Introduction

This repo presents some example codes to reproduce some results in GIT: A Generative Image-to-text Transformer for Vision and Language.

Installation

• Install azfuse. The tool is used to automatically download the data. The configuration of AzFuse has already been in this repo.

• Download the source code by

  git clone https://github.com/s5901370/GIT.git
  cd GenerativeImage2Text

• Install the package

  pip install -r requirements.txt
  python setup.py build develop
  sudo apt install openjdk-11-jdk

Inference

• Inference on a single image or multiple frames:

  # single image, captioning
  AZFUSE_TSV_USE_FUSE=1 python -m generativeimage2text.inference -p "{'type': 'test_git_inference_single_image', \
        'image_path': 'aux_data/images/1.jpg', \
        'model_name': 'GIT_BASE', \
        'prefix': '', \
  }"
  # multiple images, captioning
  AZFUSE_TSV_USE_FUSE=1 python -m generativeimage2text.inference -p "{'type': 'test_git_inference_single_image', \
        'image_path': ['aux_data/images/1.jpg', 'aux_data/images/1.jpg', 'aux_data/images/1.jpg', 'aux_data/images/1.jpg', 'aux_data/images/1.jpg', 'aux_data/images/1.jpg'], \
        'model_name': 'GIT_BASE_VATEX', \
        'prefix': '', \
  }"

  • If prefix is empty, it is effectively the captioning task.
  • Use a list for image_path if it is for video. The example here is 6 identical images, only for a demo purpose. It should be different image frames from a video.

Training

The repo shows the key code path of constructing the network input with transformations and forward/backward. The code can be plugged into any trainer easily. Here is the example for the base model.

• Pretraining/captioning

  python -m generativeimage2text.train -p "{'type': 'forward_backward_example', \
                  'image_files': ['aux_data/images/1.jpg', 'aux_data/images/2.jpg'], \
                  'captions': ['a couple of boats in a large body of water.', 'a view of a mountain with a tree'], \
              }"
  

Citation

Please consider to cite the following reference if it helps.

@article{wang2022git,
  title={GIT: A Generative Image-to-text Transformer for Vision and Language},
  author={Wang, Jianfeng and Yang, Zhengyuan and Hu, Xiaowei and Li, Linjie and Lin, Kevin and Gan, Zhe and Liu, Zicheng and Liu, Ce and Wang, Lijuan},
  journal={arXiv preprint arXiv:2205.14100},
  year={2022}
}

Acknowledgement

Part of the code is based on transformers, clip, maskrcnn-benchmark, oscar, virtex.

Contributing

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