Awesome
<p align="center"> <br> <img src="images/ofasys_logo.svg" width="250" /> <br> <br> <a href='https://ofasys-doc.readthedocs.io/en/latest/?badge=latest'> <img src='https://readthedocs.org/projects/ofasys-doc/badge/?version=latest' alt='Documentation Status' /> </a> <a href="https://github.com/OFA-Sys/OFASys/blob/main/LICENSE"><img alt="License" src="https://img.shields.io/badge/license-Apache--2.0-blue"/></a> <p align="center">  <a href="https://ofasys-doc.readthedocs.io/en/latest/start/whatis.html">Documentation </a> |  <a href="https://arxiv.org/abs/2212.04408">Paper</a> |  Blog   |  ModelScope   </p> </p>What is OFASys?
<p align="center"> <img src="images/task7.gif" width = "700" alt="" align=center /> </p> OFASys is a multi-modal multi-task learning system designed to make multi-modal tasks declarative, modular and task-scalable. With OFASys, it is easy to:- Rapidly introduce new multi-modal tasks/datasets by defining a declarative one-line instruction.
- Develop new or reuse existing modality-specific components.
- Jointly train multiple multi-modal tasks together without manual processing of multi-modal data collating.
For now, OFASys supports 7 modalities and more than 20 classes of multi-modal tasks, including:
- Text: for tasks like Natural language Understanding, Text Summarization and Text Infilling.
- Image: for tasks like Image Classification, Visual Entailment, Image Captioning, Visual Question Answering, Text-to-Image Generation and Image Infilling.
- Box: for tasks like Visual Grounding, Grounded Caption, Object Detection
- Video: for tasks like Video Classification, Video Captioning and Video Question Answering.
- Audio: for tasks like Automatic Speech Recognition, and Text to Speech.
- Structural Language: for tasks like Text-to-SQL, Table-to-Text, Table question answering, and Sudoku.
- Motion: for tasks like Text-to-Motion.
News
- 2022.12.23 v0.1.0-patch1:
- Refactored and released diffusion-based
Text-to-Motion
task (v0.1), see doc for usage. - Refactored TextPreprocess: BOS and EOS no longer required when writing an instruction.
- Added DatabasePreprocess for the
Text-to-SQL
task.
- Refactored and released diffusion-based
Requirements
- PyTorch version >= 1.8.0
- Python version >= 3.7
- Torchaudio >= 0.8.0
Installation
Install with pip
Through the pip installation, users can experience the basic multi-task training and inference functions of OFASys.
pip install http://ofasys.oss-cn-zhangjiakou.aliyuncs.com/pkg/ofasys-0.1.0-py3-none-any.whl
Test your installation.
python -c "import ofasys"
Using the audio feature in OFASys requires the soundfile library to be installed. In the Ubuntu OS, run the following command:
sudo apt-get update
sudo apt-get install libsndfile1
Install with Source (Optional)
Users can install OFASys from the source code to customize their training tasks and full functions.
git clone https://github.com/OFA-Sys/OFASys.git
cd OFASys
python setup.py develop
Getting Started
The documents contains more instructions for getting started.
Training One Model for All Tasks
Define the Tasks
OFASys can co-train multiple multi-modal tasks flexibly.
from ofasys import Task, Trainer, GeneralistModel
task1 = Task(
name='caption',
instruction='[IMAGE:image_url] what does the image describe? -> [TEXT:caption]',
micro_batch_size=4,
)
task2 = Task(
name='text_infilling',
instruction='what is the complete text of " [TEXT:sentence,mask_ratio=0.3] "? -> [TEXT:sentence]',
micro_batch_size=2,
)
In the simplest scenario, you only need to specify an instruction to define your task and a task name as an identifier.
Set the Dataset
The Task can use a regular Pytorch Dataloader which can be constructed by Huggingface Dataset or a customized Pytorch Dataset.
from datasets import load_dataset
task1.add_dataset(load_dataset('TheFusion21/PokemonCards')['train'], 'train')
task2.add_dataset(load_dataset('glue', 'cola')['train'], 'train')
Create a Generalist Model and Train All Tasks Together
The GeneralistModel of OFASys (OFA+) is capable of handling multiple modalities including: TEXT, IMAGE, AUDIO, VIDEO, MOTION, BOX, PHONE.
The OFASys Trainer “mixes” multiple Tasks with any dataset and abstracts away all the engineering complexity needed for scale.
model = GeneralistModel()
trainer = Trainer()
trainer.fit(model=model, tasks=[task1, task2])
The complete script is available at scripts/trainer_api.py.
Infer Multiple Multi-modal Tasks with One Checkpoint
OFASys can infer multiple multi-modal tasks using just One checkpoint.
from ofasys import OFASys
model = OFASys.from_pretrained('multitask.pt')
OFASys enables multi-task multi-modal inference through the instruction alone. The multitask checkpoint can be download at here. Let's go through a couple of examples!
