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Cooperative Self-Training for Multi-Target Adaptive Semantic Segmentation

[Paper] [Supp Mat.]

Abstract

In this work we address multi-target domain adaptation (MTDA) in semantic segmentation, which consists in adapting a single model from an annotated source dataset to multiple unannotated target datasets that differ in their underlying data distributions. To address MTDA, we propose a self-training strategy that employs pseudo-labels to induce cooperation among multiple domain-specific classifiers. We employ feature stylization as an efficient way to generate image views that forms an integral part of self-training. Additionally, to prevent the network from overfitting to noisy pseudo-labels, we devise a rectification strategy that leverages the predictions from different classifiers to estimate the quality of pseudo-labels. Our extensive experiments on numerous settings, based on four different semantic segmentation datasets, validates the effectiveness of the proposed self-training strategy and shows that our method outperforms state-of-the-art MTDA approaches.

1664807094912

Preparation

Installation

conda create -n coast python=3.7
conda activate coast
conda install pytorch==1.4.0 torchvision==0.5.0 cudatoolkit=10.1 -c pytorch
pip install pyyaml==5.4.1 easydict
conda install tqdm scipy

Datasets

By default, the datasets are put in <root_dir>/Dataset. We use symlinks to hook the CoaST codebase to the datasets, i.e. ln -s path_GTA5 <root_dir>/Dataset/GTA5

GTA5: Please follow the instructions here to download images and semantic segmentation annotations. The GTA5 dataset directory should have this basic structure:

<root_dir>/Dataset/GTA5/                               % GTA dataset root
<root_dir>/Dataset/GTA5/images/                        % GTA images
<root_dir>/Dataset/GTA5/labels/                        % Semantic segmentation labels
...

Cityscapes: Please follow the instructions in Cityscape to download the images and ground-truths. The Cityscapes dataset directory should have this basic structure:

<root_dir>/Dataset/Cityscapes/                         % Cityscapes dataset root
<root_dir>/Dataset/Cityscapes/leftImg8bit              % Cityscapes images
<root_dir>/Dataset/Cityscapes/leftImg8bit/train
<root_dir>/Dataset/Cityscapes/leftImg8bit/val
<root_dir>/Dataset/Cityscapes/gtFine                   % Semantic segmentation labels
<root_dir>/Dataset/Cityscapes/gtFine/train
<root_dir>/Dataset/Cityscapes/gtFine/val
...

Mapillary: Please follow the instructions in Mapillary Vistas to download the images and validation ground-truths. The Mapillary Vistas dataset directory should have this basic structure:

<root_dir>/Dataset/Mapillary/                          % Mapillary dataset root
<root_dir>/Dataset/Mapillary/train                     % Mapillary train set
<root_dir>/Dataset/Mapillary/train/images
<root_dir>/Dataset/Mapillary/validation                % Mapillary validation set
<root_dir>/Dataset/Mapillary/validation/images
<root_dir>/Dataset/Mapillary/validation/labels
...

IDD: Please follow the instructions in IDD to download the images and validation ground-truths. The IDD Segmentation dataset directory should have this basic structure:

<root_dir>/Dataset/IDD_Segmentation/                         % IDD dataset root
<root_dir>/Dataset/IDD_Segmentation/leftImg8bit              % IDD images
<root_dir>/Dataset/IDD_Segmentation/leftImg8bit/train
<root_dir>/Dataset/IDD_Segmentation/leftImg8bit/val
<root_dir>/Dataset/IDD_Segmentation/gtFine                   % Semantic segmentation labels
<root_dir>/Dataset/IDD_Segmentation/gtFine/val
...

Pre-trained models

Due to my google drive limits, I can only upload the checkpoints for G2CM 7-classes and 19-classes settings.

Evaluation

7-class setting

<details> <summary> 1. Synthetic to Real </summary>

GTA5 $\rightarrow$ Cityscapes + IDD.

python test.py --bs 1 --stage stage1 --resume_path ./logs/stage1_G2CI/from_gta5_to_2_on_deeplabv2_best_model.pkl

GTA5 $\rightarrow$ Cityscapes + Mapillary.

python test.py --bs 1 --stage stage1 --src_dataset gta5 --src_rootpath Dataset/GTA5 --tgt_dataset cityscapes mapillary --tgt_rootpath Dataset/Cityscapes Dataset/Mapillary --resume_path ./logs/stage1_G2CM/from_gta5_to_2_on_deeplabv2_best_model.pkl

GTA5 $\rightarrow$ Mapillary + IDD.

