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