vae.py

import argparse
import torch
import torch.utils.data
from torch import nn, optim
from torch.nn import functional as F
from torchvision import datasets, transforms
from torchvision.utils import save_image


# parser = argparse.ArgumentParser(description='VAE MNIST Example')
# parser.add_argument('--batch-size', type=int, default=128, metavar='N',
#                     help='input batch size for training (default: 128)')
# parser.add_argument('--epochs', type=int, default=10, metavar='N',
#                     help='number of epochs to train (default: 10)')
# parser.add_argument('--no-cuda', action='store_true', default=False,
#                     help='enables CUDA training')
# parser.add_argument('--seed', type=int, default=1, metavar='S',
#                     help='random seed (default: 1)')
# parser.add_argument('--log-interval', type=int, default=10, metavar='N',
#                     help='')
# args = parser.parse_args()
#
# torch.manual_seed(args.seed)

class VAE(nn.Module):
    def __init__(self):
        super(VAE, self).__init__()

        self.fc1 = nn.Linear(784, 400)
        self.fc21 = nn.Linear(400, 20)
        self.fc22 = nn.Linear(400, 20)
        self.fc3 = nn.Linear(20, 400)
        self.fc4 = nn.Linear(400, 784)

    def encode(self, x):
        h1 = F.relu(self.fc1(x))
        return self.fc21(h1), self.fc22(h1)

    def reparameterize(self, mu, logvar):
        std = torch.exp(0.5*logvar)
        eps = torch.randn_like(std)
        return mu + eps*std

    def decode(self, z):
        h3 = F.relu(self.fc3(z))
        return torch.sigmoid(self.fc4(h3))

    def forward(self, x):
        mu, logvar = self.encode(x.view(-1, 784))
        z = self.reparameterize(mu, logvar)
        return self.decode(z), mu, logvar


class VAETrainer(object):
    def __init__(self, batch_size, log_interval):
        """
        :param batch_size:
        :param log_interval: How many batches to wait before logging training status.
        """

        use_cuda = torch.cuda.is_available()
        kwargs = {'num_workers': 1, 'pin_memory': True} if use_cuda else {}
        self.train_loader = torch.utils.data.DataLoader(
            datasets.MNIST('../data', train=True, download=True,
                           transform=transforms.ToTensor()),
            batch_size=batch_size, shuffle=True, **kwargs)
        self.test_loader = torch.utils.data.DataLoader(
            datasets.MNIST('../data', train=False, transform=transforms.ToTensor()),
            batch_size=batch_size, shuffle=True, **kwargs)

        self.device = torch.device("cuda" if use_cuda else "cpu")
        self.model = VAE().to(self.device)
        self.optimizer = optim.Adam(self.model.parameters(), lr=1e-3)

        self.batch_size = batch_size
        self.log_interval = log_interval

    def run(self, epochs):
        for epoch in range(1, epochs + 1):
            self.train(epoch)
            self.test(epoch)
            with torch.no_grad():
                sample = torch.randn(64, 20).to(self.device)
                sample = self.model.decode(sample).cpu()
                save_image(sample.view(64, 1, 28, 28),
                           'results/sample_' + str(epoch) + '.png')

    def train(self, epoch):
        self.model.train()
        train_loss = 0
        for batch_idx, (data, _) in enumerate(self.train_loader):
            data = data.to(self.device)
            self.optimizer.zero_grad()
            recon_batch, mu, logvar = self.model(data)
            loss = self.loss_function(recon_batch, data, mu, logvar)
            loss.backward()
            train_loss += loss.item()
            self.optimizer.step()
            if batch_idx % self.log_interval == 0:
                print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
                    epoch, batch_idx * len(data), len(self.train_loader.dataset),
                           100. * batch_idx / len(self.train_loader),
                           loss.item() / len(data)))

        print('====> Epoch: {} Average loss: {:.4f}'.format(
            epoch, train_loss / len(self.train_loader.dataset)))

    def test(self, epoch):
        self.model.eval()
        test_loss = 0
        with torch.no_grad():
            for i, (data, _) in enumerate(self.test_loader):
                data = data.to(self.device)
                recon_batch, mu, logvar = self.model(data)
                test_loss += self.loss_function(recon_batch, data, mu, logvar).item()
                if i == 0:
                    n = min(data.size(0), 8)
                    comparison = torch.cat([data[:n],
                                          recon_batch.view(self.batch_size, 1, 28, 28)[:n]])
                    save_image(comparison.cpu(),
                             'results/reconstruction_' + str(epoch) + '.png', nrow=n)

        test_loss /= len(self.test_loader.dataset)
        print('====> Test set loss: {:.4f}'.format(test_loss))

    # Reconstruction + KL divergence losses summed over all elements and batch
    def loss_function(self, recon_x, x, mu, logvar):
        BCE = F.binary_cross_entropy(recon_x, x.view(-1, 784), reduction='sum')

        # see Appendix B from VAE paper:
        # Kingma and Welling. Auto-Encoding Variational Bayes. ICLR, 2014
        # https://arxiv.org/abs/1312.6114
        # 0.5 * sum(1 + log(sigma^2) - mu^2 - sigma^2)
        KLD = -0.5 * torch.sum(1 + logvar - mu.pow(2) - logvar.exp())

        return BCE + KLD


if __name__ == "__main__":
    trainer = VAETrainer(batch_size=16, log_interval=1)
    trainer.run(epochs=10)