LibMTL

LibMTL is an open-source library built on PyTorch for Multi-Task Learning (MTL). See the latest documentation for detailed introductions and API instructions.

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Table of Content

• Features
• Overall Framework
• Supported Algorithms
• Installation
• Quick Start
  • Download Dataset
  • Run a Model
• Citation
• Contributors
• Contact Us
• Acknowledgements
• License

Features

• Unified: LibMTL provides a unified code base to implement and a consistent evaluation procedure including data processing, metric objectives, and hyper-parameters on several representative MTL benchmark datasets, which allows quantitative, fair, and consistent comparisons between different MTL algorithms.
• Comprehensive: LibMTL supports 84 MTL models combined by 7 architectures and 12 loss weighting strategies. Meanwhile, LibMTL provides a fair comparison on 3 computer vision datasets.
• Extensible: LibMTL follows the modular design principles, which allows users to flexibly and conveniently add customized components or make personalized modifications. Therefore, users can easily and fast develop novel loss weighting strategies and architectures or apply the existing MTL algorithms to new application scenarios with the support of LibMTL.

Overall Framework

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• Config Module: Responsible for all the configuration parameters involved in the running framework, including the parameters of optimizer and learning rate scheduler, the hyper-parameters of MTL model, training configuration like batch size, total epoch, random seed and so on.
• Dataloaders Module: Responsible for data pre-processing and loading.
• Model Module: Responsible for inheriting classes architecture and weighting and instantiating a MTL model. Note that the architecture and the weighting strategy determine the forward and backward processes of the MTL model, respectively.
• Losses Module: Responsible for computing the loss for each task.
• Metrics Module: Responsible for evaluating the MTL model and calculating the metric scores for each task.

Supported Algorithms

LibMTL currently supports the following algorithms:

• 12 loss weighting strategies.

• 7 architectures.

• 84 combinations of different architectures and loss weighting strategies.

Installation

The simplest way to install LibMTL is using pip.

pip install -U LibMTL

More details about environment configuration is represented in Docs.

Quick Start

We use the NYUv2 dataset as an example to show how to use LibMTL.

Download Dataset

The NYUv2 dataset we used is pre-processed by mtan. You can download this dataset here.

Run a Model

The complete training code for the NYUv2 dataset is provided in examples/nyu. The file train_nyu.py is the main file for training on the NYUv2 dataset.

You can find the command-line arguments by running the following command.

python train_nyu.py -h

For instance, running the following command will train a MTL model with EW and HPS on NYUv2 dataset.

python train_nyu.py --weighting EW --arch HPS --dataset_path /path/to/nyuv2 --gpu_id 0 --scheduler step

More details is represented in Docs.

Citation

If you find LibMTL useful for your research or development, please cite the following:

@misc{LibMTL,
 author = {Baijiong Lin and Yu Zhang},
 title = {LibMTL: A PyTorch Library for Multi-Task Learning},
 year = {2021},
 publisher = {GitHub},
 journal = {GitHub repository},
 howpublished = {\url{https://github.com/median-research-group/LibMTL}}
}

Contributors

LibMTL is developed and maintained by Baijiong Lin and Yu Zhang.

Contact Us

If you have any question or suggestion, please feel free to contact us by raising an issue or sending an email to [email protected].

Acknowledgements

We would like to thank the authors that release the public repositories (listed alphabetically): CAGrad, dselect_k_moe, MultiObjectiveOptimization, and mtan.

License

LibMTL is released under the MIT license.