SEgmentation TRansformers -- SETR

SETR

Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers, Sixiao Zheng, Jiachen Lu, Hengshuang Zhao, Xiatian Zhu, Zekun Luo, Yabiao Wang, Yanwei Fu, Jianfeng Feng, Tao Xiang, Philip HS Torr, Li Zhang, CVPR 2021

Vision Transformers: From Semantic Segmentation to Dense Prediction, Li Zhang, Jiachen Lu, Sixiao Zheng, Xinxuan Zhao, Xiatian Zhu, Yanwei Fu, Tao Xiang, Jianfeng Feng

SETR

Cityscapes

ADE20K

Pascal Context

HLG

ImageNet-1K

HLG classification is under folder hlg-classification/.

Cityscapes

HLG segmentation shares the same folder as SETR.

ADE20K

HLG segmentation shares the same folder as SETR.

COCO

HLG detection is under folder hlg-detection/.

Installation

Our project is developed based on MMsegmentation. Please follow the official MMsegmentation INSTALL.md and getting_started.md for installation and dataset preparation.

🔥🔥 SETR is on MMsegmentation. 🔥🔥

A from-scratch setup script

Linux

Here is a full script for setting up SETR with conda and link the dataset path (supposing that your dataset path is $DATA_ROOT).

conda create -n open-mmlab python=3.7 -y
conda activate open-mmlab

conda install pytorch=1.6.0 torchvision cudatoolkit=10.1 -c pytorch -y
pip install mmcv-full==1.2.2 -f https://download.openmmlab.com/mmcv/dist/cu101/torch1.6.0/index.html
git clone https://github.com/fudan-zvg/SETR.git
cd SETR
pip install -e .  # or "python setup.py develop"
pip install -r requirements/optional.txt

mkdir data
ln -s $DATA_ROOT data

Windows(Experimental)

Here is a full script for setting up SETR with conda and link the dataset path (supposing that your dataset path is %DATA_ROOT%. Notice: It must be an absolute path).

conda create -n open-mmlab python=3.7 -y
conda activate open-mmlab

conda install pytorch=1.6.0 torchvision cudatoolkit=10.1 -c pytorch
set PATH=full\path\to\your\cpp\compiler;%PATH%
pip install mmcv

git clone https://github.com/fudan-zvg/SETR.git
cd SETR
pip install -e .  # or "python setup.py develop"
pip install -r requirements/optional.txt

mklink /D data %DATA_ROOT%

Get Started

Pre-trained model

The pre-trained model will be automatically downloaded and placed in a suitable location when you run the training command. If you are unable to download due to network reasons, you can download the pre-trained model from here (ViT) and here (DeiT).

Train

./tools/dist_train.sh ${CONFIG_FILE} ${GPU_NUM} 
# For example, train a SETR-PUP on Cityscapes dataset with 8 GPUs
./tools/dist_train.sh configs/SETR/SETR_PUP_768x768_40k_cityscapes_bs_8.py 8

• Tensorboard

  If you want to use tensorboard, you need to pip install tensorboard and uncomment the Line 6 dict(type='TensorboardLoggerHook') of SETR/configs/_base_/default_runtime.py.

Single-scale testing

./tools/dist_test.sh ${CONFIG_FILE} ${CHECKPOINT_FILE} ${GPU_NUM}  [--eval ${EVAL_METRICS}]
# For example, test a SETR-PUP on Cityscapes dataset with 8 GPUs
./tools/dist_test.sh configs/SETR/SETR_PUP_768x768_40k_cityscapes_bs_8.py \
work_dirs/SETR_PUP_768x768_40k_cityscapes_bs_8/iter_40000.pth \
8 --eval mIoU

Multi-scale testing

Use the config file ending in _MS.py in configs/SETR.

./tools/dist_test.sh ${CONFIG_FILE} ${CHECKPOINT_FILE} ${GPU_NUM}  [--eval ${EVAL_METRICS}]
# For example, test a SETR-PUP on Cityscapes dataset with 8 GPUs
./tools/dist_test.sh configs/SETR/SETR_PUP_768x768_40k_cityscapes_bs_8_MS.py \
work_dirs/SETR_PUP_768x768_40k_cityscapes_bs_8/iter_40000.pth \
8 --eval mIoU

Generate the png files to be submit to the official evaluation server

• Cityscapes

  First, add following to config file configs/SETR/SETR_PUP_768x768_40k_cityscapes_bs_8.py,

  data = dict(
      test=dict(
          img_dir='leftImg8bit/test',
          ann_dir='gtFine/test'))

  Then run test

  ./tools/dist_test.sh configs/SETR/SETR_PUP_768x768_40k_cityscapes_bs_8.py \
      work_dirs/SETR_PUP_768x768_40k_cityscapes_bs_8/iter_40000.pth \
      8 --format-only --eval-options "imgfile_prefix=./SETR_PUP_768x768_40k_cityscapes_bs_8_test_results"

  You will get png files under directory ./SETR_PUP_768x768_40k_cityscapes_bs_8_test_results. Run zip -r SETR_PUP_768x768_40k_cityscapes_bs_8_test_results.zip SETR_PUP_768x768_40k_cityscapes_bs_8_test_results/ and submit the zip file to evaluation server.

• ADE20k

  ADE20k dataset could be download from this link

  First, add following to config file configs/SETR/SETR_PUP_512x512_160k_ade20k_bs_16.py,

  data = dict(
      test=dict(
          img_dir='images/testing',
          ann_dir='annotations/testing'))

  Then run test

  ./tools/dist_test.sh configs/SETR/SETR_PUP_512x512_160k_ade20k_bs_16.py \
      work_dirs/SETR_PUP_512x512_160k_ade20k_bs_16/iter_1600000.pth \
      8 --format-only --eval-options "imgfile_prefix=./SETR_PUP_512x512_160k_ade20k_bs_16_test_results"

  You will get png files under ./SETR_PUP_512x512_160k_ade20k_bs_16_test_results directory. Run zip -r SETR_PUP_512x512_160k_ade20k_bs_16_test_results.zip SETR_PUP_512x512_160k_ade20k_bs_16_test_results/ and submit the zip file to evaluation server.

Please see getting_started.md for the more basic usage of training and testing.

Reference

@inproceedings{SETR,
    title={Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers}, 
    author={Zheng, Sixiao and Lu, Jiachen and Zhao, Hengshuang and Zhu, Xiatian and Luo, Zekun and Wang, Yabiao and Fu, Yanwei and Feng, Jianfeng and Xiang, Tao and Torr, Philip H.S. and Zhang, Li},
    booktitle={CVPR},
    year={2021}
}

@article{SETR-HLG,
    title={Vision Transformers: From Semantic Segmentation to Dense Prediction}, 
    author={Zhang, Li and Lu, Jiachen and Zheng, Sixia and Zhao, Xinxuan and Zhu, Xiatian and Fu, Yanwei and Xiang Tao and Feng, Jianfeng},
    journal={arXiv},
    year={2023}
}

License

MIT

Acknowledgement

Thanks to previous open-sourced repo: MMsegmentation pytorch-image-models