vae_gumbel_softmax.py

# Code to implement VAE-gumple_softmax in pytorch
# author: Devinder Kumar (devinder.kumar@uwaterloo.ca)
# The code has been modified from pytorch example vae code and inspired by the origianl tensorflow implementation of gumble-softmax by Eric Jang.

from __future__ import print_function
import argparse
import numpy as np

import torch
import torch.nn.functional as F
from torch.autograd import Variable
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('--temp', type=float, default=1.0, metavar='S',
                    help='tau(temperature) (default: 1.0)')
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')



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)


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

def sample_gumbel(shape, eps=1e-20):
    U = torch.rand(shape).cuda()
    return -Variable(torch.log(-torch.log(U + eps) + eps))

def gumbel_softmax_sample(logits, temperature):
    y = logits + sample_gumbel(logits.size())
    return F.softmax(y / temperature, dim=-1)

def gumbel_softmax(logits, temperature):
    """
    ST-gumple-softmax
    input: [*, n_class]
    return: flatten --> [*, n_class] an one-hot vector
    """
    y = gumbel_softmax_sample(logits, temperature)
    shape = y.size()
    _, ind = y.max(dim=-1)
    y_hard = torch.zeros_like(y).view(-1, shape[-1])
    y_hard.scatter_(1, ind.view(-1, 1), 1)
    y_hard = y_hard.view(*shape)
    y_hard = (y_hard - y).detach() + y
    return y_hard.view(-1,latent_dim*categorical_dim)



class VAE_gumbel(nn.Module):

    def __init__(self,temp):
        super(VAE_gumbel, self).__init__()

        self.fc1 = nn.Linear(784, 512)
        self.fc2 = nn.Linear(512,256)
        self.fc3 = nn.Linear(256, latent_dim*categorical_dim)
    
        self.fc4 = nn.Linear(latent_dim*categorical_dim, 256)
        self.fc5 = nn.Linear(256,512)
        self.fc6 = nn.Linear(512,784)

        self.relu = nn.ReLU()
        self.sigmoid = nn.Sigmoid()

    def encode(self, x):
        h1 = self.relu(self.fc1(x))
        h2 = self.relu(self.fc2(h1))
        return self.relu(self.fc3(h2))


    def decode(self, z):
        h4 = self.relu(self.fc4(z))
        h5 = self.relu(self.fc5(h4))
        return self.sigmoid(self.fc6(h5))

    def forward(self, x,temp):
        q = self.encode(x.view(-1, 784))
        q_y = q.view(q.size(0),latent_dim,categorical_dim)
        z = gumbel_softmax(q_y,temp)
        return self.decode(z),F.softmax(q)

latent_dim = 20
categorical_dim = 10 # one-of-K vector

temp_min = 0.5
ANNEAL_RATE = 0.00003


model = VAE_gumbel(args.temp)
if args.cuda:
    model.cuda()
optimizer = optim.Adam(model.parameters(), lr=1e-3)


# Reconstruction + KL divergence losses summed over all elements and batch
def loss_function(recon_x, x,qy):
    BCE = F.binary_cross_entropy(recon_x, x.view(-1, 784), size_average=False)

    log_qy = torch.log(qy+1e-20)
    g = Variable(torch.log(torch.Tensor([1.0/categorical_dim])).cuda())
    KLD = torch.sum(qy*(log_qy - g),dim=-1).mean()

    return BCE + KLD


def train(epoch):
    model.train()
    train_loss = 0
    temp = args.temp
    for batch_idx, (data, _) in enumerate(train_loader):
        data = Variable(data)
        if args.cuda:
            data = data.cuda()
        optimizer.zero_grad()
        recon_batch,qy  = model(data,temp)
        loss = loss_function(recon_batch, data,qy)
        loss.backward()
        train_loss += loss.data[0]
        optimizer.step()
        if batch_idx % 100 == 1:
            temp = np.maximum(temp*np.exp(-ANNEAL_RATE*batch_idx),temp_min)

        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)))


def test(epoch):
    model.eval()
    test_loss = 0
    temp = args.temp
    for i, (data, _) in enumerate(test_loader):
        if args.cuda:
            data = data.cuda()
        data = Variable(data, volatile=True)
        recon_batch,qy = model(data,temp)
        test_loss += loss_function(recon_batch, data,qy).data[0]
        if i % 100 == 1:
            temp = np.maximum(temp*np.exp(-ANNEAL_RATE*i),temp_min)
        if i == 0:
            n = min(data.size(0), 8)
            comparison = torch.cat([data[:n],
                                  recon_batch.view(args.batch_size, 1, 28, 28)[:n]])
            save_image(comparison.data.cpu(),
                     'results/reconstruction_' + str(epoch) + '.png', nrow=n)

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


def run():
    for epoch in range(1, args.epochs + 1):
        train(epoch)
        test(epoch)
        sample = Variable(torch.randn(64, 200))
        if args.cuda:
            sample = sample.cuda()
        sample = model.decode(sample).cpu()
        save_image(sample.data.view(64, 1, 28, 28),
                   'results/sample_' + str(epoch) + '.png')



if __name__ == '__main__':
    run()