Data Augmentation with Variational Autoencoders and Manifold Sampling

This repository is the official implementation of Data Augmentation with Variational Autoencoders and Manifold Sampling. This repository can be used to reproduce the results presented in the paper.

Update: This method is added to the software Pyraug available here. This software allows to use the method very easily on your own data. Check this out!

Requirements

Python 3.8 environment is used for the tests.

To install requirements run:

pip install -r requirements

Training a model

A commandline to train a model is provided in experiments_launcher.sh.

python experiment_runner.py --path_to_train_loader 'training_data/train_Shapes' --batch_size 200 --max_epochs 10000 --lr 0.001 --model_name 'RHVAE' --input_dim 2500 --n_lf 3 --early_stopping_epochs 50 --regularization 0.001 --eps_lf 0.01 --verbose --seed 8

Parser arguments

--path_to_train_loader "path to loader. Data must be loadable using 'checkpoint = torch.load()' and 'data = checkpoint['data']' (targets = checkpoint['targets']' if targets are available)"
--batch_size "batch size for training (default: 10)"
--max_epochs MAX_EPOCHS "Max number of epochs (default: 10000)"
--lr "Learning rate (default: 0.0001)"
--early_stopping_epochs "number of epochs for early stopping"
--no_cuda "disables CUDA training"
--seed "random seed (default: 8)"
--model_name "Choice of model [RHVAE or VAE] (default: RHVAE)"
--input_dim "Input dimension"
--latent_dim "Latent space dimension"
--n_lf "n_lf, the number of leapfrog steps in RHVAE training (default: 3)"
--eps_lf "eps_lf, the size of leapfrog step size in RHVAE training (default: 0.001)"
--beta_zero "beta zero sqrt, the temperature in the leapfrog integrator for RHVAE training"
--temperature "T, the metric temperature for RHVAE (default: 0.8)"
--regularization "lambda, the metric regularization factor for RHVAE (default: 0.01)"
--metric_fc "metric hidden units, metric's neural net architecture for RHVAE (default: 400)"
--dynamic_binarization "allow dynamic binarization"
--verbose "Verbosity (default: False)"

At the end of training the model is stored in recording/model_name__dataset_name__params/model_name__dataset.model.

Generation

A commandline to generate data from a trained model is provided in generation_launcher.sh

python generation_parser.py --path_to_model 'recordings/RHVAE_train_Shapes_ldim_2_nlf_3_epslf_0.01_T_0.8_lbd_0.001/RHVAE_train_Shapes.model' \
--num_samples 100 --verbose --mcmc_steps 400 --generation_method 'riemannian_rw' --seed 8

Parser arguments

--path_to_model "path to the model"
--batch_size "batch size for generation (default: 100)"
--num_samples "number of samples to generate"
--generation_method "generation method, choices=['prior', 'metric_sampling', 'riemannian_rw'], (default: 'metric_sampling')"
--mcmc_steps_nbr "number of MCMC steps for metric sampling and Riemannian RW (default: 400)"
--n_lf "n_lf, the number of leapfrog steps in HMC sampler"
--eps_lf "eps_lf, the leapfrog step size in HMC sampler"
--beta_zero_sqrt "the tempering coefficient beta (default: 1.0 ie no tempering)"
--eigenvalues "the eigenvalues of Sigma for the Riemannian random walk"
--seed "random seed"
--no_cuda "disables CUDA training"
--verbose "verbosity (default: False)"

Generated data are stored in the folder generated_data/YYYY-MM-DD_hh_mm_ss__model_name__dataset_name__sampling_method with an extension .data.

Short files description

• experiment_runner.py: Parser for training.
• generation_parser.py: Parser for generation.
• experiments_launcher.sh: Example of commandline for training.
• generation_launcher.sh: Example of commandline for generating data from trained models.

Short folders description

• models: Contains the main pieces of code (i.e. models, generation procedures ...).
• trainers: Contains the training.py script to train the models.
• trained_models: Contain the pre-trained models used in the paper.
• training_data: Contains the data used in the paper
• notebooks: Contains the demo notebook.

Citing

@incollection{chadebec2021data,
  title={Data Augmentation with Variational Autoencoders and Manifold Sampling},
  author={Chadebec, Cl{\'e}ment and Allassonni{\`e}re, St{\'e}phanie},
  booktitle={Deep Generative Models, and Data Augmentation, Labelling, and Imperfections},
  pages={184--192},
  year={2021},
  publisher={Springer}
}