import os
from typing import Optional
import torch
import torch.nn.functional as F
from ...data.datasets import BaseDataset
from ..base.base_utils import ModelOutput
from ..nn import BaseDecoder, BaseEncoder
from ..vae import VAE
from .beta_vae_config import BetaVAEConfig
[docs]class BetaVAE(VAE):
r"""
:math:`\beta`-VAE model.
Args:
model_config (BetaVAEConfig): The Variational Autoencoder configuration setting the main
parameters of the model.
encoder (BaseEncoder): An instance of BaseEncoder (inheriting from `torch.nn.Module` which
plays the role of encoder. This argument allows you to use your own neural networks
architectures if desired. If None is provided, a simple Multi Layer Preception
(https://en.wikipedia.org/wiki/Multilayer_perceptron) is used. Default: None.
decoder (BaseDecoder): An instance of BaseDecoder (inheriting from `torch.nn.Module` which
plays the role of decoder. This argument allows you to use your own neural networks
architectures if desired. If None is provided, a simple Multi Layer Preception
(https://en.wikipedia.org/wiki/Multilayer_perceptron) is used. Default: None.
.. note::
For high dimensional data we advice you to provide you own network architectures. With the
provided MLP you may end up with a ``MemoryError``.
"""
def __init__(
self,
model_config: BetaVAEConfig,
encoder: Optional[BaseEncoder] = None,
decoder: Optional[BaseDecoder] = None,
):
VAE.__init__(self, model_config=model_config, encoder=encoder, decoder=decoder)
self.model_name = "BetaVAE"
self.beta = model_config.beta
[docs] def forward(self, inputs: BaseDataset, **kwargs):
"""
The VAE model
Args:
inputs (BaseDataset): The training dataset with labels
Returns:
ModelOutput: An instance of ModelOutput containing all the relevant parameters
"""
x = inputs["data"]
encoder_output = self.encoder(x)
mu, log_var = encoder_output.embedding, encoder_output.log_covariance
std = torch.exp(0.5 * log_var)
z, eps = self._sample_gauss(mu, std)
recon_x = self.decoder(z)["reconstruction"]
loss, recon_loss, kld = self.loss_function(recon_x, x, mu, log_var, z)
output = ModelOutput(
recon_loss=recon_loss,
reg_loss=kld,
loss=loss,
recon_x=recon_x,
z=z,
)
return output
def loss_function(self, recon_x, x, mu, log_var, z):
if self.model_config.reconstruction_loss == "mse":
recon_loss = 0.5 * F.mse_loss(
recon_x.reshape(x.shape[0], -1),
x.reshape(x.shape[0], -1),
reduction="none",
).sum(dim=-1)
elif self.model_config.reconstruction_loss == "bce":
recon_loss = F.binary_cross_entropy(
recon_x.reshape(x.shape[0], -1),
x.reshape(x.shape[0], -1),
reduction="none",
).sum(dim=-1)
KLD = -0.5 * torch.sum(1 + log_var - mu.pow(2) - log_var.exp(), dim=-1)
return (
(recon_loss + self.beta * KLD).mean(dim=0),
recon_loss.mean(dim=0),
KLD.mean(dim=0),
)
def _sample_gauss(self, mu, std):
# Reparametrization trick
# Sample N(0, I)
eps = torch.randn_like(std)
return mu + eps * std, eps