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Beta_VAE_Conv.py
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Beta_VAE_Conv.py
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import os
import math
import torch
import random
import numpy as np
import torch.nn as nn
import torch.utils.data
import torch.optim as optim
import torch.nn.init as init
import matplotlib.pyplot as plt
import torch.nn.functional as F
from torch.autograd import Variable
from torch.utils.data import DataLoader
from torchvision import datasets, transforms
from torch.utils.data import SubsetRandomSampler
class FactorVAE1(nn.Module):
def __init__(self, z_dim=2):
super(FactorVAE1, self).__init__()
self.z_dim = z_dim
self.encode = nn.Sequential(
nn.Conv2d(1, 28, 4, 2, 1),
nn.ReLU(True),
nn.Conv2d(28, 28, 4, 2, 1),
nn.ReLU(True),
nn.Conv2d(28, 56, 4, 2, 1),
nn.ReLU(True),
nn.Conv2d(56, 118, 4, 2, 1),
nn.ReLU(True),
nn.Conv2d(118, 2 * z_dim, 1),
)
self.decode = nn.Sequential(
nn.Conv2d(z_dim, 118, 1),
nn.ReLU(True),
nn.ConvTranspose2d(118, 118, 4, 2, 1),
nn.ReLU(True),
nn.ConvTranspose2d(118, 56, 4, 2, 1),
nn.ReLU(True),
nn.ConvTranspose2d(56, 28, 4, 1),
nn.ReLU(True),
nn.ConvTranspose2d(28, 28, 4, 2, 1),
nn.ReLU(True),
nn.ConvTranspose2d(28, 1, 4, 2, 1),
nn.Sigmoid(),
)
self.weight_init()
def weight_init(self, mode='normal'):
initializer = normal_init
for block in self._modules:
for m in self._modules[block]:
initializer(m)
def reparametrize(self, mu, logvar):
std = logvar.mul(0.5).exp_()
eps = std.data.new(std.size()).normal_()
return eps.mul(std).add_(mu)
def forward(self, x, no_enc=False, no_dec=False):
if no_enc:
z = Variable(torch.randn(100, z_dim, 1, 1), requires_grad=False).to(device)
return self.decode(z).view(x.size())
stats = self.encode(x)
mu = stats[:, :self.z_dim]
logvar = stats[:, self.z_dim:]
z = self.reparametrize(mu, logvar)
if no_dec:
return z.squeeze()
else:
x_recon = self.decode(z).view(x.size())
return x_recon, mu, logvar, z.squeeze()
def normal_init(m):
if isinstance(m, (nn.Linear, nn.Conv2d)):
init.normal(m.weight, 0, 0.02)
if m.bias is not None:
m.bias.data.fill_(0)
elif isinstance(m, (nn.BatchNorm1d, nn.BatchNorm2d)):
m.weight.data.fill_(1)
if m.bias is not None:
m.bias.data.fill_(0)
def recon_loss(x_recon, x):
n = x.size(0)
loss = F.binary_cross_entropy(x_recon, x, size_average=False).div(n)
return loss
def kl_divergence(mu, logvar):
kld = -0.5 * (1 + logvar - mu ** 2 - logvar.exp()).sum(1).mean()
return kld
def convert_to_display(samples):
cnt, height, width = int(math.floor(math.sqrt(samples.shape[0]))), samples.shape[1], samples.shape[2]
samples = np.transpose(samples, axes=[1, 0, 2, 3])
samples = np.reshape(samples, [height, cnt, cnt, width])
samples = np.transpose(samples, axes=[1, 0, 2, 3])
samples = np.reshape(samples, [height*cnt, width*cnt])
return samples
use_cuda = torch.cuda.is_available()
device = 'cuda' if use_cuda else 'cpu'
print('The code is running over', device)
max_iter = int(3000)
batch_size = 100
z_dim = 2
lr_D = 0.001
beta1_D = 0.9
beta2_D = 0.999
gamma = 1e3
training_set = datasets.MNIST('../data', train=True, download=True, transform=transforms.ToTensor())
test_set = datasets.MNIST('../data', train=False, download=True, transform=transforms.ToTensor())
data_loader = DataLoader(training_set, batch_size=batch_size, shuffle=True, num_workers=3)
test_loader = DataLoader(test_set, batch_size=500, shuffle=True, num_workers=3)
VAE = FactorVAE1().to(device)
optim_VAE = optim.Adam(VAE.parameters(), lr=lr_D, betas=(beta1_D, beta2_D))
Beta = 2
for epoch in range(20):
train_loss = 0
train_info_loss = 0
for batch_idx, (x_true,_) in enumerate(data_loader):
x_true = x_true.to(device)
x_recon, mu, logvar, z = VAE(x_true)
vae_recon_loss = recon_loss(x_recon, x_true)
vae_kld = kl_divergence(mu, logvar)
vae_loss = vae_recon_loss + Beta*vae_kld
train_loss += vae_loss.item()
optim_VAE.zero_grad()
vae_loss.backward(retain_graph=True)
optim_VAE.step()
if batch_idx % 100 == 0:
print('Train Epoch: {} [{}/{} ({:.0f}%)] \t Total Loss: {:.6f} \t KL: {:.6f} '.format(epoch, batch_idx * len(x_true), len(data_loader.dataset),100. * batch_idx / len(data_loader),vae_loss.item(), vae_kld.item()))
if batch_idx % 1000 == 0:
samples = VAE(x_true, no_enc=True)
samples = samples.permute(0, 2, 3, 1).contiguous().cpu().data.numpy()
plt.imshow(convert_to_display(samples), cmap='Greys_r')
plt.show()
print('====> Epoch: {} Average loss: {:.4f}'.format(epoch, train_loss / len(data_loader.dataset)))
print('====> Epoch: {} Average loss: {:.4f}'.format(epoch, train_info_loss / len(data_loader.dataset)))
if z_dim == 2:
batch_size_test = 500
z_list, label_list = [], []
for i in range(20):
x_test, y_test = iter(test_loader).next()
x_test = Variable(x_test, requires_grad=False).to(device)
z = VAE(x_test, no_dec=True)
z_list.append(z.cpu().data.numpy())
label_list.append(y_test.numpy())
z = np.concatenate(z_list, axis=0)
label = np.concatenate(label_list)
plt.scatter(z[:, 0], z[:, 1], c=label)
plt.show()