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autoencoder.py
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from torch import nn
from nlgm.manifolds import ProductManifold
# ==========================================================================================
# ============================= FOR USE IN `mnist_experiment.ipynb`=========================
# ==========================================================================================
class Encoder(nn.Module):
"""
Encoder class for the geometric autoencoder.
Parameters
----------
hidden_dim : int
Number of hidden dimensions.
latent_dim : int
Number of latent dimensions.
Methods
-------
forward
Attributes
----------
encoder
"""
def __init__(self, hidden_dim=20, latent_dim=2):
super(Encoder, self).__init__()
self.encoder = nn.Sequential(
nn.Conv2d(1, hidden_dim, 3, padding=1),
nn.BatchNorm2d(hidden_dim),
nn.ReLU(inplace=True),
nn.Conv2d(hidden_dim, hidden_dim, 3, padding=1),
nn.BatchNorm2d(hidden_dim),
nn.ReLU(inplace=True),
nn.Conv2d(hidden_dim, hidden_dim, 3, stride=2, padding=1),
nn.BatchNorm2d(hidden_dim),
nn.ReLU(inplace=True),
nn.Conv2d(hidden_dim, hidden_dim, 3, padding=1),
nn.BatchNorm2d(hidden_dim),
nn.ReLU(inplace=True),
nn.Conv2d(hidden_dim, hidden_dim, 3, padding=1),
nn.BatchNorm2d(hidden_dim),
nn.ReLU(inplace=True),
nn.Conv2d(hidden_dim, latent_dim, 3, padding=1),
nn.BatchNorm2d(latent_dim),
nn.ReLU(inplace=True),
nn.AdaptiveAvgPool2d(1),
nn.Flatten(),
)
def forward(self, x):
"""
Forward pass of the encoder.
Parameters
----------
x : torch.Tensor
Input tensor.
Returns
-------
tensor : torch.Tensor
Encoded output tensor.
"""
z = self.encoder(x)
return z
class Decoder(nn.Module):
"""
Decoder class for the geometric autoencoder.
Parameters
----------
hidden_dim : int
Number of hidden dimensions.
latent_dim : int
Number of latent dimensions.
Methods
-------
forward
Attributes
----------
decoder
"""
def __init__(self, hidden_dim=20, latent_dim=2):
super(Decoder, self).__init__()
self.decoder = nn.Sequential(
nn.Linear(latent_dim, hidden_dim * 7 * 7),
nn.ReLU(inplace=True),
nn.Unflatten(1, (hidden_dim, 7, 7)),
nn.ConvTranspose2d(
hidden_dim, hidden_dim, 3, stride=2, padding=1, output_padding=1
), # Upsample to 14x14
nn.ReLU(inplace=True),
nn.BatchNorm2d(hidden_dim),
nn.ConvTranspose2d(
hidden_dim, hidden_dim, 3, stride=2, padding=1, output_padding=1
), # Upsample to 28x28
nn.ReLU(inplace=True),
nn.BatchNorm2d(hidden_dim),
nn.Conv2d(hidden_dim, 1, 3, padding=1), # Reduce to 1 channel
nn.Sigmoid(), # Output in range [0, 1]
)
def forward(self, z):
"""
Forward pass of the decoder.
Parameters
----------
z : torch.Tensor
Encoded input tensor.
Returns
-------
tensor : torch.Tensor
Decoded output tensor.
"""
x_recon = self.decoder(z)
return x_recon
class GeometricAutoencoder(nn.Module):
"""
Geometric Autoencoder class.
Parameters
----------
signature : list
List of signature dimensions.
hidden_dim : int
Number of hidden dimensions.
latent_dim : int
Number of latent dimensions.
Methods
-------
forward
Attributes
----------
geometry
encoder
decoder
"""
def __init__(self, signature, hidden_dim=20, latent_dim=2):
super(GeometricAutoencoder, self).__init__()
self.geometry = ProductManifold(signature)
self.encoder = Encoder(hidden_dim, latent_dim)
self.decoder = Decoder(hidden_dim, latent_dim)
def forward(self, x):
"""
Forward pass of the geometric autoencoder.
Parameters
----------
x : torch.Tensor
Input tensor.
Returns
-------
tensor : torch.Tensor
Decoded output tensor.
"""
z = self.encoder(x)
z = self.geometry.exponential_map(z)
x_recon = self.decoder(z)
return x_recon