/
models.py
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models.py
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import torch
import torch.nn as nn
import torch.nn.functional as F
import utils
# CelebA (VAE)
# Input 64x64x3.
# Adam 1e-4
# Encoder Conv 32x4x4 (stride 2), 32x4x4 (stride 2), 64x4x4 (stride 2),
# 64x4x4 (stride 2), FC 256. ReLU activation.
# Latents 32
# Decoder Deconv reverse of encoder. ReLU activation. Gaussian.
class BetaVAE(nn.Module):
def __init__(self, latent_size=32, beta=1):
super(BetaVAE, self).__init__()
self.latent_size = latent_size
self.beta = beta
# encoder
self.encoder = nn.Sequential(
self._conv(3, 32),
self._conv(32, 32),
self._conv(32, 64),
self._conv(64, 64),
)
self.fc_mu = nn.Linear(256, latent_size)
self.fc_var = nn.Linear(256, latent_size)
# decoder
self.decoder = nn.Sequential(
self._deconv(64, 64),
self._deconv(64, 32),
self._deconv(32, 32, 1),
self._deconv(32, 3),
nn.Sigmoid()
)
self.fc_z = nn.Linear(latent_size, 256)
def encode(self, x):
x = self.encoder(x)
x = x.view(-1, 256)
return self.fc_mu(x), self.fc_var(x)
def sample(self, mu, logvar):
std = torch.exp(0.5*logvar) # e^(1/2 * log(std^2))
eps = torch.randn_like(std) # random ~ N(0, 1)
return eps.mul(std).add_(mu)
def decode(self, z):
z = self.fc_z(z)
z = z.view(-1, 64, 2, 2)
return self.decoder(z)
def forward(self, x):
mu, logvar = self.encode(x)
z = self.sample(mu, logvar)
rx = self.decode(z)
return rx, mu, logvar
def _conv(self, in_channels, out_channels):
return nn.Sequential(
nn.Conv2d(
in_channels, out_channels,
kernel_size=4, stride=2
),
nn.BatchNorm2d(out_channels),
nn.ReLU()
)
# out_padding is used to ensure output size matches EXACTLY of conv2d;
# it does not actually add zero-padding to output :)
def _deconv(self, in_channels, out_channels, out_padding=0):
return nn.Sequential(
nn.ConvTranspose2d(
in_channels, out_channels,
kernel_size=4, stride=2, output_padding=out_padding
),
nn.BatchNorm2d(out_channels),
nn.ReLU()
)
def loss(self, recon_x, x, mu, logvar):
# reconstruction losses are summed over all elements and batch
recon_loss = F.binary_cross_entropy(recon_x, x, 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)
kl_diverge = -0.5 * torch.sum(1 + logvar - mu.pow(2) - logvar.exp())
return (recon_loss + self.beta * kl_diverge) / x.shape[0] # divide total loss by batch size
def save_model(self, file_path, num_to_keep=1):
utils.save(self, file_path, num_to_keep)
def load_model(self, file_path):
utils.restore(self, file_path)
def load_last_model(self, dir_path):
return utils.restore_latest(self, dir_path)
class DFCVAE(BetaVAE):
def __init__(self, latent_size=100, beta=1):
super(DFCVAE, self).__init__()
self.latent_size = latent_size
self.beta = beta
# encoder
self.e1 = self._conv(3, 32)
self.e2 = self._conv(32, 64)
self.e3 = self._conv(64, 128)
self.e4 = self._conv(128, 256)
self.fc_mu = nn.Linear(4096, latent_size)
self.fc_var = nn.Linear(4096, latent_size)
# decoder
self.d1 = self._upconv(256, 128)
self.d2 = self._upconv(128, 64)
self.d3 = self._upconv(64, 32)
self.d4 = self._upconv(32, 3)
self.fc_z = nn.Linear(latent_size, 4096)
def encode(self, x):
x = F.leaky_relu(self.e1(x))
x = F.leaky_relu(self.e2(x))
x = F.leaky_relu(self.e3(x))
x = F.leaky_relu(self.e4(x))
x = x.view(-1, 4096)
return self.fc_mu(x), self.fc_var(x)
def sample(self, mu, logvar):
std = torch.exp(0.5*logvar) # e^(1/2 * log(std^2))
eps = torch.randn_like(std) # random ~ N(0, 1)
return eps.mul(std).add_(mu)
def decode(self, z):
z = self.fc_z(z)
z = z.view(-1, 256, 4, 4)
z = F.leaky_relu(self.d1(F.interpolate(z, scale_factor=2)))
z = F.leaky_relu(self.d2(F.interpolate(z, scale_factor=2)))
z = F.leaky_relu(self.d3(F.interpolate(z, scale_factor=2)))
z = F.leaky_relu(self.d4(F.interpolate(z, scale_factor=2)))
return torch.sigmoid(z)
def forward(self, x):
mu, logvar = self.encode(x)
z = self.sample(mu, logvar)
rx = self.decode(z)
return rx, mu, logvar
def _conv(self, in_channels, out_channels):
return nn.Sequential(
nn.Conv2d(
in_channels, out_channels,
kernel_size=4, stride=2, padding=1
),
nn.BatchNorm2d(out_channels),
)
def _upconv(self, in_channels, out_channels):
return nn.Sequential(
nn.ReplicationPad2d(1),
nn.Conv2d(
in_channels, out_channels,
kernel_size=3, stride=1
),
nn.BatchNorm2d(out_channels),
)