GaussianMixtureSampler

Implementation of a Gaussian mixture sampler.

Available models:

AE	Vanilla Autoencoder model.
VAE	Vanilla Variational Autoencoder model.
BetaVAE	\(\beta\)-VAE model.
VAE_LinNF	Variational Auto Encoder with linear Normalizing Flows model.
VAE_IAF	Variational Auto Encoder with Inverse Autoregressive Flows (IAF).
DisentangledBetaVAE	Disentangled \(\beta\)-VAE model.
FactorVAE	FactorVAE model.
BetaTCVAE	\(\beta\)-TCVAE model.
IWAE	Importance Weighted Autoencoder model.
MSSSIM_VAE	VAE using perseptual similarity metrics model.
WAE_MMD	Wasserstein Autoencoder model.
INFOVAE_MMD	Info Variational Autoencoder model.
VAMP	Variational Mixture of Posteriors (VAMP) VAE model
SVAE	\(\mathcal{S}\)-VAE model.
Adversarial_AE	Adversarial Autoencoder model.
VAEGAN	Variational Autoencoder using Adversarial reconstruction loss model.
VQVAE	Vector Quantized-VAE model.
HVAE	Hamiltonian VAE.
RAE_GP	Regularized Autoencoder with gradient penalty model.
RAE_L2	Regularized Autoencoder with L2 decoder params regularization model.
RHVAE	Riemannian Hamiltonian VAE model.

class pythae.samplers.GaussianMixtureSamplerConfig(n_components=10)

Gaussian mixture sampler config class.

Parameters

n_components (int) – The number of Gaussians in the mixture

class pythae.samplers.GaussianMixtureSampler(model, sampler_config=None)

Fits a Gaussian Mixture in the Autoencoder’s latent space.

Parameters

• model (BaseAE) – The vae model to sample from.

• sampler_config (BaseSamplerConfig) – An instance of BaseSamplerConfig in which any sampler’s parameters is made available. If None a default configuration is used. Default: None.

Note

The method fit must be called to fit the sampler before sampling.

fit(train_data, batch_size=64, **kwargs)

Method to fit the sampler from the training data

Parameters

• train_data (Union[torch.Tensor, np.ndarray, Dataset]) – The train data needed to retrieve the training embeddings and fit the mixture in the latent space.

• batch_size (int) – The batch size to use to retrieve the embeddings. Default: 64.

sample(num_samples=1, batch_size=500, output_dir=None, return_gen=True, save_sampler_config=False)

Main sampling function of the sampler.

Parameters

• num_samples (int) – The number of samples to generate

• batch_size (int) – The batch size to use during sampling

• output_dir (str) – The directory where the images will be saved. If does not exist the folder is created. If None: the images are not saved. Defaults: None.

• return_gen (bool) – Whether the sampler should directly return a tensor of generated data. Default: True.

• save_sampler_config (bool) – Whether to save the sampler config. It is saved in output_dir

Returns

The generated images

Return type

Tensor