Prototypical Pseudo Label Denoising and Target Structure Learning for Domain Adaptive Semantic Segmentation (CVPR 2021)
Pan Zhang, Bo Zhang, Ting Zhang, Dong Chen, Yong Wang, and Fang Wen.
Self-training is a competitive approach in domain adaptive segmentation, which trains the network with the pseudo labels on the target domain. However inevitably, the pseudo labels are noisy and the target features are dispersed due to the discrepancy between source and target domains. In this paper, we rely on representative prototypes, the feature centroids of classes, to address the two issues for unsupervised domain adaptation. In particular, we take one step further and exploit the feature distances from prototypes that provide richer information than mere prototypes. Specifically, we use it to estimate the likelihood of pseudo labels to facilitate online correction in the course of training. Meanwhile, we align the prototypical assignments based on relative feature distances for two different views of the same target, producing a more compact target feature space. Moreover, we find that distilling the already learned knowledge to a self-supervised pretrained model further boosts the performance. Our method shows tremendous performance advantage over state-of-the-art methods.
Install dependencies:
pip install -r requirements.txt
Download Cityscapes, GTA5 and SYNTHIA-RAND-CITYSCAPES.
Download the pretrained model (57.5 mIoU) and save it in ./pretrained/gta2citylabv2_stage3
. Then run the command
python test.py --bn_clr --student_init simclr --resume ./pretrained/gta2citylabv2_stage3/from_gta5_to_cityscapes_on_deeplabv2_best_model.pkl
Download the pretrained model (55.5 mIoU, 62.0 mIoU for 16, 13 categories respectively) and save it in ./pretrained/syn2citylabv2_stage3
. Then run the command
python test.py --bn_clr --student_init simclr --n_class 16 --resume ./pretrained/syn2citylabv2_stage3/from_synthia_to_cityscapes_on_deeplabv2_best_model.pkl
To reproduce the performance, you need 4 GPUs with no less than 16G memory.
Stage1. Download warm-up model (43.3 mIoU), and save it in ./pretrained/gta2citylabv2_warmup/
.
python generate_pseudo_label.py --name gta2citylabv2_warmup_soft --soft --resume_path ./pretrained/gta2citylabv2_warmup/from_gta5_to_cityscapes_on_deeplabv2_best_model.pkl --no_droplast
python calc_prototype.py --resume_path ./pretrained/gta2citylabv2_warmup/from_gta5_to_cityscapes_on_deeplabv2_best_model.pkl
python train.py --name gta2citylabv2_stage1Denoise --used_save_pseudo --ema --proto_rectify --moving_prototype --path_soft Pseudo/gta2citylabv2_warmup_soft --resume_path ./pretrained/gta2citylabv2_warmup/from_gta5_to_cityscapes_on_deeplabv2_best_model.pkl --proto_consistW 10 --rce --regular_w 0.1
Stage2. This stage needs well-trained model from stage1 as teacher model. You can get it by above command or download the pretrained model stage1 model(53.7 mIoU) and save it in ./pretrained/gta2citylabv2_stage1Denoise/
(path of resume_path
). Besides, download the pretrained model simclr model and save it to ./pretrained/simclr/
.
python generate_pseudo_label.py --name gta2citylabv2_stage1Denoise --flip --resume_path ./logs/gta2citylabv2_stage1Denoise/from_gta5_to_cityscapes_on_deeplabv2_best_model.pkl --no_droplast
python train.py --name gta2citylabv2_stage2 --stage stage2 --used_save_pseudo --path_LP Pseudo/gta2citylabv2_stage1Denoise --resume_path ./logs/gta2citylabv2_stage1Denoise/from_gta5_to_cityscapes_on_deeplabv2_best_model.pkl --S_pseudo 1 --threshold 0.95 --distillation 1 --finetune --lr 6e-4 --student_init simclr --bn_clr --no_resume
Stage3. This stage needs well-trained model from stage2 as the teacher model. You can get it with the above command or download the pretrained model stage2 model(56.9 mIoU) and save it in ./pretrained/gta2citylabv2_stage2/
(path of resume_path
).
