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

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

Installation

We recommand to create conda virtualenv nameded pytorch-py36

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

• Install PyTorch 1.3 with Python3.6, cuda10.1

• Install other dependencies

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

• For visualization using tensorboard

pip install tensorboardX
pip install tensorflow

• For color log print

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

Datasets links to download.

VisDA-C dataset

• Refer this site and download the dataset.

OFFICE-31

• Download from here

OFFICE-HOME

• Download from here. Change domain name from "Real World" to "RealWorld" (Remove space between two words).

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)

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