In this repo, we provide the code for the paper Adaptive Boosting for Domain Adaptation: Towards Robust Predictions in Scene Segmentation.
- Will more “hard” samples to have a negative effect? How to simulate? (https://github.com/layumi/AdaBoost_Seg/blob/master/Toy_Example.md)
- Evaluation on semi-supervised Cifar10 (https://github.com/layumi/Cifar10-Adaboost)
- A brief illustration of AdaBoost in English at [Here] and in Chinese [中文介绍]
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When adopting this method to other fields, we suggest to tune the sampling weight with temperature to suit your task and dataset. In this paper, we do not change it, and keep it as 1.
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In our recent experiment, we can achieve a better performance 49.72% (MRNet+Ours) than the number reported in the paper. We think that when Aggrregated Model converges, the adboost sampler updates slowly, which also compromises the performance. If we give more weights to recent snapshots for updating sampler, it works better.
python train_ms.py --snapshot-dir ./snapshots/ReRUN_Adaboost_SWA_SE_GN_batchsize2_1024x512_pp_ms_me0_classbalance7_kl0.1_lr2_drop0.1_seg0.5_swa0_recent --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 --often-balance --use-se --swa --swa_start 0 --adaboost --recent- Python 3.6
- GPU Memory >= 14G (e.g.,RTX6000 or V100)
- Pytorch
Download [GTA5] and [Cityscapes] to run the basic code. Alternatively, you could download extra two datasets from [SYNTHIA] and [OxfordRobotCar].
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Download The GTA5 Dataset
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Download The SYNTHIA Dataset SYNTHIA-RAND-CITYSCAPES (CVPR16)
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Download The Cityscapes Dataset
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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/
| | ├── ...
- GTA5 to Cityscapes (ResNet-101)
Stage-I: (around 49.0%)
python train_ms.py --snapshot-dir ./snapshots/ReRUN_Adaboost_SWA_SE_GN_batchsize2_1024x512_pp_ms_me0_classbalance7_kl0.1_lr2_drop0.1_seg0.5_swa0 --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 --often-balance --use-se --swa --swa_start 0 --adaboostGenerate Pseudo Label:
python generate_plabel_cityscapes.py --restore ./snapshots/ReRUN_Adaboost_SWA_SE_GN_batchsize2_1024x512_pp_ms_me0_classbalance7_kl0.1_lr2_drop0.1_seg0.5_swa0/GTA5_40000_average.pthStage-II (with recitfying pseudo label): (around 50.9%)
python train_ft.py --snapshot-dir ./snapshots/Adaboost_1280x640_restore_ft48_GN_batchsize2_960x480_pp_ms_me0_classbalance7_kl0_lr4_drop0.2_seg0.5_BN_80_255_0.8_Noaug_swa2.5W_t97 --restore-from ./snapshots/ReRUN_Adaboost_SWA_SE_GN_batchsize2_1024x512_pp_ms_me0_classbalance7_kl0.1_lr2_drop0.1_seg0.5_swa0/GTA5_40000_average.pth --drop 0.2 --warm-up 5000 --batch-size 2 --learning-rate 4e-4 --crop-size 960,480 --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 --often-balance --use-se --input-size 1280,640 --train_bn --autoaug False --swa --adaboost --swa_start 25000 --threshold 97- SYNTHIA to Cityscapes
Stage-I:
python train_ms_synthia.py --snapshot-dir ./snapshots/AdaBoost_SWA_SY_SE_GN_batchsize2_1024x512_pp_ms_me0_classbalance7_kl0.1_lr2_drop0.1_seg0.5_power0.5 --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 --often-balance --use-se --swa --swa_start 0 --adaboost Generate Pseudo Label:
python generate_plabel_cityscapes_SYNTHIA.py --restore ./snapshots/AdaBoost_SWA_SY_SE_GN_batchsize2_1024x512_pp_ms_me0_classbalance7_kl0.1_lr2_drop0.1_seg0.5_power0.5/GTA5_50000_average.pthStage-II:
python train_ft_synthia.py --snapshot-dir ./snapshots/Cosine_Adaboost_SY_1280x640_restore_ft_GN_batchsize8_512x256_pp_ms_me0_classbalance7_kl0.1_lr8_drop0.1_seg0.5_BN_255_Noaug_t777_swa2.5W --restore ./snapshots/AdaBoost_SWA_SY_SE_GN_batchsize2_1024x512_pp_ms_me0_classbalance7_kl0.1_lr2_drop0.1_seg0.5_power0.5/GTA5_50000_average.pth --drop 0.1 --warm-up 5000 --batch-size 8 --learning-rate 8e-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 50 --max-value 7 --gpu-ids 0 --often-balance --use-se --input-size 1280,640 --autoaug False --swa --swa_start 25000 --threshold 777 --adaboost --train_bn --cosine- Cityscapes to Oxford RobotCar
Stage-I: (around 73.80%) higher than paper.
