vae.py

"""MNIST digit classificatin."""
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F


class Encoder(nn.Module):
    def __init__(self, image_dim, latent_dim):
        super().__init__()
        self.image_dim = image_dim
        self.latent_dim = latent_dim
        self.cnn = nn.Sequential(
            nn.Conv2d(in_channels=1, out_channels=16, kernel_size=5, padding=2),
            nn.MaxPool2d(kernel_size=2),
            nn.Conv2d(in_channels=16, out_channels=32, kernel_size=5, stride=1, padding=2),
            nn.MaxPool2d(kernel_size=2),
            nn.Flatten(1, -1),
        )
        self.l_mu = nn.Linear(1568, np.product(self.latent_dim))
        self.l_sigma = nn.Linear(1568, np.product(self.latent_dim))

    def forward(self, x):
        x = x.reshape((-1, 1, *self.image_dim))
        x = self.cnn(x)
        mu = self.l_mu(x)
        sigma = self.l_sigma(x)
        return mu, sigma


class Decoder(nn.Module):
    def __init__(self, image_dim, latent_dim):
        super().__init__()
        self.image_dim = image_dim
        self.latent_dim = latent_dim
        self.cnn = nn.Sequential(
            nn.Conv2d(in_channels=1, out_channels=16, kernel_size=5, padding=2),
            nn.MaxPool2d(kernel_size=2),
            nn.Conv2d(in_channels=16, out_channels=32, kernel_size=5, stride=1, padding=2),
            nn.MaxPool2d(kernel_size=2),
            nn.Flatten(1, -1),
            nn.Linear(288, np.product(self.image_dim)),
            nn.Sigmoid(),
        )

    def forward(self, c):
        c = c.reshape((-1, 1, *self.latent_dim))
        x = self.cnn(c)        
        return x


class VAE(nn.Module):
    def __init__(self, image_dim=(28, 28), latent_dim=(14, 14)):
        super().__init__()
        self.image_dim = image_dim
        self.encoder = Encoder(image_dim, latent_dim)
        self.decoder = Decoder(image_dim, latent_dim)

    def forward(self, x):
        x = x.reshape((-1, 1, *self.image_dim))
        mu, sigma = self.encoder(x)
        c = mu + sigma * torch.randn_like(sigma)
        xhat = self.decoder(c)
        return xhat, mu, sigma