dandelin / UNITER

Research code for ECCV 2020 paper "UNITER: UNiversal Image-TExt Representation Learning"

Home Page:https://arxiv.org/abs/1909.11740

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UNITER: UNiversal Image-TExt Representation Learning

This is the official repository of UNITER (ECCV 2020). This repository currently supports finetuning UNITER on NLVR2, VQA, VCR, SNLI-VE, and Image-Text Retrieval for COCO and Flickr30k. Both UNITER-base and UNITER-large pre-trained checkpoints are released. UNITER-base pre-training with in-domain data is also available.

Overview of UNITER

Some code in this repo are copied/modified from opensource implementations made available by PyTorch, HuggingFace, OpenNMT, and Nvidia. The image features are extracted using BUTD.

Requirements

We provide Docker image for easier reproduction. Please install the following:

Our scripts require the user to have the docker group membership so that docker commands can be run without sudo. We only support Linux with NVIDIA GPUs. We test on Ubuntu 18.04 and V100 cards. We use mixed-precision training hence GPUs with Tensor Cores are recommended.

Quick Start

NOTE: Please run bash scripts/download_pretrained.sh $PATH_TO_STORAGE to get our latest pretrained checkpoints. This will download both the base and large models.

We use NLVR2 as an end-to-end example for using this code base.

  1. Download processed data and pretrained models with the following command.

    bash scripts/download_nlvr2.sh $PATH_TO_STORAGE

    After downloading you should see the following folder structure:

    ├── ann
    │   ├── dev.json
    │   └── test1.json
    ├── finetune
    │   ├── nlvr-base
    │   └── nlvr-base.tar
    ├── img_db
    │   ├── nlvr2_dev
    │   ├── nlvr2_dev.tar
    │   ├── nlvr2_test
    │   ├── nlvr2_test.tar
    │   ├── nlvr2_train
    │   └── nlvr2_train.tar
    ├── pretrained
    │   └── uniter-base.pt
    └── txt_db
        ├── nlvr2_dev.db
        ├── nlvr2_dev.db.tar
        ├── nlvr2_test1.db
        ├── nlvr2_test1.db.tar
        ├── nlvr2_train.db
        └── nlvr2_train.db.tar
    
  2. Launch the Docker container for running the experiments.

    # docker image should be automatically pulled
    source launch_container.sh $PATH_TO_STORAGE/txt_db $PATH_TO_STORAGE/img_db \
        $PATH_TO_STORAGE/finetune $PATH_TO_STORAGE/pretrained

    The launch script respects $CUDA_VISIBLE_DEVICES environment variable. Note that the source code is mounted into the container under /src instead of built into the image so that user modification will be reflected without re-building the image. (Data folders are mounted into the container separately for flexibility on folder structures.)

  3. Run finetuning for the NLVR2 task.

    # inside the container
    python train_nlvr2.py --config config/train-nlvr2-base-1gpu.json
    
    # for more customization
    horovodrun -np $N_GPU python train_nlvr2.py --config $YOUR_CONFIG_JSON
  4. Run inference for the NLVR2 task and then evaluate.

    # inference
    python inf_nlvr2.py --txt_db /txt/nlvr2_test1.db/ --img_db /img/nlvr2_test/ \
        --train_dir /storage/nlvr-base/ --ckpt 6500 --output_dir . --fp16
    
    # evaluation
    # run this command outside docker (tested with python 3.6)
    # or copy the annotation json into mounted folder
    python scripts/eval_nlvr2.py ./results.csv $PATH_TO_STORAGE/ann/test1.json

    The above command runs inference on the model we trained. Feel free to replace --train_dir and --ckpt with your own model trained in step 3. Currently we only support single GPU inference.

  5. Customization

    # training options
    python train_nlvr2.py --help
    • command-line argument overwrites JSON config files
    • JSON config overwrites argparse default value.
    • use horovodrun to run multi-GPU training
    • --gradient_accumulation_steps emulates multi-gpu training
  6. Misc.