python test.py --bs 1 --stage stage1 --src_dataset gta5 --src_rootpath Dataset/GTA5 --tgt_dataset mapillary idd --tgt_rootpath Dataset/Mapillary Dataset/IDD_Segmentation --resume_path ./logs/stage1_G2MI/from_gta5_to_2_on_deeplabv2_best_model.pkl

GTA5 $\rightarrow$ Cityscapes + Mapillary + IDD.

python test.py --bs 1 --stage stage1 --src_dataset gta5 --src_rootpath Dataset/GTA5 --tgt_dataset cityscapes mapillary idd --tgt_rootpath Dataset/Cityscapes Dataset/Mapillary Dataset/IDD_Segmentation --resume_path ./logs/stage1_G2CMI/from_gta5_to_3_on_deeplabv2_best_model.pkl

</details> <details> <summary> 2. Real to Real </summary>

Cityscapes $\rightarrow$ Mapillary + IDD.

python test.py --bs 1 --stage stage1 --src_dataset cityscapes --src_rootpath Dataset/Cityscapes --tgt_dataset mapillary idd --tgt_rootpath Dataset/Mapillary Dataset/IDD_Segmentation --resume_path ./logs/stage1_G2CI/from_cityscapes_to_2_on_deeplabv2_best_model.pkl

IDD $\rightarrow$ Cityscapes + Mapillary.

python test.py --bs 1 --stage stage1 --src_dataset idd --src_rootpath Dataset/IDD_Segmentation --tgt_dataset cityscapes mapillary --tgt_rootpath Dataset/Cityscapes Dataset/Mapillary --resume_path ./logs/stage1_G2CI/from_idd_to_2_on_deeplabv2_best_model.pkl

Mapillary $\rightarrow$ Cityscapes + IDD.

python test.py --bs 1 --stage stage1 --src_dataset mapillary --src_rootpath Dataset/Mapillary --tgt_dataset cityscapes idd --tgt_rootpath Dataset/Cityscapes Dataset/IDD_Segmentation --resume_path ./logs/stage1_G2CI/from_mapillary_to_2_on_deeplabv2_best_model.pkl

</details>

19-class setting

<details> <summary> 1. Synthetic to Real </summary>

GTA5 $\rightarrow$ Cityscapes + IDD.

python test.py --bs 1 --n_class 19 --img_size '1024,512' --resize 1024 --rcrop '512,256' --stage stage1 --resume_path ./logs/stage1_G2CI_19/from_gta5_to_2_on_deeplabv2_best_model.pkl

GTA5 $\rightarrow$ Cityscapes + Mapillary.

python test.py --bs 1 --n_class 19 --img_size '1024,512' --resize 1024 --rcrop '512,256' --stage stage1 --src_dataset gta5 --src_rootpath Dataset/GTA5 --tgt_dataset cityscapes mapillary --tgt_rootpath Dataset/Cityscapes Dataset/Mapillary --resume_path ./logs/stage1_G2CM_19/from_gta5_to_2_on_deeplabv2_best_model.pkl

GTA5 $\rightarrow$ Mapillary + IDD.

python test.py --bs 1 --n_class 19 --img_size '1024,512' --resize 1024 --rcrop '512,256' --stage stage1 --src_dataset gta5 --src_rootpath Dataset/GTA5 --tgt_dataset mapillary idd --tgt_rootpath Dataset/Mapillary Dataset/IDD_Segmentation --resume_path ./logs/stage1_G2MI_19/from_gta5_to_2_on_deeplabv2_best_model.pkl

GTA5 $\rightarrow$ Cityscapes + Mapillary + IDD.

python test.py --bs 1 --n_class 19 --img_size '1024,512' --resize 1024 --rcrop '512,256' --stage stage1 --src_dataset gta5 --src_rootpath Dataset/GTA5 --tgt_dataset cityscapes mapillary idd --tgt_rootpath Dataset/Cityscapes Dataset/Mapillary Dataset/IDD_Segmentation --resume_path ./logs/stage1_G2CMI_19/from_gta5_to_3_on_deeplabv2_best_model.pkl

</details> <details> <summary> 2. Real to Real </summary>

Cityscapes $\rightarrow$ Mapillary + IDD.

python test.py --bs 1 --n_class 19 --img_size '1024,512' --resize 1024 --rcrop '512,256' --stage stage1 --src_dataset cityscapes --src_rootpath Dataset/Cityscapes --tgt_dataset mapillary idd --tgt_rootpath Dataset/Mapillary Dataset/IDD_Segmentation --resume_path ./logs/stage1_G2CI_19/from_cityscapes_to_2_on_deeplabv2_best_model.pkl