Image Captioning
<img src="https://ofasys.oss-cn-zhangjiakou.aliyuncs.com/data/coco/2014/val2014/COCO_val2014_000000222628.jpg" width="400">instruction = '[IMAGE:img] what does the image describe? -> [TEXT:cap]'
data = {'img': "./COCO_val2014_000000222628.jpg"}
output = model.inference(instruction, data=data)
print(output.text)
# "a man and woman sitting in front of a laptop computer"
Visual Grounding
<img src="https://www.2008php.com/2014_Website_appreciate/2015-06-22/20150622131649.jpg" width="400">instruction = '[IMAGE:img] which region does the text " [TEXT:cap] " describe? -> [BOX:patch_boxes]'
data = {'img': "https://www.2008php.com/2014_Website_appreciate/2015-06-22/20150622131649.jpg", "cap": "hand"}
output = model.inference(instruction, data=data)
output.save_box("output.jpg")
<img src="http://ofasys.oss-cn-zhangjiakou.aliyuncs.com/examples/inference_caption_0.jpg" width="400">
Text Summarization
instruction = 'what is the summary of article " [TEXT:src] "? -> [TEXT:tgt]'
data = {'src': "poland 's main opposition party tuesday endorsed president lech walesa in an upcoming "
"presidential run-off election after a reformed communist won the first round of voting ."}
output = model.inference(instruction, data=data)
print(output.text)
# "polish opposition endorses walesa in presidential run-off"
Table-to-Text Generation
instruction = 'structured knowledge: " [STRUCT:database,uncased] " . how to describe the tripleset ? -> [TEXT:tgt]'
data = {
'database': [['Atlanta', 'OFFICIAL_POPULATION', '5,457,831'],
['[TABLECONTEXT]', 'METROPOLITAN_AREA', 'Atlanta'],
['5,457,831', 'YEAR', '2012'],
['[TABLECONTEXT]', '[TITLE]', 'List of metropolitan areas by population'],
['Atlanta', 'COUNTRY', 'United States'],
]
}
output = model.inference(instruction, data=data, beam_size=1)
print(output.text)
# "atlanta, united states has a population of 5,457,831 in 2012."
Text-to-SQL Generation
instruction = ' " [TEXT:src] " ; structured knowledge: " [STRUCT:database,max_length=876] " . generating sql code. -> [TEXT:tgt]'
database = [
['concert_singer'],
['stadium', 'stadium_id , location , name , capacity , highest , lowest , average'],
['singer', 'singer_id , name , country , song_name , song_release_year , age , is_male'],
['concert', 'concert_id , concert_name , theme , stadium_id , year'],
['singer_in_concert', 'concert_id , singer_id']
]
data = [
{'src': 'What are the names, countries, and ages for every singer in descending order of age?', 'database': database},
{'src': 'What are all distinct countries where singers above age 20 are from?', 'database': database},
{'src': 'Show the name and the release year of the song by the youngest singer.', 'database': database}
]
output = model.inference(instruction, data=data)
print('\n'.join([o.text for o in output]))
# "select name, country, age from singer order by age desc"
# "select distinct country from singer where age > 20"
# "select song_name, song_release_year from singer order by age limit 1"
Video Captioning
<img src="https://ofasys.oss-cn-zhangjiakou.aliyuncs.com/examples/video.png" width="400">instruction = '[VIDEO:video] what does the video describe? -> [TEXT:cap]'
data = {'video': './video7021.mp4'}
output = model.inference(instruction, data=data)
print(output.text)
# "a baseball player is hitting a ball"
Speech-to-Text Generation
<audio controls="controls"> <source src="http://ofasys.oss-cn-zhangjiakou.aliyuncs.com/data/librispeech/dev-clean/1272/128104/1272-128104-0001.flac" type="audio/wav"> Your browser does not support the <code>audio</code> element. </audio>instruction = '[AUDIO:wav] what is the text corresponding to the voice? -> [TEXT:text,preprocess=text_phone]'
data = {'wav': './1272-128104-0001.flac'}
output = model.inference(instruction, data=data)
print(output.text)
# "nor is mister klohs manner less interesting than his manner"
Text-to-Image Generation
instruction = 'what is the complete image? caption: [TEXT:text]"? -> [IMAGE,preprocess=image_vqgan,adaptor=image_vqgan]'
data = {'text': "a city with tall buildings and a large green park."}
output = model.inference(instruction, data=data)
output[0].save_image('0.png')
<img src="https://ofasys.oss-cn-zhangjiakou.aliyuncs.com/examples/image-gen_example.png" width="400">
Text-to-Motion Generation
model = OFASys.from_pretrained('single_task_motion.pt')
instruction = 'motion capture: [TEXT:text] -> [MOTION:bvh_frames,preprocess=motion_6d,adaptor=motion_6d]'
guided_prompts = [
{'text': 'run then jump'}, # # The positive prompt.
{'text': ''}, # The negative prompt, or an empty string for classifier-free guidance.
]
# This API requires the positive and negative prompts be in the same batch, so please ensure batch_size % 2 == 0.
output = model.inference(instruction, data=guided_prompts, guidance_weight=3.0, batch_size=2)
output[0].save_as_gif('run_then_jump__guided.gif')
<img src="https://ofasys.oss-cn-zhangjiakou.aliyuncs.com/examples/run_then_jump_guided.gif" width="400">
The checkpoint of the single motion task and more motion cases can be found at here.
Learn More
Section | Description |
---|---|
Documentation | Full API documentation and tutorials |
Quick tour | Usage in 15 minutes, including training and inference |
How to define a task | How to define a task using the instruction |
Task summary | Tasks supported by OFASys |
Getting Involved
Feel free to submit Github issues or pull requests. Welcome to contribute to our project!
To contact us, never hestitate to send an email to jinze.bjz@alibaba-inc.com
or menrui.mr@alibaba-inc.com
!
<br></br>
Citation
Please cite our paper if you find it helpful :)
@article{bai2022ofasys,
author = {
Jinze Bai and
Rui Men and
Hao Yang and
Xuancheng Ren and
Kai Dang and
Yichang Zhang and
Xiaohuan Zhou and
Peng Wang and
Sinan Tan and
An Yang and
Zeyu Cui and
Yu Han and
Shuai Bai and
Wenbin Ge and
Jianxin Ma and
Junyang Lin and
Jingren Zhou and
Chang Zhou},
title = {OFASys: A Multi-Modal Multi-Task Learning System for Building Generalist Models},
journal = {CoRR},
volume = {abs/2212.04408},
year = {2022}
}