    # text annotation preprocessing
    bash scripts/create_txtdb.sh $PATH_TO_STORAGE/txt_db $PATH_TO_STORAGE/ann
    
    # image feature extraction (Tested on Titan-Xp; may not run on latest GPUs)
    bash scripts/extract_imgfeat.sh $PATH_TO_IMG_FOLDER $PATH_TO_IMG_NPY
    
    # image preprocessing
    bash scripts/create_imgdb.sh $PATH_TO_IMG_NPY $PATH_TO_STORAGE/img_db

    In case you would like to reproduce the whole preprocessing pipeline.

Downstream Tasks Finetuning

VQA

NOTE: train and inference should be ran inside the docker container

  1. download data
    bash scripts/download_vqa.sh $PATH_TO_STORAGE
    
  2. train
    horovodrun -np 4 python train_vqa.py --config config/train-vqa-base-4gpu.json \
        --output_dir $VQA_EXP
    
  3. inference
    python inf_vqa.py --txt_db /txt/vqa_test.db --img_db /img/coco_test2015 \
        --output_dir $VQA_EXP --checkpoint 6000 --pin_mem --fp16
    
    The result file will be written at $VQA_EXP/results_test/results_6000_all.json, which can be submitted to the evaluation server

VCR

NOTE: train and inference should be ran inside the docker container

  1. download data
    bash scripts/download_vcr.sh $PATH_TO_STORAGE
    
  2. train
    horovodrun -np 4 python train_vcr.py --config config/train-vcr-base-4gpu.json \
        --output_dir $VCR_EXP
    
  3. inference
    horovodrun -np 4 python inf_vcr.py --txt_db /txt/vcr_test.db \
        --img_db "/img/vcr_gt_test/;/img/vcr_test/" \
        --split test --output_dir $VCR_EXP --checkpoint 8000 \
        --pin_mem --fp16
    
    The result file will be written at $VCR_EXP/results_test/results_8000_all.csv, which can be submitted to VCR leaderboard for evluation.

Visual Entailment (SNLI-VE)

NOTE: train should be ran inside the docker container

  1. download data
    bash scripts/download_ve.sh $PATH_TO_STORAGE
    
  2. train
    horovodrun -np 2 python train_ve.py --config config/train-ve-base-2gpu.json \
        --output_dir $VE_EXP
    

Image-Text Retrieval

download data

bash scripts/download_itm.sh $PATH_TO_STORAGE

NOTE: Image-Text Retrieval is computationally heavy, especially on COCO.

Zero-shot Image-Text Retrieval (Flickr30k)

# every image-text pair has to be ranked; please use as many GPUs as possible
horovodrun -np $NGPU python inf_itm.py \
    --txt_db /txt/itm_flickr30k_test.db --img_db /img/flickr30k \
    --checkpoint /pretrain/uniter-base.pt --model_config /src/config/uniter-base.json \
    --output_dir $ZS_ITM_RESULT --fp16 --pin_mem

Image-Text Retrieval (Flickr30k)

  • normal finetune
    horovodrun -np 8 python train_itm.py --config config/train-itm-flickr-base-8gpu.json
    
  • finetune with hard negatives
    horovodrun -np 16 python train_itm_hard_negatives.py \
        --config config/train-itm-flickr-base-16gpu-hn.jgon
    

Image-Text Retrieval (COCO)

  • finetune with hard negatives
    horovodrun -np 16 python train_itm_hard_negatives.py \
        --config config/train-itm-coco-base-16gpu-hn.json
    

Pre-tranining

download

bash scripts/download_indomain.sh $PATH_TO_STORAGE

pre-train

horovodrun -np 8 python pretrain.py --config config/pretrain-indomain-base-8gpu.json \
    --output_dir $PRETRAIN_EXP

Unfortunately, we cannot host CC/SBU features due to their large size. Users will need to process them on their own. We will provide a smaller sample for easier reference to the expected format soon.

Citation

If you find this code useful for your research, please consider citing:

@inproceedings{chen2020uniter,
  title={Uniter: Universal image-text representation learning},
  author={Chen, Yen-Chun and Li, Linjie and Yu, Licheng and Kholy, Ahmed El and Ahmed, Faisal and Gan, Zhe and Cheng, Yu and Liu, Jingjing},
  booktitle={ECCV},
  year={2020}
}

License

MIT

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Research code for ECCV 2020 paper "UNITER: UNiversal Image-TExt Representation Learning"

https://arxiv.org/abs/1909.11740


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