/DSBN

Official Implementation of "Domain Specific Batch Normalization for Unsupervised Domain Adaptation (CVPR2019)"

Primary LanguagePython

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

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

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
  • 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)

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