Unified-EPT

Code for the ICCV 2021 Workshop paper: A Unified Efficient Pyramid Transformer for Semantic Segmentation.

Installation

  • Linux, CUDA>=10.0, GCC>=5.4
  • Python>=3.7
  • Create a conda environment:

    conda create -n unept python=3.7 pip

Then, activate the environment:

    conda activate unept
  • PyTorch>=1.5.1, torchvision>=0.6.1 (following instructions here)

For example:

conda install pytorch==1.5.1 torchvision==0.6.1 cudatoolkit=10.2 -c pytorch


pip install -r requirements.txt

Data Preparation

Please following the code from openseg to generate ground truth for boundary refinement.

The data format should be like this.

ADE20k

You can download the processed dt_offset file here.

path/to/ADEChallengeData2016/
  images/
    training/
    validation/
  annotations/ 
    training/
    validation/
  dt_offset/
    training/
    validation/

PASCAL-Context

You can download the processed dataset here.

path/to/PASCAL-Context/
  train/
    image/
    label/
    dt_offset/
  val/
    image/
    label/
    dt_offset/

Usage

Training

The default is for multi-gpu, DistributedDataParallel training.

python -m torch.distributed.launch --nproc_per_node=8 \ # specify gpu number
--master_port=29500  \
train.py  --launcher pytorch \
--config /path/to/config_file
  • specify the data_root in the config file;
  • log dir will be created in ./work_dirs;
  • download the DeiT pretrained model and specify the pretrained path in the config file.

Evaluation

# single-gpu testing
python test.py --checkpoint /path/to/checkpoint \
--config /path/to/config_file \
--eval mIoU \
[--out ${RESULT_FILE}] [--show] \
--aug-test \ # for multi-scale flip aug

# multi-gpu testing (4 gpus, 1 sample per gpu)
python -m torch.distributed.launch --nproc_per_node=4 --master_port=29500 \
test.py  --launcher pytorch --eval mIoU \
--config_file /path/to/config_file \
--checkpoint /path/to/checkpoint \
--aug-test \ # for multi-scale flip aug

Results

We report results on validation sets.

Backbone Crop Size Batch Size Dataset Lr schd Mem(GB) mIoU(ms+flip) config
Res-50 480×480 16 ADE20K 160K 7.0G 46.1 config
DeiT 480×480 16 ADE20K 160K 8.5G 50.5 config
DeiT 480×480 16 PASCAL-Context 160K 8.5G 55.2 config

Security

See CONTRIBUTING for more information.

License

This project is licensed under the Apache-2.0 License.

Citation

If you use this code and models for your research, please consider citing:

@article{zhu2021unified,
  title={A Unified Efficient Pyramid Transformer for Semantic Segmentation},
  author={Zhu, Fangrui and Zhu, Yi and Zhang, Li and Wu, Chongruo and Fu, Yanwei and Li, Mu},
  journal={arXiv preprint arXiv:2107.14209},
  year={2021}
}

Acknowledgment

We thank the authors and contributors of MMCV, MMSegmentation, and Deformable DETR.

GitHub - amazon-research/unified-ept: A Unified Efficient Pyramid Transformer for Semantic Segmentation, ICCVW 2021
A Unified Efficient Pyramid Transformer for Semantic Segmentation, ICCVW 2021 - GitHub - amazon-research/unified-ept: A Unified Efficient Pyramid Transformer for Semantic Segmentation, ICCVW 2021
GitHubamazon-research