VAE_CNN_BCEloss.py

import os
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

os.environ['CUDA_VISIBLE_DEVICES'] = '3'
# changed configuration to this instead of argparse for easier interaction
CUDA = True
SEED = 1
BATCH_SIZE = 128
LOG_INTERVAL = 10
EPOCHS = 10
no_of_sample = 10

# connections through the autoencoder bottleneck
# in the pytorch VAE example, this is 20
ZDIMS = 20

torch.manual_seed(SEED)
if CUDA:
    torch.cuda.manual_seed(SEED)

# DataLoader instances will load tensors directly into GPU memory
kwargs = {'num_workers': 1, 'pin_memory': True} if CUDA else {}

# Download or load downloaded MNIST dataset
# shuffle data at every epoch
train_loader = torch.utils.data.DataLoader(datasets.MNIST('./mnist', train=True, download=True,transform=transforms.ToTensor()),
    batch_size=BATCH_SIZE, shuffle=True, **kwargs)

# Same for test data
test_loader = torch.utils.data.DataLoader(datasets.MNIST('./mnist', train=False, transform=transforms.ToTensor()),
    batch_size=BATCH_SIZE, shuffle=True, **kwargs)


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

        self.conv1 = nn.Conv2d(in_channels=1, out_channels=64, kernel_size=(4, 4), padding=(15, 15),
                               stride=2)  # This padding keeps the size of the image same, i.e. same padding
        self.conv2 = nn.Conv2d(in_channels=64, out_channels=128, kernel_size=(4, 4), padding=(15, 15), stride=2)
        self.fc11 = nn.Linear(in_features=128 * 28 * 28, out_features=1024)
        self.fc12 = nn.Linear(in_features=1024, out_features=ZDIMS)

        self.fc21 = nn.Linear(in_features=128 * 28 * 28, out_features=1024)
        self.fc22 = nn.Linear(in_features=1024, out_features=ZDIMS)
        self.relu = nn.ReLU()

        # For decoder

        # For mu
        self.fc1 = nn.Linear(in_features=20, out_features=1024)
        self.fc2 = nn.Linear(in_features=1024, out_features=7 * 7 * 128)
        self.conv_t1 = nn.ConvTranspose2d(in_channels=128, out_channels=64, kernel_size=4, padding=1, stride=2)
        self.conv_t2 = nn.ConvTranspose2d(in_channels=64, out_channels=1, kernel_size=4, padding=1, stride=2)



    def encode(self, x: Variable) -> (Variable, Variable):

        x = x.view(-1, 1, 28, 28)
        x = F.elu(self.conv1(x))
        x = F.elu(self.conv2(x))
        x = x.view(-1, 128 * 28 * 28)

        mu_z = F.elu(self.fc11(x))
        mu_z = self.fc12(mu_z)

        logvar_z = F.elu(self.fc21(x))
        logvar_z = self.fc22(logvar_z)

        return mu_z, logvar_z


    def reparameterize(self, mu: Variable, logvar: Variable) -> Variable:


        if self.training:
            # multiply log variance with 0.5, then in-place exponent
            # yielding the standard deviation

            sample_z = []
            for _ in range(no_of_sample):
                std = logvar.mul(0.5).exp_()  # type: Variable
                eps = Variable(std.data.new(std.size()).normal_())
                sample_z.append(eps.mul(std).add_(mu))

            return sample_z

        else:
            # During inference, we simply spit out the mean of the
            # learned distribution for the current input.  We could
            # use a random sample from the distribution, but mu of
            # course has the highest probability.
            return mu

    def decode(self, z: Variable) -> Variable:

        x = F.elu(self.fc1(z))
        x = F.elu(self.fc2(x))
        x = x.view(-1, 128, 7, 7)
        x = F.relu(self.conv_t1(x))
        x = F.sigmoid(self.conv_t2(x))

        return x.view(-1, 784)


    def forward(self, x: Variable) -> (Variable, Variable, Variable):
        mu, logvar = self.encode(x.view(-1, 784))
        z = self.reparameterize(mu, logvar)
        if self.training:
            return [self.decode(z) for z in z], mu, logvar
        else:
            return self.decode(z), mu, logvar
        # return self.decode(z), mu, logvar

    def loss_function(self, recon_x, x, mu, logvar) -> Variable:
        # how well do input x and output recon_x agree?

