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
Our project is developed based on MMsegmentation. Please follow the official MMsegmentation INSTALL.md and getting_started.md for installation and dataset preparation.
๐ฅ๐ฅ SETR is on MMsegmentation. ๐ฅ๐ฅ
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
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%
Method | Crop Size | Batch size | iteration | set | mIoU | model | config |
---|---|---|---|---|---|---|---|
SETR-Naive | 768x768 | 8 | 40k | val | 77.36 | google drive | config |
SETR-Naive | 768x768 | 8 | 80k | val | 77.90 | google drive | config |
SETR-MLA | 768x768 | 8 | 40k | val | 76.65 | google drive | config |
SETR-MLA | 768x768 | 8 | 80k | val | 77.24 | google drive | config |
SETR-PUP | 768x768 | 8 | 40k | val | 78.39 | google drive | config |
SETR-PUP | 768x768 | 8 | 80k | val | 79.34 | google drive | config |
SETR-Naive-DeiT | 768x768 | 8 | 40k | val | 77.85 | config | |
SETR-Naive-DeiT | 768x768 | 8 | 80k | val | 78.66 | config | |
SETR-MLA-DeiT | 768x768 | 8 | 40k | val | 78.04 | config | |
SETR-MLA-DeiT | 768x768 | 8 | 80k | val | 78.98 | config | |
SETR-PUP-DeiT | 768x768 | 8 | 40k | val | 78.79 | config | |
SETR-PUP-DeiT | 768x768 | 8 | 80k | val | 79.45 | config |
Method | Crop Size | Batch size | iteration | set | mIoU | mIoU(ms+flip) | model | Config |
---|---|---|---|---|---|---|---|---|
SETR-Naive | 512x512 | 16 | 160k | Val | 48.06 | 48.80 | google drive | config |
SETR-MLA | 512x512 | 8 | 160k | val | 47.79 | 50.03 | google drive | config |
SETR-MLA | 512x512 | 16 | 160k | val | 48.64 | 50.28 | config | |
SETR-MLA-Deit | 512x512 | 16 | 160k | val | 46.15 | 47.71 | config | |
SETR-PUP | 512x512 | 16 | 160k | val | 48.62 | 50.09 | google drive | config |
SETR-PUP-Deit | 512x512 | 16 | 160k | val | 46.34 | 47.30 | config |
Method | Crop Size | Batch size | iteration | set | mIoU | mIoU(ms+flip) | model | Config |
---|---|---|---|---|---|---|---|---|
SETR-Naive | 480x480 | 16 | 80k | val | 52.89 | 53.61 | google drive | config |
SETR-MLA | 480x480 | 8 | 80k | val | 54.39 | 55.39 | google drive | config |
SETR-MLA | 480x480 | 16 | 80k | val | 55.01 | 55.83 | google drive | config |
SETR-MLA-DeiT | 480x480 | 16 | 80k | val | 52.91 | 53.74 | config | |
SETR-PUP | 480x480 | 16 | 80k | val | 54.37 | 55.27 | google drive | config |
SETR-PUP-DeiT | 480x480 | 16 | 80k | val | 52.00 | 52.50 | config |
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).
./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 6dict(type='TensorboardLoggerHook')
ofSETR/configs/_base_/default_runtime.py
.
./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
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
-
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
. Runzip -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. Runzip -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.
@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}
}
MIT
Thanks to previous open-sourced repo:
MMsegmentation
pytorch-image-models