Awesome

Seg_Uncertainty

Zhedong Zheng, Yi Yang

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

• MRNet：Unsupervised Scene Adaptation with Memory Regularization in vivo, IJCAI (2020)

• MRNet+Rectifying: Rectifying Pseudo Label Learning via Uncertainty Estimation for Domain Adaptive Semantic Segmentation, IJCV (2021) [中文介绍] [Poster]

• [中文介绍视频]

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

• CommonQ&A
• The Core Code
• Prerequisites
• Prepare Data
• Training
• Testing
• Trained Model
• Related Works
• Citation

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.

2. 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.

• Memory in vivo: https://github.com/layumi/Seg_Uncertainty/blob/master/trainer_ms.py#L232

• Recitfying Pseudo label: https://github.com/layumi/Seg_Uncertainty/blob/master/trainer_ms_variance.py#L166

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].

• Download The GTA5 Dataset

• Download The SYNTHIA Dataset SYNTHIA-RAND-CITYSCAPES (CVPR16)

• Download The Cityscapes Dataset

• Download The Oxford RobotCar Dataset

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:

• https://github.com/wasidennis/AdaptSegNet
• https://github.com/RoyalVane/CLAN
• https://github.com/yzou2/CRST

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}
}