python generate_pseudo_label.py --name gta2citylabv2_stage2 --flip --resume_path ./logs/gta2citylabv2_stage2/from_gta5_to_cityscapes_on_deeplabv2_best_model.pkl --no_droplast --bn_clr --student_init simclr
python train.py --name gta2citylabv2_stage3 --stage stage3 --used_save_pseudo --path_LP Pseudo/gta2citylabv2_stage2 --resume_path ./logs/gta2citylabv2_stage2/from_gta5_to_cityscapes_on_deeplabv2_best_model.pkl --S_pseudo 1 --threshold 0.95 --distillation 1 --finetune --lr 6e-4 --student_init simclr --bn_clr --ema_bn
Stage1. Download warmup model(41.4 mIoU), save it in ./pretrained/syn2citylabv2_warmup/
.
python generate_pseudo_label.py --name syn2citylabv2_warmup_soft --soft --n_class 16 --resume_path ./pretrained/syn2citylabv2_warmup/from_synthia_to_cityscapes_on_deeplabv2_best_model.pkl --no_droplast
python calc_prototype.py --resume_path ./pretrained/syn2citylabv2_warmup/from_synthia_to_cityscapes_on_deeplabv2_best_model.pkl --n_class 16
python train.py --name syn2citylabv2_stage1Denoise --src_dataset synthia --n_class 16 --src_rootpath src_rootpath --used_save_pseudo --path_soft Pseudo/syn2citylabv2_warmup_soft --ema --proto_rectify --moving_prototype --proto_consistW 10 --resume_path ./pretrained/syn2citylabv2_warmup/from_synthia_to_cityscapes_on_deeplabv2_best_model.pkl --rce
Stage2. This stage needs well-trained model from stage1 as teacher model. You can get it by above command or download released pretrained stage1 model(51.9 mIoU) and save it in ./pretrained/syn2citylabv2_stage1Denoise/
(path of resume_path
).
python generate_pseudo_label.py --name syn2citylabv2_stage1Denoise --flip --resume_path ./logs/syn2citylabv2_stage2/from_synthia_to_cityscapes_on_deeplabv2_best_model.pkl --no_droplast --n_class 16
python train.py --name syn2citylabv2_stage2 --stage stage2 --src_dataset synthia --n_class 16 --src_rootpath src_rootpath --used_save_pseudo --path_LP Pseudo/syn2citylabv2_stage1Denoise --resume_path ./logs/syn2citylabv2_stage2/from_synthia_to_cityscapes_on_deeplabv2_best_model.pkl --S_pseudo 1 --threshold 0.95 --distillation 1 --finetune --lr 6e-4 --student_init simclr --bn_clr --no_resume
Stage3. This stage needs well-trained model from stage2 as teacher model. You can get it by above command or download released pretrained stage2 model(54.6 mIoU) and save it in ./pretrained/stn2citylabv2_stage2/
(path of resume_path
).
python generate_pseudo_label.py --name syn2citylabv2_stage2 --flip --resume_path ./logs/syn2citylabv2_stage2/from_synthia_to_cityscapes_on_deeplabv2_best_model.pkl --no_droplast --bn_clr --student_init simclr --n_class 16
python train.py --name syn2citylabv2_stage3 --stage stage3 --src_dataset synthia --n_class 16 --src_rootpath src_rootpath --used_save_pseudo --path_LP Pseudo/syn2citylabv2_stage2 --resume_path ./logs/syn2citylabv2_stage2/from_synthia_to_cityscapes_on_deeplabv2_best_model.pkl --S_pseudo 1 --threshold 0.95 --distillation 1 --finetune --lr 6e-4 --student_init simclr --bn_clr --ema_bn
If you like our work and use the code or models for your research, please cite our work as follows.
@article{zhang2021prototypical,
title={Prototypical Pseudo Label Denoising and Target Structure Learning for Domain Adaptive Semantic Segmentation},
author={Zhang, Pan and Zhang, Bo and Zhang, Ting and Chen, Dong and Wang, Yong and Wen, Fang},
journal={arXiv preprint arXiv:2101.10979},
year={2021}
}
The codes and the pretrained model in this repository are under the MIT license as specified by the LICENSE file.
This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact [email protected] with any additional questions or comments.
This code is heavily borrowed from CAG_UDA.
We also thank Jiayuan Mao for his Synchronized Batch Normalization code.