vae_conv_mnist.py

from __future__ import print_function
import argparse
import torch
import torch.utils.data
from torch import nn, optim
from torch.autograd import Variable
from torch.nn import functional as F
from torchvision import datasets, transforms
from torchvision.utils import save_image
import json

import datetime
import os

import vae_conv_model_mnist

parser = argparse.ArgumentParser(description='PyTorch MNIST Example')
parser.add_argument('--batch-size', type=int, default=64, metavar='N',
                    help='input batch size for training (default: 64)')
parser.add_argument('--dataroot', help='path to dataset')
parser.add_argument('--epochs', type=int, default=10, metavar='N',
                    help='number of epochs to train (default: 2)')
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='how many batches to wait before logging training status')
parser.add_argument('--outf', default='.', help='folder to output images and model checkpoints')
args = parser.parse_args()
args.cuda = not args.no_cuda and torch.cuda.is_available()


torch.manual_seed(args.seed)
if args.cuda:
    torch.cuda.manual_seed(args.seed)

print("cuda", args.cuda, args.no_cuda, torch.cuda.is_available())

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

model = vae_conv_model_mnist.VAE(20)
model.have_cuda = args.cuda
if args.cuda:
    model.cuda()

print(model)

optimizer = optim.Adam(model.parameters(), lr=1e-3)

# Reconstruction + KL divergence losses summed over all elements and batch
def loss_function(recon_x, x, mu, logvar):
    # print(recon_x.size(), x.size())
    BCE = F.binary_cross_entropy(recon_x.view(-1, 784), x.view(-1, 784), size_average=False)
    # 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
    return BCE + 3 * KLD

outf = args.outf + "_" + datetime.datetime.now().strftime("%Y-%m-%d_%H:%M:%S")
if not os.path.exists(outf):
    os.makedirs(outf)
outf_samples = outf + "/samples";
os.makedirs(outf_samples)

with open(outf + '/model.txt', 'w') as f1:
    print(model, file=f1)

def train(epoch):
    model.train()
    train_loss = 0
    for batch_idx, (data, _) in enumerate(train_loader):
        data = Variable(data)
        if args.cuda:
            data = data.cuda()
        optimizer.zero_grad()

        recon_batch, mu, logvar = model(data)

        loss = loss_function(recon_batch, data, mu, logvar)
        loss.backward()
        train_loss += loss.data[0]
        optimizer.step()
        if batch_idx % args.log_interval == 0:
            print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
                epoch, batch_idx * len(data), len(train_loader.dataset),
                100. * batch_idx / len(train_loader),
                loss.data[0] / len(data)))
    print('====> Epoch: {} Average loss: {:.4f}'.format(
          epoch, train_loss / len(train_loader.dataset)))

    if epoch % 10 == 0:
        torch.save(model.state_dict(), '%s/vae_epoch_%d.pth' % (outf, epoch))

def test(epoch):
    model.eval()
    test_loss = 0
    for i, (data, _) in enumerate(test_loader):
        if args.cuda:
            data = data.cuda()
        data = Variable(data, volatile=True)
        recon_batch, mu, logvar = model(data)
        test_loss += loss_function(recon_batch, data, mu, logvar).data[0]
        if i == 0:
            n = min(data.size(0), 16)
            comparison = torch.cat([data[:n],
                                  recon_batch.view(args.batch_size, 1, 28, 28)[:n]])
            save_image(comparison.data.cpu(), '%s/%03d.png' % (outf_samples, epoch), nrow=n)
    test_loss /= len(test_loader.dataset)
    print('====> Test set loss: {:.4f}'.format(test_loss))

for epoch in range(1, args.epochs + 1):
    train(epoch)
    test(epoch)