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Domain-Specific Batch Normalization for Unsupervised Domain Adaptation (DSBN)

Pytorch implementation of Domain-Specific Batch Normalization for Unsupervised Domain Adaptation (CVPR2019). BN vs DSBN

Woong-Gi Chang, Tackgeun You, Seonguk Seo, Suha Kwak, Bohyung Han

The first author name has changed from Woong-Gi Chang to Woojae Chang.

Citation

If you want to cite our work, follow the link arXiv.

Installation

We recommand to create conda virtualenv nameded pytorch-py36

conda create -n pytorch-py36 python=3.6 
source activate pytorch-py36

conda install numpy scipy matplotlib cython h5py
conda install -c menpo opencv

pip install tensorboardX
pip install tensorflow

pip install coloredlogs

Dataset

data directory looks like below:

data
├── Office
│   └── domain_adaptation_images
│       ├── amazon
│       ├── dslr
│       └── webcam
├── Office-home
│   └── OfficeHomeDataset_10072016
│       ├── Art
│       ├── Clipart
│       ├── Product
│       └── RealWorld
└── VisDA
    ├── test
    ├── train
    └── validation
<!-- ```text data ├── image-clef │ ├── b │ ├── c │ ├── i │ ├── list │ └── p ├── MNIST │ ├── processed │ └── raw ├── Office │ └── domain_adaptation_images │ ├── amazon │ ├── dslr │ └── webcam ├── OfficeCaltech │ ├── amazon │ ├── caltech │ ├── dslr │ └── webcam ├── Office-home │ └── OfficeHomeDataset_10072016 │ ├── Art │ ├── Clipart │ ├── Product │ └── RealWorld ├── SVHN ├── USPS │ ├── processed │ └── raw └── VisDA ├── test ├── train └── validation ``` -->

Datasets links to download.

<!-- #### SVHN-USPS-MNIST Dataset (We used dataset from torchvision.dataset) * For SVHN, MNIST, you can automatically download the datasets by running our training code. * For USPS dataset, [Downlaod]("https://www.kaggle.com/bistaumanga/usps-dataset/downloads/usps.h5") and place file at "data/USPS/raw" -->

VisDA-C dataset

OFFICE-31

OFFICE-HOME

<!-- #### OFFICE-CALTECH -->

Training Examples

VISDA2017

Stage1 Training (training existing UDA model with DSBN)

This is a example script for training MSTN on visda 2017 dataset for stage1. Use resnet101dsbn for resnet101 with domain-specific batchnorm

# DSCN
python trainval_multi.py --model-name resnet101dsbn --exp-setting visda --sm-loss --adv-loss --source-datasets train --target-datasets validation --batch-size 40 --save-dir output/resnet101dsbn_visda_stage1 --print-console
# cf. batchnorm
python trainval_multi.py --model-name resnet101 --exp-setting visda --sm-loss --adv-loss --source-datasets train --target-datasets validation --batch-size 40 --save-dir output/resnet101_visda_stage1 --print-console

After training you can get stage1 model at save-dir.

Stage2 Training (self-training a new model with the model trained on stage1)

Stage2 Training

For stage1, use finetune for single source unsupervised domain adaptation, and finetune_multi for multi source setting.

This is a example script for training MSTN on visda 2017 dataset for stage2.

# DSCN
python finetune_multi.py --model-name resnet101dsbn --exp-setting visda --source-dataset train --target-dataset validation --pseudo-target-loss default_ensemble --no-lambda --teacher-model-path output/resnet101dsbn_visda_stage1/best_resnet101dsbn+None+i0_train2validation.pth --learning-rate 5e-5 --batch-size 40 --save-dir output/resnet101dsbn_visda_stage2 --print-console
# cf. batchnorm
python finetune_multi.py --model-name resnet101 --exp-setting visda --source-dataset train --target-dataset validation --pseudo-target-loss default_ensemble --no-lambda --teacher-model-path output/resnet101_visda_stage1/best_resnet101+None+i0_train2validation.pth --learning-rate 5e-5 --batch-size 40 --save-dir output/resnet101_visda_stage2 --print-console

Testing

python evlauate_multi.py --model-path [model-path] # for multi-source setting

File name should follow the format: "best_{model_name}+{jitter}+{infeatures}_{source_dataset}2{target_dataset}.pth"

example: best_resnet101dsbn+None+i0_train2validation.pth