Lawin Transformer: Improving Semantic Segmentation Transformer with Multi-Scale Representations via Large Window Attention (Under Review).
For install and data preparation, please refer to the guidelines in MMSegmentation v0.13.0.
Important requirements: CUDA 11.6 and pytorch 1.8.1
pip install torchvision==0.9.1
pip install timm==0.3.2
pip install mmcv-full==1.2.7
pip install opencv-python==4.5.1.48
pip install einops
cd lawin && pip install -e . --user
Download trained models.
# Single-gpu testing
python tools/test.py local_configs/segformer/B2/lawin.b2.512x512.ade.160k.py /path/to/checkpoint_file
Download weights pretrained on ImageNet-1K, and put them in a folder pretrained/.
Example: train lawin-B2 on ADE20K:
# Multi-gpu training
./tools/dist_train.sh local_configs/segformer/B2/lawin.b2.512x512.ade.160k.py <GPU_NUM> --work-dir <WORK_DIRS_TO_SAVE_WEIGHTS&LOGS> --options evaluation.interval=320000
@article{yan2022lawin,
title={Lawin transformer: Improving semantic segmentation transformer with multi-scale representations via large window attention},
author={Yan, Haotian and Zhang, Chuang and Wu, Ming},
journal={arXiv preprint arXiv:2201.01615},
year={2022}
}