# GenerativeImage2Text **Repository Path**: Yzc526/GenerativeImage2Text ## Basic Information - **Project Name**: GenerativeImage2Text - **Description**: No description available - **Primary Language**: Unknown - **License**: MIT - **Default Branch**: main - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 0 - **Forks**: 0 - **Created**: 2025-07-03 - **Last Updated**: 2025-07-03 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README # Introduction This repo presents some example codes to reproduce some results in [GIT: A Generative Image-to-text Transformer for Vision and Language](https://arxiv.org/abs/2205.14100). # Installation - Install [azfuse](https://github.com/microsoft/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 ```shell git clone https://github.com/microsoft/GenerativeImage2Text.git cd GenerativeImage2Text ``` - Install the package ```shell pip install -r requirements.txt python setup.py build develop ``` # Inference - Inference on a single image or multiple frames: ```shell # 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': '', \ }" # single image, question answering 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_VQAv2', \ 'prefix': 'what is it?', \ }" # 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': '', \ }" # multiple images, question answering 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_MSRVTT_QA', \ 'prefix': 'what is it?', \ }" ``` - If `prefix` is empty, it is effectively the captioning task. - If `prefix` is a question, it is effectively the visual question answering 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. - `model_name` here can be the following. Performance details can be found in the reference paper. | model_name | Information | Performance | |---------------------|-----------------------------------------------------|------------------------ | | GIT_BASE | pretrained on 4M images | | | GIT_BASE_COCO | fine-tuned on COCO | CIDEr: 131.4 | | GIT_BASE_TEXTCAPS | fine-tuned on TextCaps for captioning | val/CIDEr: 64.9 | | GIT_BASE_VQAv2 | fine-tuned on VQAv2 | test-dev: 72.72 | | GIT_BASE_TEXTVQA | fine-tuned on TextVQA | val/acc: 18.81 | | GIT_BASE_VATEX | fine-tuned on VATEX for captioning | public/test/CIDEr: 60.0 | | GIT_BASE_MSRVTT | fine-tuned on MSRVTT for captioning | test/CIDEr: 57.8 | | GIT_BASE_MSRVTT_QA | fine-tuned on MSRVTT for question answering | acc: 41.0 | | GIT_LARGE | pretrained on 14M images | | | GIT_LARGE_COCO | fine-tuned on COCO | CIDEr: 138.5 | | GIT_LARGE_TEXTCAPS | fine-tuned on TextCaps for captioning | val/CIDEr: 106.3 | | GIT_LARGE_VQAv2 | fine-tuned on VQAv2 | test-dev: 75.51 | | GIT_LARGE_TEXTVQA | fine-tuned on TextVQA | val/acc: 37.47 | | GIT_LARGE_VATEX | fine-tuned on VATEX for captioning | public/test/CIDEr: 72.5 | | GIT_LARGE_MSRVTT | fine-tuned on MSRVTT for captioning | test/CIDEr: 64.1 | | GIT_LARGE_MSRVTT_QA | fine-tuned on MSRVTT for question answering | acc: 42.7 | - In the dataset of cc12m, the caption may contain some special tags to hide person names and the model might also predict such special tokens. To eliminate this issue, we remove these captions (around 25\% in cc12m), and re-trained the large-sized model. The base-sized model is not affected as cc12 is not part of the training data. | model_name | Information | Performance | |---------------------|-----------------------------------------------------|------------------------ | | GIT_LARGE_R | pretrained on 14M images with special tag removed | | | GIT_LARGE_R_COCO | fine-tuned on COCO | CIDEr: 137.6 | | GIT_LARGE_R_TEXTCAPS| fine-tuned on TextCaps for captioning | val/CIDEr: 105.3 | - Inference on a [TSV](https://en.wikipedia.org/wiki/Tab-separated_values) file, which is a collection of multiple images. - Data format (for information only) - image