Technical Report

The technical report can be found here

Additional Setup for UoE Team & convenience scripts

The following are additional setup steps that are needed to get this working. You should still follow all the directions in the official "Introduction" below this.

Installation

Install additional requirements

pip install -r requirements_2.txt

Download pretrained model

./setup_download_model.sh

Install java

sudo apt install default-jdk

GIT model setup

pip install -r requirements.txt
python setup.py build develop

General Workflow:

1. Download Data

Calling ./setup_download_data.sh will do this for you and setup the following directory structure

video_summarisation_git/data/
|
|-- category.txt ...................... # video category name to id mapping file
|
|-- train_val/ ........................ # dir for training & validation sets
|   |-- train_val_videodatainfo.json .. # annotation file
|   |-- pyscenedetect_frames/ ......... # dir for pyscenedetect sampled frames
|   |-- random_frames/ ................ # auto-generated: dir for randonmly sampled frames
|   |-- transnet_frames/ .............. # auto-generated: dir for transnet sampled frames
|   `-- videos/ ....................... # parent dir for videos (each video should have its own folder inside this dir)
|
`-- test/ ............................. # dir for test set (structure same as train_val)                             
    |-- test_videodatainfo.json ....... # annotation file
    `-- [...]

2. Sample Frames

You can download presampled frames here

Download and unzip them in /data/train_val or /data/train as appropriate.

Training (2023-03-20)
Test (2023-03-20)
out of order frames (out of date)
- ~~https://storage.googleapis.com/mlpgit/data/train_val/train_val.zip~~
- ~~https://storage.googleapis.com/mlpgit/data/test/test_frames.zip~~

Or generate them yourself with a script (will approx 40hrs w/ a k80, or 6hrs+ with a A100)

./setup_sample_frames.sh train # sample frames for training data
./setup_sample_frames.sh test # same for test

Or do it piecemeal by hand:

open /setup_sample_frames.sh to get an idea of the commands to run for each sampling method.

Alternatively, you can look at the actual samplers in /sampling_scripts

3. Create training csv

# for random frames
python command_builder/training_command.py -d data/train_val/random_frames/ -c data/train_val/train_val_videodatainfo.json

# or for transnet frames
python command_builder/training_command.py -d data/train_val/transnet_frames/ -c data/train_val/train_val_videodatainfo.json

# or for pyscenedetect frames
python command_builder/training_command.py -d data/train_val/pyscenedetect_frames/ -c data/train_val/train_val_videodatainfo.json

4. Finetune Model or Download Already Finetuned Models

Download one already finetuned

onedrive

Onedrive link

GCloud bucket (faster but costs $$)

finetune your own

Do this for ONE selected sampling method using the following.

Alternatively you can call ./runner.sh which should have everything you need, and will be representative of the last data you called the training command builder on

python -m generativeimage2text.finetune -p '{
    "type": "train",
    "model_name": "GIT_BASE",
    "model_path": "model.pt",
    "batch_size": 3,
    "epochs": 20,
    "train_csv": "data/train_val/{FRAME DIRECTORY HERE}/processed_data_train.csv", # Be sure to swap out {FRAME DIRECTORY HERE} for the directory where your frames are
    "validation_csv": "data/train_val/{FRAME DIRECTORY HERE}/processed_data_validate.csv",
    "validation_annotations_json": "data/train_val/train_val_videodatainfo.json" #path to annotations file
}

5. Run Inference

on test set:

python -m generativeimage2text.vc_inference -p "{'type': 'multi_video_inference', 'videos_csv': '', 'annotations_json_path': '', 'model_path':'./msrvtt_model_epoch1.pt', 'model_name':'GIT_BASE', 'predictions_file':None}"

on multiple models

python -m generativeimage2text.vc_inference -p "{'type': 'multi_video_inference_dir', 'videos_csv': '', 'annotations_json_path': '', 'model_dir':'./model_transnet', 'model_name':'GIT_BASE'}"

Inference Results

one drive *Onedrive link
gcp bucket
Viewable HTML Results
- onedrive link
- gpc bucket

Resources Created:

merged frames thumbnails for sampling comparison
models (see section #4)

FAQ

Where can I find a A100 to finetune on?
- try the netherlands region OR salt lake city US
errors about cv2, pandas, numpy, etc.
- make sure you've installed the second requirements file as described above
errors about model.py when running the fine tuning script
- make sure you've downloaded vatex as described above in the root dir of the project

below this point is the original readme

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/microsoft/GenerativeImage2Text.git
cd GenerativeImage2Text

Install the package

pip install -r requirements.txt
python setup.py build develop

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': '', \
}"
# 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_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_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 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 Karpathy test.

Inference.

# 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', \
}"

Calculate the evaluation metric

# 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.

(optional) To exactly reproduce the number, please run the following:

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 test

Inference

# 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', \
}"

Convert the output tsv to the json format for submission to evalai

# 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.

(optional) To exactly reproduce the number, you can use the following:

# 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

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. under aux_data/imagenet/
Convert the wordnet ID to readable names as follows
```
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.

@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

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.

When you submit a pull request, a CLA bot will automatically determine whether you need to provide a CLA and decorate the PR appropriately (e.g., status check, comment). Simply follow the instructions provided by the bot. You will only need to do this once across all repos using our CLA.

This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact opencode@microsoft.com with any additional questions or comments.

Trademarks

This project may contain trademarks or logos for projects, products, or services. Authorized use of Microsoft trademarks or logos is subject to and must follow Microsoft's Trademark & Brand Guidelines. Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship. Any use of third-party trademarks or logos are subject to those third-party's policies.

Name		Name	Last commit message	Last commit date
Latest commit History 243 Commits
.github/workflows		.github/workflows
aux_data		aux_data
command_builder		command_builder
data		data
data_subset		data_subset
generativeimage2text		generativeimage2text
reporting_scripts		reporting_scripts
sampling_scripts		sampling_scripts
.gitignore		.gitignore
CODE_OF_CONDUCT.md		CODE_OF_CONDUCT.md
LICENSE		LICENSE
MANIFEST.in		MANIFEST.in
README.md		README.md
SECURITY.md		SECURITY.md
SUPPORT.md		SUPPORT.md
prediction_presentation.ipynb		prediction_presentation.ipynb
requirements.txt		requirements.txt
requirements_2.txt		requirements_2.txt
runner.sh		runner.sh
setup.py		setup.py
setup_download_data.sh		setup_download_data.sh
setup_download_model.sh		setup_download_model.sh
setup_sample_frames.sh		setup_sample_frames.sh

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SanniM3/video_summarisation_git

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Technical Report

Additional Setup for UoE Team & convenience scripts

Installation

General Workflow:

1. Download Data

2. Sample Frames

You can download presampled frames here

Or generate them yourself with a script (will approx 40hrs w/ a k80, or 6hrs+ with a A100)

Or do it piecemeal by hand:

3. Create training csv

4. Finetune Model or Download Already Finetuned Models

Download one already finetuned

onedrive

GCloud bucket (faster but costs $$)

finetune your own

5. Run Inference

Inference Results

Resources Created:

FAQ

below this point is the original readme

Introduction

Installation

Inference

Training

ImageNet

Class ID to unique readable names

Citation

Acknowledgement

Contributing

Trademarks

About

Resources

License

Code of conduct

Security policy

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