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Seg_Uncertainty

Python 3.6 License: MIT

Zhedong Zheng, Yi Yang

In this repo, we provide the code for the two papers, i.e.,

Initial Model

The original DeepLab link of ucmerced is failed. Please use the following link.

[Google Drive] https://drive.google.com/file/d/1BMTTMCNkV98pjZh_rU0Pp47zeVqF3MEc/view?usp=share_link

[One Drive] https://1drv.ms/u/s!Avx-MJllNj5b3SqR7yurCxTgIUOK?e=A1dq3m

or use

pip install gdown
pip install --upgrade gdown
gdown 1BMTTMCNkV98pjZh_rU0Pp47zeVqF3MEc

Table of contents

News

  • [19 Jan 2024] We further apply the uncertainty to compositional image retrieval. The paper is accepted by ICLR'24 [code].

  • [27 Jan 2023] You are welcomed to check our new transformer-based work PiPa, which achieves 75.6 mIoU on GTA5->Cityscapes.

  • [5 Sep 2021] Zheng etal. apply the Uncertainty to domain adaptive reid, and also achieve good performance. "Exploiting Sample Uncertainty for Domain Adaptive Person Re-Identification" Kecheng Zheng, Cuiling Lan, Wenjun Zeng, Zhizheng Zhang, and Zheng-Jun Zha. AAAI 2021

  • [13 Aug 2021] We release one new method by Adaptive Boosting (AdaBoost) for Domain Adaptation. You may check the project at https://github.com/layumi/AdaBoost_Seg

Common Q&A

  1. Why KLDivergence is always non-negative (>=0)?

Please check the wikipedia at (https://en.wikipedia.org/wiki/Kullback–Leibler_divergence#Properties) . It provides one good demonstration.

  1. Why both log_sm and sm are used?

You may check the pytorch doc at https://pytorch.org/docs/stable/generated/torch.nn.KLDivLoss.html?highlight=nn%20kldivloss#torch.nn.KLDivLoss. I follow the discussion at https://discuss.pytorch.org/t/kl-divergence-loss/65393

The Core Code

Core code is relatively simple, and could be directly applied to other works.

Prerequisites

  • Python 3.6
  • GPU Memory >= 11G (e.g., GTX2080Ti or GTX1080Ti)
  • Pytorch or Paddlepaddle

Prepare Data

Download [GTA5] and [Cityscapes] to run the basic code. Alternatively, you could download extra two datasets from [SYNTHIA] and [OxfordRobotCar].

The data folder is structured as follows:

├── data/
│   ├── Cityscapes/  
|   |   ├── data/
|   |       ├── gtFine/
|   |       ├── leftImg8bit/
│   ├── GTA5/
|   |   ├── images/
|   |   ├── labels/
|   |   ├── ...
│   ├── synthia/ 
|   |   ├── RGB/
|   |   ├── GT/
|   |   ├── Depth/
|   |   ├── ...
│   └── Oxford_Robot_ICCV19
|   |   ├── train/
|   |   ├── ...

Training

Stage-I:

python train_ms.py --snapshot-dir ./snapshots/SE_GN_batchsize2_1024x512_pp_ms_me0_classbalance7_kl0.1_lr2_drop0.1_seg0.5  --drop 0.1 --warm-up 5000 --batch-size 2 --learning-rate 2e-4 --crop-size 1024,512 --lambda-seg 0.5  --lambda-adv-target1 0.0002 --lambda-adv-target2 0.001   --lambda-me-target 0  --lambda-kl-target 0.1  --norm-style gn  --class-balance  --only-hard-label 80  --max-value 7  --gpu-ids 0,1  --often-balance  --use-se

Generate Pseudo Label:

python generate_plabel_cityscapes.py  --restore-from ./snapshots/SE_GN_batchsize2_1024x512_pp_ms_me0_classbalance7_kl0.1_lr2_drop0.1_seg0.5/GTA5_25000.pth

Stage-II (with recitfying pseudo label):

python train_ft.py --snapshot-dir ./snapshots/1280x640_restore_ft_GN_batchsize9_512x256_pp_ms_me0_classbalance7_kl0_lr1_drop0.2_seg0.5_BN_80_255_0.8_Noaug --restore-from ./snapshots/SE_GN_batchsize2_1024x512_pp_ms_me0_classbalance7_kl0.1_lr2_drop0.1_seg0.5/GTA5_25000.pth --drop 0.2 --warm-up 5000 --batch-size 9 --learning-rate 1e-4 --crop-size 512,256 --lambda-seg 0.5 --lambda-adv-target1 0 --lambda-adv-target2 0 --lambda-me-target 0 --lambda-kl-target 0 --norm-style gn --class-balance --only-hard-label 80 --max-value 7 --gpu-ids 0,1,2 --often-balance  --use-se  --input-size 1280,640  --train_bn  --autoaug False

*** If you want to run the code without rectifying pseudo label, please change [this line] to 'from trainer_ms import AD_Trainer', which would apply the conventional pseudo label learning. ***

Testing

python evaluate_cityscapes.py --restore-from ./snapshots/1280x640_restore_ft_GN_batchsize9_512x256_pp_ms_me0_classbalance7_kl0_lr1_drop0.2_seg0.5_BN_80_255_0.8_Noaug/GTA5_25000.pth

Trained Model

The trained model is available at https://drive.google.com/file/d/1smh1sbOutJwhrfK8dk-tNvonc0HLaSsw/view?usp=sharing

  • The folder with SY in name is for SYNTHIA-to-Cityscapes
  • The folder with RB in name is for Cityscapes-to-Robot Car

One Note for SYNTHIA-to-Cityscapes

Note that the evaluation code I provided for SYNTHIA-to-Cityscapes is still average the IoU by divide 19. Actually, you need to re-calculate the value by divide 16. There are only 16 shared classes for SYNTHIA-to-Cityscapes. In this way, the result is same as the value reported in paper.

Related Works

We also would like to thank great works as follows:

Citation

@inproceedings{zheng2020unsupervised,
  title={Unsupervised Scene Adaptation with Memory Regularization in vivo},
  author={Zheng, Zhedong and Yang, Yi},
  booktitle={IJCAI},
  year={2020}
}
@article{zheng2021rectifying,
  title={Rectifying Pseudo Label Learning via Uncertainty Estimation for Domain Adaptive Semantic Segmentation },
  author={Zheng, Zhedong and Yang, Yi},
  journal={International Journal of Computer Vision (IJCV)},
  doi={10.1007/s11263-020-01395-y},
  note={\mbox{doi}:\url{10.1007/s11263-020-01395-y}},
  year={2021}
}