PoincareDiskSampler

Implementation of a the sampling scheme from a Wrapped Riemannian or Riemannian Gaussian distribution on the Poincaré Disk as proposed in (https://arxiv.org/abs/1901.06033).

Available models:

PoincareVAE

Poincaré Variational Autoencoder model.
class pythae.samplers.PoincareDiskSampler(model, sampler_config=None)[source]

Sampling from the Poincaré Disk using either a Wrapped Riemannian or Riemannian Gaussian distribution.

Parameters

  • model (VAMP) – 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

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

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