import torch
from ...models import PoincareVAE
from ..base import BaseSampler
from .pvae_sampler_config import PoincareDiskSamplerConfig
[docs]class PoincareDiskSampler(BaseSampler):
"""Sampling from the Poincaré Disk using either a Wrapped Riemannian or Riemannian Gaussian
distribution.
Args:
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
"""
def __init__(
self, model: PoincareVAE, sampler_config: PoincareDiskSamplerConfig = None
):
assert isinstance(
model, PoincareVAE
), "This sampler is only suitable for PoincareVAE model"
if sampler_config is None:
sampler_config = PoincareDiskSamplerConfig()
BaseSampler.__init__(self, model=model, sampler_config=sampler_config)
self.gen_distribution = self.model.prior(
loc=self.model._pz_mu,
scale=self.model._pz_logvar.exp(),
manifold=self.model.latent_manifold,
)
[docs] def sample(
self,
num_samples: int = 1,
batch_size: int = 500,
output_dir: str = None,
return_gen: bool = True,
save_sampler_config: bool = False,
) -> torch.Tensor:
"""Main sampling function of the sampler.
Args:
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:
~torch.Tensor: The generated images
"""
full_batch_nbr = int(num_samples / batch_size)
last_batch_samples_nbr = num_samples % batch_size
x_gen_list = []
for i in range(full_batch_nbr):
z = self.gen_distribution.rsample(torch.Size([batch_size])).reshape(
batch_size, -1
)
x_gen = self.model.decoder(z)["reconstruction"].detach()
if output_dir is not None:
for j in range(batch_size):
self.save_img(
x_gen[j], output_dir, "%08d.png" % int(batch_size * i + j)
)
x_gen_list.append(x_gen)
if last_batch_samples_nbr > 0:
z = self.gen_distribution.rsample(
torch.Size([last_batch_samples_nbr])
).reshape(last_batch_samples_nbr, -1)
x_gen = self.model.decoder(z)["reconstruction"].detach()
if output_dir is not None:
for j in range(last_batch_samples_nbr):
self.save_img(
x_gen[j],
output_dir,
"%08d.png" % int(batch_size * full_batch_nbr + j),
)
x_gen_list.append(x_gen)
if save_sampler_config:
self.save(output_dir)
if return_gen:
return torch.cat(x_gen_list, dim=0)