 2.0

Flickr30k Captions

Inference and Testing

COCO Captions

VQA 2.0

Flickr30k Captions

Testing

Misc

Reference

Related Projects/Codebase

Acknowledgement

License

Owner

Luowei Zhou

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