Fine-grained Image Captioning with CLIP Reward

Authors: Jaemin Cho, David Seunghyun Yoon, Ajinkya Kale, Franck Dernoncourt, Trung Bui, Mohit Bansal
Findings of NAACL 2022 Paper
(Inference using pretrained model on custom image)
Integrated into Huggingface Spaces 🤗 using Gradio. Try out the Web Demo
Try Replicate web demo and docker image here

Code structure

# Configurations
./configs/
    # MLE
    phase1/
    # RL
    phase2/

# COCO caption evaluation
./cider
./coco-caption

# Preprocessing
./clip # CLIP feature extractor
./scripts # COCO preprocessing
./scripts_FineCapEval # FineCapEval preprocessing
./data # Storing preprocessed features

# Core model / Rewards / Data loading
./captioning

# Training / Evaluation
./tools

# Fine-tuning CLIP Text encoder
./retrieval

# Pretrained checkpoints
./save

# Storing original dataset files
./datasets

Setup

Install Dependencies

# Create python environment (optional)
conda create -n clip4caption python=3.7
source activate clip4caption

# python dependenceies
pip install -r requirements.txt

## Install this repo as package
pip install -e .

# Install Detectron2 (optional for training utilities)
pip install detectron2 -f https://dl.fbaipublicfiles.com/detectron2/wheels/cu113/torch1.10/index.html

# Setup coco-caption (optional for text metric evaluation)
git clone https://github.com/clip-vil/cider
git clone https://github.com/clip-vil/coco-caption

cd coco-caption
bash get_stanford_models.sh
bash get_google_word2vec_model.sh

# Install java (optional for METEOR evaluation as part of text metrics)
sudo apt install default-jre

Download Pretrained models

We host model checkpoints via google drive. Download checkpoints as below. The .ckpt file size for captioning and CLIP models are 669.65M and 1.12G, respectively.

# Captioning model
./save/
    clipRN50_cider/
        clipRN50_cider-last.ckpt
    clipRN50_cider_clips/
        clipRN50_cider_clips-last.ckpt
    clipRN50_clips/
        clipRN50_clips-last.ckpt
    clipRN50_clips_grammar/
        clipRN50_clips_grammar-last.ckpt
    clipRN50_mle/
        clipRN50_mle-last.ckpt

# Finetuned CLIP Text encoder
./retrieval/
    save/
        clip_negative_text-last.ckpt

Dataset preparation

# Original dataset files - to be downloaded
./datasets/
    # Download from http://mscoco.org/dataset/#download
    COCO/
        images/
            train2014/
            val2014/
        annotations/
            captions_train2014.json
            captions_val2014.json

    # Download from https://drive.google.com/drive/folders/1jlwInAsVo-PdBdJlmHKPp34dLnxIIMLx
    FineCapEval/
        images/
            XXX.jpg

MS COCO

Download files

./datasets/
    # Download from http://mscoco.org/dataset/#download
     COCO/
        images/
            train2014/
            val2014/
        annotations/
            captions_train2014.json
            captions_val2014.json


./data/
    # Download from http://cs.stanford.edu/people/karpathy/deepimagesent/caption_datasets.zip
    dataset_coco.json

    # Download from from https://drive.google.com/drive/folders/1eCdz62FAVCGogOuNhy87Nmlo5_I0sH2J
    coco-train-words.p

Text processing

python scripts/prepro_labels.py --input_json data/dataset_coco.json --output_json data/cocotalk.json --output_h5 data/cocotalk

Visual feature extraction

python scripts/clip_prepro_feats.py --input_json data/dataset_coco.json --output_dir data/cocotalk --images_root datasets/COCO/images --model_type RN50

# optional (n_jobs)
--n_jobs 4 --job_id 0

Visual fetaure extraction for CLIP-S Reward

python scripts/clipscore_prepro_feats.py --input_json data/dataset_coco.json --output_dir data/cocotalk --images_root datasets/COCO/images

# optional (n_jobs)
--n_jobs 4 --job_id 0

FineCapEval

Download files from https://drive.google.com/drive/folders/1jlwInAsVo-PdBdJlmHKPp34dLnxIIMLx?usp=sharing

./datasets/
    FineCapEval/
        images/
            XXX.jpg

./data/
    FineCapEval.json
    FineCapEval.csv

Visual feature extraction

python scripts_FineCapEval/clip_prepro_feats.py --input_json data/FineCapEval.json --output_dir data/FineCapEval --images_root datasets/FineCapEval/images --model_type RN50

# optional (n_jobs)
--n_jobs 4 --job_id 0

Training and Evaluation

1) MLE training

export MLE_ID='clipRN50_mle'

# Training
python tools/train_pl.py --cfg configs/phase1/$MLE_ID.yml --id $MLE_ID

# Evaluation
EVALUATE=1 python tools/train_pl.py --cfg configs/phase1/$MLE_ID.yml --id $MLE_ID

# Text-to-Iage Retrieval with CLIP VIT-B/32
python tools/eval_clip_retrieval.py --gen_caption_path "./eval_results/$MLE_ID.json"

