QtacierP / transformer-image-captioning

Implementation of the paper CPTR : FULL TRANSFORMER NETWORK FOR IMAGE CAPTIONING

transformer-image-captioning

Implementation of the paper CPTR : FULL TRANSFORMER NETWORK FOR IMAGE CAPTIONING

architecture of the CPTR model for image captioning

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predictions

prerequisites

• git
• python3
• python3-venv
• docker

clone the repo and prepare data

    # clone 
    git clone https://github.com/Milkymap/transformer-image-captioning
    cd transformer-image-captioning
    # prepare data 
    # models is the space where resnet152 and clip will be saved 
    # models also contains the checkpoints during training 
    # images contains a set of image files for inference time(see docker describe step)
    # source contains the data used for training 
        # the data is in the next format 
        # images directory : contains all images for training 
        # captions.json    : is a hashmap(image_file_id=>[text, text, text])
    # target contains extracted features such as vectors, tokenizer, vocabulary
    mkdir models images source target 

docker build and run

    docker build -t capformer:0.0 -f Dockerfile.gpu

docker run processing step

    docker run 
        --rm 
        --tty 
        --name capformer 
        --gpus all 
        -v $(pwd)/source:/home/solver/source 
        -v $(pwd)/models:/home/solver/models 
        -v $(pwd)/target:/home/solver/target
        -v $(pwd)/images:/home/solver/images  
        -e TERM=xterm-256color 
        capformer:0.0 processing 
            --path2images /home/solver/source/images 
            --path2captions /home/solver/source/captions.json 
            --path2vectorizer /home/solver/models/resnet152.th 
            --extension jpg 
            --path2features /home/solver/target/map_img2features.pkl 
            --path2tokenids /home/solver/target/zip_img2tokenids.pkl 
            --path2vocabulary /home/solver/target/vocabulary.pkl

docker run learning step

    docker run 
        --rm 
        --tty 
        --name capformer 
        --gpus all 
        -v $(pwd)/source:/home/solver/source 
        -v $(pwd)/models:/home/solver/models 
        -v $(pwd)/target:/home/solver/target
        -v $(pwd)/images:/home/solver/images  
        -e TERM=xterm-256color 
        capformer:0.0
        learning 
            --path2features /home/solver/target/map_img2features.pkl 
            --path2tokenids /home/solver/target/zip_img2tokenids.pkl 
            --path2vocabulary /home/solver/target/vocabulary.pkl 
            --nb_epochs 92 
            --bt_size 128 
            --path2checkpoint /home/solver/models/checkpoint_128.th 
            --checkpoint 16 
            --start 0

docker run describe step

    docker run 
        --rm 
        --tty 
        --name capformer 
        --gpus all 
        -v $(pwd)/source:/home/solver/source 
        -v $(pwd)/models:/home/solver/models 
        -v $(pwd)/target:/home/solver/target 
        -v $(pwd)/images:/home/solver/images 
        -e TERM=xterm-256color 
        capformer:0.0 
        describe 
            --path2vectorizer /home/solver/models/resnet152.th 
            --path2ranker /home/solver/models/ranker.pkl 
            --path2vocabulary /home/solver/target/vocabulary.pkl 
            --path2checkpoint /home/solver/models/checkpoint_128.th 
            --beam_width 17 
            --path2image /home/solver/images/bob.jpg

structure of the project

this project is based on opensource libraries such as [pytorch, clip(openai), opencv, PIL] It contains :

• core.py
  • this is the main file of the project
  • it contains the definition of the transformer
  • it is based on the paper Attention Is All You Need
  • i added some modifications for handling multiple output of the decoder
• dataset.py
  • this file contains two classes :
  • DatasetForFeaturesExtraction
  • DatasetForTraining
• model.py
  • this file contains the definition of the CPTR model
  • it uses the transformer defined on the core module
  • it has some additional moduless like : token_embedding, prediction_head
• libraries
  • contains usefull functions such as :
  • log handler
  • tokenization
  • features extraction
  • model loading
  • beam and greedy search for caption generation
• static
  • contains images and fonts for the readme
• main.py
  • this is the entrypoint of the program
  • it defines three subcommands
  • processing : for features extraction and tokenization
  • learning : training loop of the CPTR
  • describe : generate caption by taking an image path
• .gitignore
• .dockerignore
• Dockerfile.gpu
• LICENCE
• README.md

Citations

@misc{Liu2021cptr,
    title   = {CPTR: FULL TRANSFORMER NETWORK FOR IMAGE CAPTIONING}, 
    author  = {Wei Liu, Sihan Chen, Longteng Guo, Xinxin Zhu1, Jing Liu1},
    year    = {2021},
    eprint  = {2101.10804},
    archivePrefix = {arXiv},
    primaryClass = {cs.CV}
}