IDD $\rightarrow$ Cityscapes + Mapillary.

python test.py --bs 1 --n_class 19 --img_size '1024,512' --resize 1024 --rcrop '512,256' --stage stage1 --src_dataset idd --src_rootpath Dataset/IDD_Segmentation --tgt_dataset cityscapes mapillary --tgt_rootpath Dataset/Cityscapes Dataset/Mapillary --resume_path ./logs/stage1_G2CI_19/from_idd_to_2_on_deeplabv2_best_model.pkl

Mapillary $\rightarrow$ Cityscapes + IDD.

python test.py --bs 1 --n_class 19 --img_size '1024,512' --resize 1024 --rcrop '512,256' --stage stage1 --src_dataset mapillary --src_rootpath Dataset/Mapillary --tgt_dataset cityscapes idd --tgt_rootpath Dataset/Cityscapes Dataset/IDD_Segmentation --resume_path ./logs/stage1_G2CI_19/from_mapillary_to_2_on_deeplabv2_best_model.pkl

</details>

Training

To ensure reproduction, the random seed has been fixed in the code. Still, you may need to train a few times to reach the comparable performance.

7-class setting

<details> <summary> 1. Synthetic to Real </summary>

GTA5 $\rightarrow$ Cityscapes + IDD.

Warm up.

python train.py --name warmup_G2CI --stage warm_up --freeze_bn --gan Vanilla --lr 2.5e-4 --adv 0.001 --no_resume

Self-training.

python train.py --name stage1_G2CI --used_save_pseudo --rectify --resume_path ./logs/warmup_G2CI/from_gta5_to_2_on_deeplabv2_current_model.pkl

GTA5 $\rightarrow$ Cityscapes + Mapillary.

Warm up.

python train.py --name warmup_G2CM --src_dataset gta5 --src_rootpath Dataset/GTA5 --tgt_dataset cityscapes mapillary --tgt_rootpath Dataset/Cityscapes Dataset/Mapillary --stage warm_up --freeze_bn --gan Vanilla --lr 2.5e-4 --adv 0.001 --no_resume

Self-training.

python train.py --name stage1_G2CM --src_dataset gta5 --src_rootpath Dataset/GTA5 --tgt_dataset cityscapes mapillary --tgt_rootpath Dataset/Cityscapes Dataset/Mapillary --used_save_pseudo --rectify --resume_path ./logs/warmup_G2CM/from_gta5_to_2_on_deeplabv2_current_model.pkl

GTA5 $\rightarrow$ Mapillary + IDD.

Warm up.

python train.py --name warmup_G2MI --src_dataset gta5 --src_rootpath Dataset/GTA5 --tgt_dataset mapillary idd --tgt_rootpath Dataset/Mapillary Dataset/IDD_Segmentation --stage warm_up --freeze_bn --gan Vanilla --lr 2.5e-4 --adv 0.001 --no_resume

Self-training.

python train.py --name stage1_G2MI --src_dataset gta5 --src_rootpath Dataset/GTA5 --tgt_dataset mapillary idd --tgt_rootpath Dataset/Mapillary Dataset/IDD_Segmentation --used_save_pseudo --rectify --resume_path ./logs/warmup_G2MI/from_gta5_to_2_on_deeplabv2_current_model.pkl

GTA5 $\rightarrow$ Cityscapes + Mapillary + IDD.

Warm up.

python train.py --name warmup_G2CMI --src_dataset gta5 --src_rootpath Dataset/GTA5 --tgt_dataset cityscapes mapillary idd --tgt_rootpath Dataset/Cityscapes Dataset/Mapillary Dataset/IDD_Segmentation --stage warm_up --freeze_bn --gan Vanilla --lr 2.5e-4 --adv 0.001 --no_resume

Self-training.

python train.py --name stage1_G2CMI --src_dataset gta5 --src_rootpath Dataset/GTA5 --tgt_dataset cityscapes mapillary idd --tgt_rootpath Dataset/Cityscapes Dataset/Mapillary Dataset/IDD_Segmentation --used_save_pseudo --rectify --resume_path ./logs/warmup_G2CMI/from_gta5_to_3_on_deeplabv2_current_model.pkl

</details> <details> <summary> 2. Real to Real </summary>

Cityscapes $\rightarrow$ Mapillary + IDD.