# Evaluation on FineCapEval
python tools/finecapeval_inference.py --reward mle
python tools/eval_finecapeval.py --generated_id2caption ./FineCapEval_results/clipRN50_mle.json

2) RL finetuning

Reward: CIDEr

export REWARD='cider'
export MLE_ID='clipRN50_mle'
export RL_ID='clipRN50_'$REWARD

# Copy MLE checkpoint as starting point of RL finetuning
mkdir save/$RL_ID
cp save/$MLE_ID/$MLE_ID-last.ckpt save/$RL_ID/$RL_ID-last.ckpt

# Training
python tools/train_pl.py --cfg configs/phase2/$RL_ID.yml --id $RL_ID

# Evaluation
EVALUATE=1 python tools/train_pl.py --cfg configs/phase2/$RL_ID.yml --id $RL_ID

# Text-to-Iage Retrieval with CLIP VIT-B/32
python tools/eval_clip_retrieval.py --gen_caption_path "./eval_results/$RL_ID.json"

# Evaluation on FineCapEval
python tools/finecapeval_inference.py --reward $REWARD
python tools/eval_finecapeval.py --generated_id2caption ./FineCapEval_results/$RL_ID.json

Reward: CLIP-S

export REWARD='clips'
export MLE_ID='clipRN50_mle'
export RL_ID='clipRN50_'$REWARD

# Copy MLE checkpoint as starting point of RL finetuning
mkdir save/$RL_ID
cp save/$MLE_ID/$MLE_ID-last.ckpt save/$RL_ID/$RL_ID-last.ckpt

# Training
python tools/train_pl.py --cfg configs/phase2/$RL_ID.yml --id $RL_ID

# Evaluation
EVALUATE=1 python tools/train_pl.py --cfg configs/phase2/$RL_ID.yml --id $RL_ID

# Text-to-Iage Retrieval with CLIP VIT-B/32
python tools/eval_clip_retrieval.py --gen_caption_path "./eval_results/$RL_ID.json"

# Evaluation on FineCapEval
python tools/finecapeval_inference.py --reward $REWARD
python tools/eval_finecapeval.py --generated_id2caption ./FineCapEval_results/$RL_ID.json

Reward: CLIP-S + CIDEr

export REWARD='clips_cider'
export MLE_ID='clipRN50_mle'
export RL_ID='clipRN50_'$REWARD

# Copy MLE checkpoint as starting point of RL finetuning
mkdir save/$RL_ID
cp save/$MLE_ID/$MLE_ID-last.ckpt save/$RL_ID/$RL_ID-last.ckpt

# Training
python tools/train_pl.py --cfg configs/phase2/$RL_ID.yml --id $RL_ID

# Evaluation
EVALUATE=1 python tools/train_pl.py --cfg configs/phase2/$RL_ID.yml --id $RL_ID

# Text-to-Iage Retrieval with CLIP VIT-B/32
python tools/eval_clip_retrieval.py --gen_caption_path "./eval_results/$RL_ID.json"

# Evaluation on FineCapEval
python tools/finecapeval_inference.py --reward $REWARD
python tools/eval_finecapeval.py --generated_id2caption ./FineCapEval_results/$RL_ID.json

Reward: CLIP-S + Grammar

Run CLIP Finetuning (for grammar) following ./retrieval/README.md
Run RL training using the updated CLIP

export REWARD='clips_grammar'
export MLE_ID='clipRN50_mle'
export RL_ID='clipRN50_'$REWARD

# Copy MLE checkpoint as starting point of RL finetuning
mkdir save/$RL_ID
cp save/$MLE_ID/$MLE_ID-last.ckpt save/$RL_ID/$RL_ID-last.ckpt

# Training
python tools/train_pl.py --cfg configs/phase2/$RL_ID.yml --id $RL_ID

# Evaluation
EVALUATE=1 python tools/train_pl.py --cfg configs/phase2/$RL_ID.yml --id $RL_ID

# Text-to-Iage Retrieval with CLIP VIT-B/32
python tools/eval_clip_retrieval.py --gen_caption_path "./eval_results/$RL_ID.json"

# Evaluation on FineCapEval
python tools/finecapeval_inference.py --reward $REWARD
python tools/eval_finecapeval.py --generated_id2caption ./FineCapEval_results/$RL_ID.json

Acknowledgments

We thank the developers of CLIP-ViL, ImageCaptioning.pytorch, CLIP, coco-caption, cider for their public code release.

Reference

Please cite our paper if you use our models in your works:

@inproceedings{Cho2022CLIPReward,
  title     = {Fine-grained Image Captioning with CLIP Reward},
  author    = {Jaemin Cho and Seunghyun Yoon and Ajinkya Kale and Franck Dernoncourt and Trung Bui and Mohit Bansal},
  booktitle = {Findings of NAACL},
  year      = {2022}
}

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