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"""
Autoencoder model for MNIST.
Can be used for latent diffusion.
"""
import torch
import torch.nn as nn
class AEModule(nn.Module):
"""Base class for autoencoder modules (encoder and decoder)."""
def __init__(self):
super().__init__()
def forward(self, x: torch.Tensor) -> torch.Tensor:
raise NotImplementedError("Subclasses must implement forward method.")
def encode(self, x: torch.Tensor) -> torch.Tensor:
"""Encode input to latent space."""
raise NotImplementedError("Subclasses must implement encode method.")
def decode(self, z: torch.Tensor) -> torch.Tensor:
"""Decode from latent space to image space."""
raise NotImplementedError("Subclasses must implement decode method.")
class Autoencoder(AEModule):
"""
Convolutional Autoencoder for MNIST images.
Args:
input_channels: Number of input image channels (1 for MNIST)
latent_channels: Number of channels in latent space
"""
def __init__(self, input_channels: int = 1, latent_channels: int = 1):
super().__init__()
self.input_channels = input_channels
self.latent_channels = latent_channels
# Encoder: 28x28 -> 8x8
self.encoder = nn.Sequential(
nn.Conv2d(input_channels, 16, kernel_size=3, padding=1),
nn.ReLU(),
nn.Conv2d(16, 16, kernel_size=3, padding=1),
nn.ReLU(),
nn.MaxPool2d(kernel_size=2), # 14x14
nn.Conv2d(16, 32, kernel_size=3, padding=1),
nn.ReLU(),
nn.Conv2d(32, 32, kernel_size=3, padding=1),
nn.ReLU(),
nn.MaxPool2d(kernel_size=2), # 7x7
nn.Conv2d(32, 64, kernel_size=3, padding=1),
nn.ReLU(),
nn.Conv2d(64, 64, kernel_size=3, padding=1),
nn.ReLU(),
nn.Conv2d(64, latent_channels, kernel_size=3, padding=1),
nn.ReLU(),
# 7x7 -> 8x8 (no activation - latent space should be unconstrained)
nn.Conv2d(latent_channels, latent_channels, kernel_size=2, padding=1),
)
# Decoder: 8x8 -> 28x28
self.decoder = nn.Sequential(
# 8x8 -> 7x7
nn.Conv2d(latent_channels, latent_channels, kernel_size=2, padding=0),
nn.ReLU(),
nn.Conv2d(latent_channels, 64, kernel_size=3, padding=1),
nn.ReLU(),
nn.Conv2d(64, 64, kernel_size=3, padding=1),
nn.ReLU(),
nn.ConvTranspose2d(64, 32, kernel_size=2, stride=2), # 14x14
nn.ReLU(),
nn.Conv2d(32, 32, kernel_size=3, padding=1),
nn.ReLU(),
nn.ConvTranspose2d(32, 16, kernel_size=2, stride=2), # 28x28
nn.ReLU(),
nn.Conv2d(16, 16, kernel_size=3, padding=1),
nn.ReLU(),
nn.Conv2d(16, input_channels, kernel_size=3, padding=1),
nn.Sigmoid(),
)
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""Full autoencoder forward pass."""
z = self.encoder(x)
x_recon = self.decoder(z)
return x_recon
def encode(self, x: torch.Tensor) -> torch.Tensor:
"""Encode input to latent space."""
return self.encoder(x)
def decode(self, z: torch.Tensor) -> torch.Tensor:
"""Decode from latent space to image space."""
return self.decoder(z)
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