Warm up.

python train.py --name warmup_C2MI --src_dataset cityscapes --src_rootpath Dataset/Cityscapes --tgt_dataset mapillary idd --tgt_rootpath Dataset/Mapillary Dataset/IDD_Segmentation --stage warm_up --freeze_bn --gan Vanilla --lr 2.5e-4 --adv 0.001 --no_resume

Self-training.

python train.py --name stage1_C2MI --src_dataset cityscapes --src_rootpath Dataset/Cityscapes --tgt_dataset mapillary idd --tgt_rootpath Dataset/Mapillary Dataset/IDD_Segmentation --used_save_pseudo --rectify --resume_path ./logs/warmup_C2MI/from_cityscapes_to_2_on_deeplabv2_current_model.pkl

IDD $\rightarrow$ Cityscapes + Mapillary.

Warm up.

python train.py --name warmup_I2CM --src_dataset idd --src_rootpath Dataset/IDD_Segmentation --tgt_dataset cityscapes mapillary --tgt_rootpath Dataset/Cityscapes Dataset/Mapillary --stage warm_up --freeze_bn --gan Vanilla --lr 2.5e-4 --adv 0.001 --no_resume

Self-training.

python train.py --name stage1_I2CM --src_dataset idd --src_rootpath Dataset/IDD_Segmentation --tgt_dataset cityscapes mapillary --tgt_rootpath Dataset/Cityscapes Dataset/Mapillary --used_save_pseudo --rectify --resume_path ./logs/warmup_I2CM/from_idd_to_2_on_deeplabv2_current_model.pkl

Mapillary $\rightarrow$ Cityscapes + IDD.

Warm up.

python train.py --name warmup_M2CI --src_dataset mapillary --src_rootpath Dataset/Mapillary --tgt_dataset cityscapes idd --tgt_rootpath Dataset/Cityscapes Dataset/IDD_Segmentation --stage warm_up --freeze_bn --gan Vanilla --lr 2.5e-4 --adv 0.001 --no_resume

Self-training.

python train.py --name stage1_M2CI --src_dataset mapillary --src_rootpath Dataset/Mapillary --tgt_dataset cityscapes idd --tgt_rootpath Dataset/Cityscapes Dataset/IDD_Segmentation --used_save_pseudo --rectify --resume_path ./logs/warmup_M2CI/from_mapillary_to_2_on_deeplabv2_current_model.pkl

</details>

19-class setting

<details> <summary> 1. Synthetic to Real </summary>

GTA5 $\rightarrow$ Cityscapes + IDD.

Warm up.

python train.py --name warmup_G2CI_19 --n_class 19 --img_size '1024,512' --resize 1024 --rcrop '512,256' --stage warm_up --freeze_bn --gan Vanilla --lr 2.5e-4 --adv 0.001 --no_resume

Self-training.

python train.py --name stage1_G2CI_19 --n_class 19 --img_size '1024,512' --resize 1024 --rcrop '512,256' --used_save_pseudo --rectify --resume_path ./logs/warmup_G2CI_19/from_gta5_to_2_on_deeplabv2_current_model.pkl

GTA5 $\rightarrow$ Cityscapes + Mapillary.

Warm up.

python train.py --name warmup_G2CM_19 --n_class 19 --img_size '1024,512' --resize 1024 --rcrop '512,256' --src_dataset gta5 --src_rootpath Dataset/GTA5 --tgt_dataset cityscapes mapillary --tgt_rootpath Dataset/Cityscapes Dataset/Mapillary --stage warm_up --freeze_bn --gan Vanilla --lr 2.5e-4 --adv 0.001 --no_resume

Self-training.

python train.py --name stage1_G2CM_19 --n_class 19 --img_size '1024,512' --resize 1024 --rcrop '512,256' --src_dataset gta5 --src_rootpath Dataset/GTA5 --tgt_dataset cityscapes mapillary --tgt_rootpath Dataset/Cityscapes Dataset/Mapillary --used_save_pseudo --rectify --resume_path ./logs/warmup_G2CM_19/from_gta5_to_2_on_deeplabv2_current_model.pkl

GTA5 $\rightarrow$ Mapillary + IDD.

Warm up.

python train.py --name warmup_G2MI_19 --n_class 19 --img_size '1024,512' --resize 1024 --rcrop '512,256' --src_dataset gta5 --src_rootpath Dataset/GTA5 --tgt_dataset mapillary idd --tgt_rootpath Dataset/Mapillary Dataset/IDD_Segmentation --stage warm_up --freeze_bn --gan Vanilla --lr 2.5e-4 --adv 0.001 --no_resume

Self-training.

python train.py --name stage1_G2MI_19 --n_class 19 --img_size '1024,512' --resize 1024 --rcrop '512,256' --src_dataset gta5 --src_rootpath Dataset/GTA5 --tgt_dataset mapillary idd --tgt_rootpath Dataset/Mapillary Dataset/IDD_Segmentation --used_save_pseudo --rectify --resume_path ./logs/warmup_G2MI/from_gta5_to_2_on_deeplabv2_current_model.pkl

GTA5 $\rightarrow$ Cityscapes + Mapillary + IDD.

