import os
import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision
from torchvision import transforms
from torchvision.utils import save_image
# Device configuration
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
# Create a directory if not exists
sample_dir = 'samples'
if not os.path.exists(sample_dir):
os.makedirs(sample_dir)
# Hyper-parameters
image_size = 784
h_dim = 400
z_dim = 20
num_epochs = 15
batch_size = 128
learning_rate = 1e-3
# MNIST dataset
dataset = torchvision.datasets.MNIST(root='../../data',
train=True,
transform=transforms.ToTensor(),
download=True)
# Data loader
data_loader = torch.utils.data.DataLoader(dataset=dataset,
batch_size=batch_size,
shuffle=True)
# VAE model
class VAE(nn.Module):
def __init__(self, image_size=784, h_dim=400, z_dim=20):
super(VAE, self).__init__()
self.fc1 = nn.Linear(image_size, h_dim)
self.fc2 = nn.Linear(h_dim, z_dim)
self.fc3 = nn.Linear(h_dim, z_dim)
self.fc4 = nn.Linear(z_dim, h_dim)
self.fc5 = nn.Linear(h_dim, image_size)
def encode(self, x):
h = F.relu(self.fc1(x))
return self.fc2(h), self.fc3(h)
def reparameterize(self, mu, log_var):
std = torch.exp(log_var/2)
eps = torch.randn_like(std)
return mu + eps * std
def decode(self, z):
h = F.relu(self.fc4(z))
return F.sigmoid(self.fc5(h))
def forward(self, x):
mu, log_var = self.encode(x)
z = self.reparameterize(mu, log_var)
x_reconst = self.decode(z)
return x_reconst, mu, log_var
model = VAE().to(device)
optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)
# Start training
for epoch in range(num_epochs):
for i, (x, _) in enumerate(data_loader):
# Forward pass
x = x.to(device).view(-1, image_size)
x_reconst, mu, log_var = model(x)
# Compute reconstruction loss and kl divergence
# For KL divergence, see Appendix B in VAE paper or http://yunjey47.tistory.com/43
reconst_loss = F.binary_cross_entropy(x_reconst, x, size_average=False)
kl_div = - 0.5 * torch.sum(1 + log_var - mu.pow(2) - log_var.exp())
# Backprop and optimize
loss = reconst_loss + kl_div
optimizer.zero_grad()
loss.backward()
optimizer.step()
if (i+1) % 10 == 0:
print ("Epoch[{}/{}], Step [{}/{}], Reconst Loss: {:.4f}, KL Div: {:.4f}"
.format(epoch+1, num_epochs, i+1, len(data_loader), reconst_loss.item(), kl_div.item()))
with torch.no_grad():
# Save the sampled images
z = torch.randn(batch_size, z_dim).to(device)
out = model.decode(z).view(-1, 1, 28, 28)
save_image(out, os.path.join(sample_dir, 'sampled-{}.png'.format(epoch+1)))
# Save the reconstructed images
out, _, _ = model(x)
x_concat = torch.cat([x.view(-1, 1, 28, 28), out.view(-1, 1, 28, 28)], dim=3)
save_image(x_concat, os.path.join(sample_dir, 'reconst-{}.png'.format(epoch+1)))
