Self-ensembling for visual domain adaptation (small images)

Implementation of the paper Self-ensembling for visual domain adaptation, accepted as a poster at ICLR 2018.

For small image datasets including MNIST, USPS, SVHN, CIFAR-10, STL, GTSRB, etc.

For the VisDA experiments go to https://github.com/Britefury/self-ensemble-visual-domain-adapt-photo/.

Installation

You will need:

• Python 3.6 (Anaconda Python recommended)
• OpenCV with Python bindings
• PyTorch

First, install OpenCV and PyTorch as pip may have trouble with these.

OpenCV with Python bindings

On Linux, install using conda:

> conda install opencv

On Windows, go to https://www.lfd.uci.edu/~gohlke/pythonlibs/ and download the OpenCV wheel file and install with:

> pip install <path_of_opencv_file>

PyTorch

For installation instructions head over to the PyTorch website.

The rest

Use pip like so:

> pip install -r requirements.txt

Usage

Domain adaptation experiments are run via the experiment_domainadapt_meanteacher.py Python program.

The experiments in our paper can be re-created by running the batch_search_exp.sh shell script like so:

bash batch_search_exp.sh <GPU> <RUN>

Where <GPU> is a string identifying the GPU to use (e.g. cuda:0) and enumerates the experiment number so that you can keep logs of multiple repeated runs separate, e.g.:

bash batch_search_exp.sh cuda:0 01

Will run on GPU 0 and will generate log files with names suffixed with run01.

To re-create the supervised baseline experiments:

bash batch_search_exp_sup.sh <GPU> <RUN

Please see the contents of the shell scripts to see the command line options used to control the experiments.

Syn-Digits, GTSTB and Syn-Signs datasets

You will need to download the Syn-Digits, GTSRB and Syn-signs datasets. After this you will need to create the file domain_datasets.cfg to tell the software where to find them.

The following assumes that you have a directory called data in which you will store these three datasets.

Syn-digits

Download Syn-digits from http://yaroslav.ganin.net, on which you will find a Google Drive link to a file called SynthDigits.zip. Create a directory call syndigits within data and unzip SynthDigits.zip within it.

GTSRB

Download GTSRB from http://benchmark.ini.rub.de/?section=gtsrb&subsection=dataset and get the training 'Images and annotations' (GTSRB_Final_Training_Images.zip), Test 'images and annotations' (GTSRB_Final_Test_Images.zip) and the test 'extended annotations including class IDs' (GTSRB_Final_Test_GT.zip).

Unzip the three files within the data directory. You should end up with the following directory structure:

GTSRB/
GTSRB/Final_Training/
GTSRB/Final_Training/Images/   -- training set images
GTSRB/Final_Training/Images/00000/   -- one directory for each class, contains image files
GTSRB/Final_Training/Images/00001/
...
GTSRB/Final_Training/Images/00042/
GTSRB/Final_Test/
GTSRB/Final_Test/Images/   -- test set images
GTSRB/GT-final_test.csv   -- test set ground truths
GTSRB/Readme-Images.txt
GTSRB/Readme-Images-Final-test.txt

Prepare GTSRB

Convert GTSRB to the required format using:

> python prepare_gtsrb.py

Syn-signs

Download Syn-signs from http://graphics.cs.msu.ru/en/node/1337/. You should get a file called synthetic_data.zip. Create a directory called synsigns within data and unzip synthetic_data.zip within data/synsigns to get the following:

synthetic_data/
synthetic_data/train/   -- contains the images as PNGs
synthetic_data/train_labelling.txt   -- ground truths

Prepare Syn-signs

Convert Syn-signs to the required format using:

> python prepare_synsigns.py

Create domain_datasets.cfg

Create the configuration file domain_datasets.cfg within the same directory as the experiment scripts. Put the following into it (change the paths if they are different):

[paths]
syn_digits=data/syndigits
gtsrb=data/GTSRB
syn_signs=data/synsigns