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"""Conditioning Augmentation Module"""
from typing import Any
import torch
from torch import nn
class CondAugmentation(nn.Module):
"""Conditioning Augmentation Module"""
def __init__(self, D: int, conditioning_dim: int):
"""
:param D: Dimension of the text embedding space [D from AttnGAN paper]
:param conditioning_dim: Dimension of the conditioning space
"""
super().__init__()
self.cond_dim = conditioning_dim
self.cond_augment = nn.Linear(D, conditioning_dim * 4, bias=True)
self.glu = nn.GLU(dim=1)
def encode(self, text_embedding: torch.Tensor) -> Any:
"""
This function encodes the text embedding into the conditioning space
:param text_embedding: Text embedding
:return: Conditioning embedding
"""
x_tensor = self.glu(self.cond_augment(text_embedding))
mu_tensor = x_tensor[:, : self.cond_dim]
logvar = x_tensor[:, self.cond_dim :]
return mu_tensor, logvar
def sample(self, mu_tensor: torch.Tensor, logvar: torch.Tensor) -> torch.Tensor:
"""
This function samples from the Gaussian distribution
:param mu: Mean of the Gaussian distribution
:param logvar: Log variance of the Gaussian distribution
:return: Sample from the Gaussian distribution
"""
std = torch.exp(0.5 * logvar)
eps = torch.randn_like(
std
) # check if this should add requires_grad = True to this tensor?
return mu_tensor + eps * std
def forward(self, text_embedding: torch.Tensor) -> Any:
"""
This function encodes the text embedding into the conditioning space,
and samples from the Gaussian distribution.
:param text_embedding: Text embedding
:return c_hat: Conditioning embedding (C^ from StackGAN++ paper)
:return mu: Mean of the Gaussian distribution
:return logvar: Log variance of the Gaussian distribution
"""
mu_tensor, logvar = self.encode(text_embedding)
c_hat = self.sample(mu_tensor, logvar)
return c_hat, mu_tensor, logvar
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