python train_ms_robot.py --snapshot-dir ./snapshots/Adaboost_SWA3W_Robot_SE_GN_batchsize6_adapative_kl0.1_sam_lr6 --drop 0.1 --warm-up 5000 --batch-size 6 --learning-rate 6e-4 --crop-size 800,400 --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 0,1,2 --often-balance --use-se --swa --swa_start 30000 --adaboost --samGenerate Pseudo Label:
python generate_plabel_robot.py --restore ./snapshots/Adaboost_SWA3W_Robot_SE_GN_batchsize6_adapative_kl0.1_sam_lr6/GTA5_70000_average.pthStage-II: (around 75.62%)
python train_ft_robot.py --snapshot-dir ./snapshots/Adaboost_0.9RB_b3_lr3_800x432_97_swa0W_T80 --restore-from ./snapshots/Adaboost_SWA3W_Robot_SE_GN_batchsize6_adapative_kl0.1_sam_lr6/GTA5_70000_average.pth --drop 0.1 --warm-up 5000 --batch-size 3 --learning-rate 3e-4 --crop-size 800,432 --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 50 --max-value 7 --gpu-ids 0,1,2 --often-balance --use-se --input-size 1280,960 --train_bn --adaboost --swa --swa_start 0 --threshold 0.8 --autoaug False- GTA5 to Cityscapes (VGG-16)
Stage-I: (around 39.5%)
python train_ms.py --snapshot-dir ./snapshots/255VGGBN_Adaboost_SWA_SE_GN_batchsize3_1024x512_pp_ms_me0_classbalance7_kl0.1_lr3_drop0.1_seg0.5_swa0_auto --drop 0.1 --warm-up 5000 --batch-size 3 --learning-rate 3e-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 0,1,2 --often-balance --use-se --swa --swa_start 0 --adaboost --model DeepVGG --autoaug python evaluate_cityscapes.py --restore-from ./snapshots/ReRUN_Adaboost_SWA_SE_GN_batchsize2_1024x512_pp_ms_me0_classbalance7_kl0.1_lr2_drop0.1_seg0.5_swa0/GTA5_40000_average.pthThe trained model is available at [Wait]
- The folder with
SYin name is for SYNTHIA-to-Cityscapes - The folder with
RBin name is for Cityscapes-to-Robot Car
Core code is relatively simple, and could be directly applied to other works.
Adaptive Data Sampler: https://github.com/layumi/AdaBoost_Seg/blob/master/train_ms.py#L429-L436
Student Aggregation: https://github.com/layumi/AdaBoost_Seg/blob/master/train_ms.py#L415-L427
We also would like to thank great works as follows:
- https://github.com/layumi/Seg-Uncertainty
- https://github.com/wasidennis/AdaptSegNet
- https://github.com/RoyalVane/CLAN
- https://github.com/yzou2/CRST
@article{zheng2021adaboost,
title={Adaptive Boosting for Domain Adaptation: Towards Robust Predictions in Scene Segmentation},
author={Zheng, Zhedong and Yang, Yi},
journal={IEEE Transactions on Image Processing},
doi={10.1109/TIP.2022.3195642},
note={\mbox{doi}:\url{10.1109/TIP.2022.3195642}},
year={2021}
}
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