        if self.training:
            BCE = 0
            for recon_x_one in recon_x:
                BCE += F.binary_cross_entropy(recon_x_one, x.view(-1, 784))
            BCE /= len(recon_x)
        else:
            BCE = F.binary_cross_entropy(recon_x, x.view(-1, 784))

        # KLD is Kullback–Leibler divergence -- how much does one learned
        # distribution deviate from another, in this specific case the
        # learned distribution from the unit Gaussian

        # see Appendix B from VAE paper:
        # Kingma and Welling. Auto-Encoding Variational Bayes. ICLR, 2014
        # https://arxiv.org/abs/1312.6114
        # - D_{KL} = 0.5 * sum(1 + log(sigma^2) - mu^2 - sigma^2)
        # note the negative D_{KL} in appendix B of the paper
        KLD = -0.5 * torch.sum(1 + logvar - mu.pow(2) - logvar.exp())
        # Normalise by same number of elements as in reconstruction
        KLD /= BATCH_SIZE * 784


        return BCE + KLD


model = VAE()
if CUDA:
    model.cuda()





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


def train(epoch):
    # toggle model to train mode
    model.train()
    train_loss = 0
    # in the case of MNIST, len(train_loader.dataset) is 60000
    # each `data` is of BATCH_SIZE samples and has shape [128, 1, 28, 28]
    for batch_idx, (data, _) in enumerate(train_loader):
        data = Variable(data)
        if CUDA:
            data = data.cuda()
        optimizer.zero_grad()

        # push whole batch of data through VAE.forward() to get recon_loss
        recon_batch, mu, logvar = model(data)
        # calculate scalar loss
        loss = model.loss_function(recon_batch, data, mu, logvar)
        # calculate the gradient of the loss w.r.t. the graph leaves
        # i.e. input variables -- by the power of pytorch!
        loss.backward()
        train_loss += loss.data[0]
        optimizer.step()
        if batch_idx % 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)))


def test(epoch):
    model.eval()
    test_loss = 0

    # each data is of BATCH_SIZE (default 128) samples
    for i, (data, _) in enumerate(test_loader):
        if CUDA:
            # make sure this lives on the GPU
            data = data.cuda()

        # we're only going to infer, so no autograd at all required: volatile=True
        data = Variable(data, volatile=True)
        recon_batch, mu, logvar = model(data)
        test_loss += model.loss_function(recon_batch, data, mu, logvar).data[0]
        if i == 0:
            n = min(data.size(0), 8)
            # for the first 128 batch of the epoch, show the first 8 input digits
            # with right below them the reconstructed output digits
            comparison = torch.cat([data[:n],
                                    recon_batch.view(BATCH_SIZE, 1, 28, 28)[:n]])
            save_image(comparison.data.cpu(),
                       './mnist/reconstruction_' + str(epoch) + '.png', nrow=n)

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


if __name__ == "__main__":
    for epoch in range(1, EPOCHS + 1):
        train(epoch)
        test(epoch)

        # 64 sets of random ZDIMS-float vectors, i.e. 64 locations / MNIST
        # digits in latent space
        sample = Variable(torch.randn(64, ZDIMS))
        if CUDA:
            sample = sample.cuda()
        sample = model.decode(sample).cpu()

        # save out as an 8x8 matrix of MNIST digits
        # this will give you a visual idea of how well latent space can generate things
        # that look like digits
        save_image(sample.data.view(64, 1, 28, 28),'./mnist/reconstruction' + str(epoch) + '.png')