README.md

Multi-Task Learning Quantization: Evaluating Impact of Task-Specific Quantization on Overall Performance

Introduction

This is a forked directory of the official implementation of the paper: E-MTL: Efficient Multi-task Learning Architecture using Hybrid Transformer and ConvNet blocks. This repository explores the effect of quantization on different modules of an Efficient Multi-Task Learning Model.

How to Run

If you'd like to run the standalone E-MTL model, checkout EMTL Repo.

An example run of a specific quantization configuration:

python -m torch.distributed.launch --nproc_per_node 1 --master_port $1 main.py --cfg configs/swin/swin_tiny_patch4_window7_224.yaml --pascal ../../data/shared/AdaMTL/data/PASCAL_MT --tasks $5 --batch-size 96 --ckpt-freq=100 --epoch=400 --eval-freq 100 --resume-backbone pretrained/swin_tiny_patch4_window7_224.pth --name $2/ --wieb $3 --widbpt $4 --output ../../data/shared/QEMTL/

Relevant Arguments:

Argument	Example	Description
--nproc_per_node	1	number of GPUs
--cfg	configs/swin/swin_tiny_patch4_window7_224.yaml	swin backend configuration
--tasks	semseg,normals,sal,human_parts	tasks performed by the MTL
--resume-backbone	pretrained/swin_tiny_patch4_window7_224.pth	loading init weights
--wieb	6-8	weights, inputs encoder bits for quantization
--widbpt	8-8,8-8,8-8,8-8	decoder-specific in-order weights, inputs quantized bits

Authorship

Current Authors of the project:

• Mahdi Boulila
• Marina Neseem

Citation

If you find E-MTL helpful in your research, please cite our paper:

@inproceedings{boulila2024qmtl,
  title={E-MTL: Efficient Multi-task Learning Architecture using Hybrid Transformer and ConvNet blocks},
  author={Boulila, Mahdi and Neseem, Marina and Reda, Sherief},
  booktitle={},
  pages={},
  year={2024}
}

License

MIT License. See LICENSE file