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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News

• [Jul 24 2024] Added support for STCH (ICML 2024).
• [Feb 08 2024] Added support for DB-MTL.
• [Aug 16 2023]: Added support for MoCo (ICLR 2023). Many thanks to the author's help @heshandevaka.
• [Jul 11 2023] Paper got accepted to JMLR.
• [Jun 19 2023] Added support for Aligned-MTL (CVPR 2023).
• [Mar 10 2023]: Added QM9 and PAWS-X examples.
• [Jul 22 2022]: Added support for Nash-MTL (ICML 2022).
• [Jul 21 2022]: Added support for Learning to Branch (ICML 2020). Many thanks to @yuezhixiong (#14).
• [Mar 29 2022]: Paper is now available on the arXiv.

Table of Content

• Features
• Overall Framework
• Supported Algorithms
• Supported Benchmark Datasets
• Installation
• Quick Start
  • Download Dataset
  • Run a Model
• Citation
• Contributor
• 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 many state-of-the-art MTL methods including 8 architectures and 16 optimization strategies. Meanwhile, LibMTL provides a fair comparison of several benchmark datasets covering different fields.
• 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 optimization strategies and architectures or apply the existing MTL algorithms to new application scenarios with the support of LibMTL.

Overall Framework

Each module is introduced in Docs.

Supported Algorithms

LibMTL currently supports the following algorithms:

Optimization Strategies	Venues	Arguments
Equal Weighting (EW)	-	--weighting EW
Gradient Normalization (GradNorm)	ICML 2018	--weighting GradNorm
Uncertainty Weights (UW)	CVPR 2018	--weighting UW
MGDA (official code)	NeurIPS 2018	--weighting MGDA
Dynamic Weight Average (DWA) (official code)	CVPR 2019	--weighting DWA
Geometric Loss Strategy (GLS)	CVPR 2019 Workshop	--weighting GLS
Projecting Conflicting Gradient (PCGrad)	NeurIPS 2020	--weighting PCGrad
Gradient sign Dropout (GradDrop)	NeurIPS 2020	--weighting GradDrop
Impartial Multi-Task Learning (IMTL)	ICLR 2021	--weighting IMTL
Gradient Vaccine (GradVac)	ICLR 2021	--weighting GradVac
Conflict-Averse Gradient descent (CAGrad) (official code)	NeurIPS 2021	--weighting CAGrad
Nash-MTL (official code)	ICML 2022	--weighting Nash_MTL
Random Loss Weighting (RLW)	TMLR 2022	--weighting RLW
MoCo	ICLR 2023	--weighting MoCo
Aligned-MTL (official code)	CVPR 2023	--weighting Aligned_MTL
STCH (official code)	ICML 2024	--weighting STCH
DB-MTL	arXiv	--weighting DB_MTL

Architectures	Venues	Arguments
Hard Parameter Sharing (HPS)	ICML 1993	--arch HPS
Cross-stitch Networks (Cross_stitch)	CVPR 2016	--arch Cross_stitch
Multi-gate Mixture-of-Experts (MMoE)	KDD 2018	--arch MMoE
Multi-Task Attention Network (MTAN) (official code)	CVPR 2019	--arch MTAN
Customized Gate Control (CGC), Progressive Layered Extraction (PLE)	ACM RecSys 2020	--arch CGC, --arch PLE
Learning to Branch (LTB)	ICML 2020	--arch LTB
DSelect-k (official code)	NeurIPS 2021	--arch DSelect_k

Supported Benchmark Datasets

Datasets	Problems	Task Number	Tasks	multi-input	Supported Backbone
NYUv2	Scene Understanding	3	Semantic Segmentation+ Depth Estimation+ Surface Normal Prediction	✘	ResNet50/ SegNet
Office-31	Image Recognition	3	Classification	✓	ResNet18
Office-Home	Image Recognition	4	Classification	✓	ResNet18
QM9	Molecular Property Prediction	11 (default)	Regression	✘	GNN
PAWS-X	Paraphrase Identification	4 (default)	Classification	✓	Bert

Installation

1. Create a virtual environment

   conda create -n libmtl python=3.8
   conda activate libmtl
   pip install torch==1.8.1+cu111 torchvision==0.9.1+cu111 -f https://download.pytorch.org/whl/torch_stable.html

2. Clone the repository

   git clone https://github.com/median-research-group/LibMTL.git

3. Install LibMTL

   cd LibMTL
   pip install -r requirements.txt
   pip install -e .

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 main.py is the main file for training on the NYUv2 dataset.

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

python main.py -h

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

python main.py --weighting EW --arch HPS --dataset_path /path/to/nyuv2 --gpu_id 0 --scheduler step --mode train --save_path PATH

More details is represented in Docs.

Citation

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

@article{lin2023libmtl,
  title={{LibMTL}: A {P}ython Library for Multi-Task Learning},
  author={Baijiong Lin and Yu Zhang},
  journal={Journal of Machine Learning Research},
  volume={24},
  number={209},
  pages={1--7},
  year={2023}
}

Contributor

LibMTL is developed and maintained by Baijiong Lin.

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, mtan, MTL, nash-mtl, pytorch_geometric, and xtreme.

License

LibMTL is released under the MIT license.