TSV: Each row has two columns. The first is the image key; the second is base64-encoded jpg or png bit string. - caption or question tsv: Each row has two columns. The first is the image key; the second is a list of dictionaries in the json format. For caption TSV, the dictionary should contain at least the field of `'caption'`. For the question answering TSV, it should contain at least `question_id` and `question`. - inference on [COCO](https://cocodataset.org) Karpathy test. 1. Inference. ```shell # base AZFUSE_TSV_USE_FUSE=1 python -m generativeimage2text.inference -p "{'type': 'test_git_inference_single_tsv', \ 'image_tsv': 'data/coco_caption/test.img.tsv', \ 'model_name': 'GIT_BASE_COCO', \ 'question_tsv': null, \ 'out_tsv': 'inference/GIT_BASE_COCO/coco.tsv', \ }" # GIT_LARGE_COCO. If there are 8 GPUs, it can parallel by mpirun -n 8 AZFUSE_TSV_USE_FUSE=1 mpirun -n 8 python -m generativeimage2text.inference -p "{'type': 'test_git_inference_single_tsv', \ 'image_tsv': 'data/coco_caption/test.img.tsv', \ 'model_name': 'GIT_LARGE_COCO', \ 'question_tsv': null, \ 'out_tsv': 'inference/GIT_LARGE_COCO/coco.tsv', \ }" ``` 2. Calculate the evaluation metric ```shell # base AZFUSE_TSV_USE_FUSE=1 python -m generativeimage2text.inference -p "{'type': 'evaluate_on_coco_caption', \ 'res_file': 'inference/GIT_BASE_COCO/coco.tsv', \ 'label_file': 'data/coco_caption/test.caption.tsv', \ }" ``` The CIDEr score should be 131.35 for `GIT_BASE_COCO` and 138.45 for `GIT_LARGE_COCO`. If you get lower score (e.g. 126 for the base model), the reason could be the misalignment of the environment, e.g. pytorch version. 3. (optional) To exactly reproduce the number, please run the following: ```bash nvidia-docker run --ipc=host amsword/setup:py38pt19u20cu11 \ bash -c "mkdir -p /tmp/code \ && cd /tmp/code \ && pip install git+https://github.com/microsoft/azfuse.git \ && git clone https://github.com/amsword/generativeimage2text.git \ && cd generativeimage2text \ && pip install -r requirements.txt \ && python setup.py build develop \ && AZFUSE_TSV_USE_FUSE=1 python -m generativeimage2text.inference -p "{'type': 'test_git_inference_single_tsv', \ 'image_tsv': 'data/coco_caption/test.img.tsv', \ 'model_name': 'GIT_BASE_COCO', \ 'question_tsv': null, \ 'out_tsv': 'inference/GIT_BASE_COCO/coco.tsv', \ }" \ && AZFUSE_TSV_USE_FUSE=1 python -m generativeimage2text.inference -p "{'type': 'evaluate_on_coco_caption', \ 'res_file': 'inference/GIT_BASE_COCO/coco.tsv', \ 'label_file': 'data/coco_caption/test.caption.tsv', \ 'outfile': 'inference/GIT_BASE_COCO/coco.score.json', \ }" \ && cat inference/GIT_BASE_COCO/coco.score.json \ " ``` - Inference on [vqa](https://visualqa.org/index.html) test 1. Inference ```shell # base model AZFUSE_TSV_USE_FUSE=1 python -m generativeimage2text.inference -p "{'type': 'test_git_inference_single_tsv', \ 'image_tsv': 'data/TaxVQAv2/test.tsv', \ 'model_name': 'GIT_BASE_VQAv2', \ 'question_tsv': 'data/TaxVQAv2/test.caption.tsv', \ 'out_tsv': 'inference/GIT_BASE_VQAv2/snapshot/vqav2.tsv', \ }" # GIT_LARGE_VQAv2 with 8 GPUs. AZFUSE_TSV_USE_FUSE=1 mpirun -n 8 python -m generativeimage2text.inference -p "{'type': 'test_git_inference_single_tsv', \ 'image_tsv': 'data/TaxVQAv2/test.tsv', \ 'model_name': 'GIT_LARGE_VQAv2', \ 'question_tsv': 'data/TaxVQAv2/test.caption.tsv', \ 'out_tsv': 'inference/GIT_LARGE_VQAv2/snapshot/vqav2.tsv', \ }" ``` 2. Convert the output tsv to the json format for submission to [evalai](https://eval.ai/web/challenges/challenge-page/830/overview) ```shell # base model AZFUSE_TSV_USE_FUSE=1 python -m generativeimage2text.inference -p "{'type': 'convert_tsv_to_vqa_json', \ 'predict_file': 'inference/GIT_BASE_VQAv2/snapshot/vqav2.tsv', \ 'out_json': 'inference/GIT_BASE_VQAv2/snapshot/vqav2.json', \ }" # large model AZFUSE_TSV_USE_FUSE=1 python -m generativeimage2text.inference -p "{'type': 'convert_tsv_to_vqa_json', \ 'predict_file': 'inference/GIT_LARGE_VQAv2/snapshot/vqav2.tsv', \ 'out_json': 'inference/GIT_LARGE_VQAv2/snapshot/vqav2.json', \ }" ``` Submit the file of `inference/GIT_BASE_VQAv2/snapshot/vqav2.json` to evalai and you should get `72.72` on `test-dev`. If it is `GIT_LARGE_VQAv2`, the accuracy is `75.51`. 