SSL-PG-GAN

A Semi-Supervised Progressively Growing Generative Adversarial Network

This project builds mainly off of the code presented in Progressive Growing of GAN's for Improved Quality, Stability and Variation by Kerras et al. ICLR 2018. Inspiration also came from OpenAI's Improved Techniques for Training GANs which details semi-supervised implementations of GANs.

My research tries to incorporate semi-supervised learning alongside progressively growing training to get the best of both. Not needing to worry about an overabundance of labeled data and the ability to generate high definition images.

Installation

Go to to get the latest version of anaconda for Python3 it should download a shell script

https://www.anaconda.com/products/individual#linux

Once downloaded chmod +x <The file you just downloaded>

Then execute the file with either ./Anaconda3-2020.02-Linux-x86_64.sh or bash </path/to/file.sh>

Complete the installation with all default settings

Once complete open a new terminal so the changes take effect.

Make sure you have git installed

sudo apt update

sudo apt install git

sudo apt install build-essential

git clone https://github.com/ACK-J/SSL-PG-GAN.git

Open up the file environment.yml in any text editor you want. Go all the way to the bottom of the file and you should see the line prefix: /home/USERNAME/anaconda3/envs/ssl-pggan Please change where it says USERNAME to be your current username. Once that's done save and quit.

Next, run the following command to create the environment

conda env create -f environment.yml

This could take a little while for everything to install depending on your internet connection

Once complete you will be able to start the environment with the following command

conda activate ssl-pggan

Now you are in an environment with everything you need to run SSL-PGGAN

Creating a dataset

You should have a dataset in mind that you want to use but if you don't I am providing the one I used to test below so you can follow along and get something running

https://drive.google.com/file/d/13OHIv7A_AFbKWe_URwbyOvEKRnPovrAq/view?usp=sharing

Once downloaded place into the root of the SSL-PG-GAN folder

Please note that every image must:

1. Have the same width and height
2. Image resolution must be a power-of-two
3. Image must be either grayscale or RGB

Example Image Size: 3 X 512 X 512

To create the cat/dogs dataset run the following command python3 dataset_tool.py <Labeled dir> <Unlabeled dir> 2> /dev/null

Make sure to use full paths for both Labeled and Unlabeled datasets.
The reason that I send stderr to /dev/null is because the version of tensorflow has very noisy deprecation warnings which are annoying.
Now if you look in the Labeled and Unlabeled directories you should see a bunch of .tfrecord files

Configuration

Edit config.py to see all of the options available. You must change two lines in the configuration file before training the model. They are both commented with # CHANGE ME and are the paths to the parent directory of the labeled and unlabeled data folders. This should normally just be the path to the SSL-PG-GAN folder.

Other configuration changes include changing the amount of Nvidia GPU's used.

Note if you want to select multiple GPU's make sure the environment variable CUDA_VISIBLE_DEVICES is set. You can do this by typing echo $CUDA_VISIBLE_DEVICES

You can view the GPU's available to you by typing nvidia-smi or nvidia-smi -l

You can change the GPU's selected by typing export CUDA_VISIBLE_DEVICES=0,1,2,3

Make sure the amount of GPU's selected matches the uncommented line in the config.py file

Training

python3 train.py

This will most likely take a while even with 4 GPU's running. I recommend running the training using screen or tmux so that if you lose connection via SSH you won't have to restart the training.

Testing

python3 test_discriminator.py <FULL path to out-of-sample image directory> <id of training round>

You can find the training round by going to results/ directory. The number at the beginning of each directory identifies each training session. Make sure tp use the entire 3 digit number ex) 000 or 001 etc...

Comments and Tips

It is not advised to run this code in a VM or a docker container due to the fact that it is tricky to pass graphics cards into them and have them function properly. This is not to say that it isn't possible just that it may be time-consuming.

notem / SSL-PG-GAN