Warm up.

python train.py --name warmup_G2CMI_19 --n_class 19 --img_size '1024,512' --resize 1024 --rcrop '512,256' --src_dataset gta5 --src_rootpath Dataset/GTA5 --tgt_dataset cityscapes mapillary idd --tgt_rootpath Dataset/Cityscapes Dataset/Mapillary Dataset/IDD_Segmentation --stage warm_up --freeze_bn --gan Vanilla --lr 2.5e-4 --adv 0.001 --no_resume

Self-training.

python train.py --name stage1_G2CMI_19 --n_class 19 --img_size '1024,512' --resize 1024 --rcrop '512,256' --src_dataset gta5 --src_rootpath Dataset/GTA5 --tgt_dataset cityscapes mapillary idd --tgt_rootpath Dataset/Cityscapes Dataset/Mapillary Dataset/IDD_Segmentation --used_save_pseudo --rectify --resume_path ./logs/warmup_G2CMI/from_gta5_to_3_on_deeplabv2_current_model.pkl

</details> <details> <summary> 2. Real to Real </summary>

Cityscapes $\rightarrow$ Mapillary + IDD.

Warm up.

python train.py --name warmup_C2MI_19 --n_class 19 --img_size '1024,512' --resize 1024 --rcrop '512,256'  --src_dataset cityscapes --src_rootpath Dataset/Cityscapes --tgt_dataset mapillary idd --tgt_rootpath Dataset/Mapillary Dataset/IDD_Segmentation --stage warm_up --freeze_bn --gan Vanilla --lr 2.5e-4 --adv 0.001 --no_resume

Self-training.

python train.py --name stage1_C2MI_19 --n_class 19 --img_size '1024,512' --resize 1024 --rcrop '512,256'  --src_dataset cityscapes --src_rootpath Dataset/Cityscapes --tgt_dataset mapillary idd --tgt_rootpath Dataset/Mapillary Dataset/IDD_Segmentation --used_save_pseudo --rectify --resume_path ./logs/warmup_C2MI_19/from_cityscapes_to_2_on_deeplabv2_current_model.pkl

IDD $\rightarrow$ Cityscapes + Mapillary.

Warm up.

python train.py --name warmup_I2CM_19 --n_class 19 --img_size '1024,512' --resize 1024 --rcrop '512,256'  --src_dataset idd --src_rootpath Dataset/IDD_Segmentation --tgt_dataset cityscapes mapillary --tgt_rootpath Dataset/Cityscapes Dataset/Mapillary --stage warm_up --freeze_bn --gan Vanilla --lr 2.5e-4 --adv 0.001 --no_resume

Self-training.

python train.py --name stage1_I2CM_19 --n_class 19 --img_size '1024,512' --resize 1024 --rcrop '512,256'  --src_dataset idd --src_rootpath Dataset/IDD_Segmentation --tgt_dataset cityscapes mapillary --tgt_rootpath Dataset/Cityscapes Dataset/Mapillary --used_save_pseudo --rectify --resume_path ./logs/warmup_I2CM_19/from_idd_to_2_on_deeplabv2_current_model.pkl

Mapillary $\rightarrow$ Cityscapes + IDD.

Warm up.

python train.py --name warmup_M2CI_19 --n_class 19 --img_size '1024,512' --resize 1024 --rcrop '512,256'  --src_dataset mapillary --src_rootpath Dataset/Mapillary --tgt_dataset cityscapes idd --tgt_rootpath Dataset/Cityscapes Dataset/IDD_Segmentation --stage warm_up --freeze_bn --gan Vanilla --lr 2.5e-4 --adv 0.001 --no_resume

Self-training.

python train.py --name stage1_M2CI_19 --n_class 19 --img_size '1024,512' --resize 1024 --rcrop '512,256'  --src_dataset mapillary --src_rootpath Dataset/Mapillary --tgt_dataset cityscapes idd --tgt_rootpath Dataset/Cityscapes Dataset/IDD_Segmentation --used_save_pseudo --rectify --resume_path ./logs/warmup_M2CI_19/from_mapillary_to_2_on_deeplabv2_current_model.pkl

</details>

Acknowledgements

This codebase is heavily borrowed from MTAF and ProDA.