3. (optional) To exactly reproduce the number, you can use the following: ```shell # base model nvidia-docker run --ipc=host amsword/setup:py38pt19u20cu11 \ bash -c "mkdir /tmp/code \ && cd /tmp/code \ && pip install git+https://github.com/microsoft/azfuse.git \ && git clone https://github.com/amsword/generativeimage2text.git \ && cd generativeimage2text \ && pip install -r requirements.txt \ && python setup.py build develop \ && AZFUSE_TSV_USE_FUSE=1 python -m generativeimage2text.inference -p "{'type': 'test_git_inference_single_tsv', \ 'image_tsv': 'data/TaxVQAv2/test.tsv', \ 'model_name': 'GIT_BASE_VQAv2', \ 'question_tsv': 'data/TaxVQAv2/test.caption.tsv', \ 'out_tsv': 'inference/GIT_BASE_VQAv2/snapshot/vqav2.tsv', \ }" \ && AZFUSE_TSV_USE_FUSE=1 python -m generativeimage2text.inference -p "{'type': 'convert_tsv_to_vqa_json', \ 'predict_file': 'inference/GIT_BASE_VQAv2/snapshot/vqav2.tsv', \ 'out_json': 'inference/GIT_BASE_VQAv2/snapshot/vqav2.json', \ }" \ }" ``` Note that, please modify the docker command properly so that the output file can be saved permanently to the host machine. It is also recommended to run it inside the docker container by ```shell nvidia-docker run --ipc=host amsword/setup:py38pt19u20cu11 sleep infinity docker ps # get the docker container ID docker exec -it container_id /bin/bash # attach inside the docker container # all other commands to run the inference. ``` # 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'], \ }" ``` - VQA ``` python -m generativeimage2text.train -p "{'type': 'forward_backward_example', \ 'image_files': ['aux_data/images/1.jpg', 'aux_data/images/2.jpg'], \ 'prefixs': ['what is this?', 'how many trees?'], \ 'captions': ['several boats in a large body of water', '1'], \ }" ``` # ImageNet ## Class ID to unique readable names - Save the file of `LOC_synset_mapping.txt` from [Kaggle](https://www.kaggle.com/competitions/imagenet-object-localization-challenge/data?select=LOC_synset_mapping.txt). under `aux_data/imagenet/` - Convert the wordnet ID to readable names as follows ```python python -m generativeimage2text.data_prepare -p "{'type': 'generate_imagenet_unique_names'}" ``` The input file is hard coded as `./aux_data/imagenet/LOC_synset_mapping.txt` and the output file is `./aux_data/imagenet/imagenet_unique_readable_names.txt` # Citation Please consider to cite the following reference if it helps. ```text @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} } ``` # Misc The model is now [available](https://huggingface.co/docs/transformers/model_doc/git) in 🤗 Transformers. You can also find a fine-tuning guide on image captioning with GIT [here](https://huggingface.co/docs/transformers/main/en/tasks/image_captioning). Thanks to [Niels Rogge](https://github.com/NielsRogge) for contributing the model to 🤗 Transformers and [Sayak Paul](https://github.com/sayakpaul) for the fine-tuning guide. # Acknowledgement Part of the code is based on [transformers](https://github.com/huggingface/transformers), [clip](https://github.com/openai/CLIP), [maskrcnn-benchmark](https://github.com/facebookresearch/maskrcnn-benchmark), [oscar](https://github.com/microsoft/Oscar), [virtex](https://github.com/kdexd/virtex). ## Contributing This project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit https://cla.opensource.microsoft.com. 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