DAMEX: Dataset-aware Mixture-of-Experts for visual understanding of mixture-of-datasets

Paper | Data

PyTorch implementation of DAMEX, a dataset-aware Mixture-of-Experts architecture that learns dataset-specific experts inside a single architecture

DAMEX: Dataset-aware Mixture-of-Experts for visual understanding of mixture-of-datasets
Yash Jain¹, Harkirat Behl². Zsolt Kira¹, Vibhav Vineet²,
¹Georgia Institute of Technlogy, ²Microsoft Research

Quickstart 🚀

Follow the instructions below to train DAMEX on the UODB datasets. The codebase assumes one expert per gpu but this can be tweaked in tutel library and models/dino/moe.py.

Set up a conda environment:

conda env create -n damex
conda activate damex
pip install -r requirements.txt


# download local copy of tutel
python3 -m pip install --user --upgrade git+https://github.com/microsoft/tutel@v0.2.1

# setup dino
cd models/dino/ops
python setup.py build install --user
cd ../../..
pip uninstall protobuf -y
pip install protobuf==3.20.3

Set up data

All the datasets need to be in COCO format. Download the dataset from the UODB website Universal Object-detection Benchmark and place the annotation json downloaded from drive link inside the Annotations folder for non-COCO datasets. The directory structure should be like

  uodb/
    clipart/
      Annotations/
      JPEGImages/
      ...
    comic/
      Annotations/
      JPEGImages/
      ...
    coco/
      ...
    ...

Training on UODB set:

python -m torch.distributed.launch --nproc_per_node=8 main.py \
      --config_file config/DINO/uodb/DAMEX_4scale.py \
      --damex \
      --options batch_size=1 save_checkpoint_interval=4 epochs=36 lr=0.00014 \
      --output_dir ./output/uodb/expt \
      --coco_path $$DATA_DIR/uodb/ \
      --datasets clipart coco deeplesion dota kitchen lisa watercolor widerface kitti voc07 voc12 comic  \ # name of datasets

# Optionally, you can use wandb to monitor experiments, need WANDB_API_KEY in main.py
# --use_wandb --run_group expt

The dataset mapping of DAMEX is currently hard-coded in datasets/dataset.py file as dataset_map. The comments and examples will help you understand the mapping. If you find a more amicable solution to it then please feel free to raise a PR.

BibTeX

@article{jain2024damex,
  title={DAMEX: Dataset-aware Mixture-of-Experts for visual understanding of mixture-of-datasets},
  author={Jain, Yash and Behl, Harkirat and Kira, Zsolt and Vineet, Vibhav},
  journal={Advances in Neural Information Processing Systems},
  volume={36},
  year={2024}
}

Comments

Thank you for working with DAMEX. 😄

jinga-lala/DAMEX