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model.py
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model.py
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"""implementing models"""
import torch.nn as nn
import torch.nn.init as init
from torch.autograd import Variable
def reparametrize(mu, logvar):
std = logvar.div(2).exp()
eps = Variable(std.data.new(std.size()).normal_())
return mu + std*eps
class View(nn.Module):
def __init__(self, size):
super(View, self).__init__()
self.size = size
def forward(self, tensor):
return tensor.view(self.size)
def kaiming_init(m):
if isinstance(m, (nn.Linear, nn.Conv2d)):
init.kaiming_normal(m.weight)
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)
class base_model(nn.Module):
def __init__(self, z_dim, nc):
super(base_model, self).__init__()
self.z_dim = z_dim
self.nc = nc
def weight_init(self):
for block in self._modules:
for m in self._modules[block]:
kaiming_init(m)
def _encode(self, x):
return self.encoder(x)
class AutoEncoder(base_model):
def __init__(self, z_dim, nc):
super(AutoEncoder, self).__init__(z_dim, nc)
def forward(self, x):
distributions = self._encode(x)
mu = distributions[:, :self.z_dim]
logvar = distributions[:, self.z_dim:]
z = reparametrize(mu, logvar)
x_recon = self._decode(z)
return x_recon, mu, logvar
def _decode(self, z):
if z.shape[1] == self.z_dim:
return self.decoder(z)
else:
mu = z[:, :self.z_dim]
logvar = z[:, self.z_dim:]
z = reparametrize(mu, logvar)
return self.decoder(z)
class BetaVAE_H_net(AutoEncoder):
"""Model proposed in original beta-VAE paper(Higgins et al, ICLR, 2017)."""
def __init__(self, z_dim=32, nc=3):
super(BetaVAE_H_net, self).__init__(z_dim, nc)
self.encoder = nn.Sequential(
nn.Conv2d(nc, 32, 4, 2, 1), # B, 32, 32, 32
nn.ReLU(True),
nn.Conv2d(32, 32, 4, 2, 1), # B, 32, 16, 16
nn.ReLU(True),
nn.Conv2d(32, 64, 4, 2, 1), # B, 64, 8, 8
nn.ReLU(True),
nn.Conv2d(64, 64, 4, 2, 1), # B, 64, 4, 4
nn.ReLU(True),
nn.Conv2d(64, 256, 4, 1), # B, 256, 1, 1
nn.ReLU(True),
View((-1, 256*1*1)), # B, 256
nn.Linear(256, z_dim*2), # B, z_dim*2
)
self.decoder = nn.Sequential(
nn.Linear(z_dim, 256), # B, 256
View((-1, 256, 1, 1)), # B, 256, 1, 1
nn.ReLU(True),
nn.ConvTranspose2d(256, 64, 4), # B, 64, 4, 4
nn.ReLU(True),
nn.ConvTranspose2d(64, 64, 4, 2, 1), # B, 64, 8, 8
nn.ReLU(True),
nn.ConvTranspose2d(64, 32, 4, 2, 1), # B, 32, 16, 16
nn.ReLU(True),
nn.ConvTranspose2d(32, 32, 4, 2, 1), # B, 32, 32, 32
nn.ReLU(True),
nn.ConvTranspose2d(32, nc, 4, 2, 1), # B, nc, 64, 64
nn.Sigmoid()
)
self.weight_init()
class BetaVAE_B_net(AutoEncoder):
"""Model proposed in understanding beta-VAE paper(Burgess et al, arxiv:1804.03599, 2018)."""
def __init__(self, z_dim=32, nc=1):
super(BetaVAE_B_net, self).__init__(z_dim, nc)
self.encoder = nn.Sequential(
nn.Conv2d(nc, 32, 4, 2, 1), # B, 32, 32, 32
nn.ReLU(True),
nn.Conv2d(32, 32, 4, 2, 1), # B, 32, 16, 16
nn.ReLU(True),
nn.Conv2d(32, 32, 4, 2, 1), # B, 32, 8, 8
nn.ReLU(True),
nn.Conv2d(32, 32, 4, 2, 1), # B, 32, 4, 4
nn.ReLU(True),
View((-1, 32*4*4)), # B, 512
nn.Linear(32*4*4, 256), # B, 256
nn.ReLU(True),
nn.Linear(256, 256), # B, 256
nn.ReLU(True),
nn.Linear(256, z_dim*2), # B, z_dim*2
)
self.decoder = nn.Sequential(
nn.Linear(z_dim, 256), # B, 256
nn.ReLU(True),
nn.Linear(256, 256), # B, 256
nn.ReLU(True),
nn.Linear(256, 32*4*4), # B, 512
nn.ReLU(True),
View((-1, 32, 4, 4)), # B, 32, 4, 4
nn.ConvTranspose2d(32, 32, 4, 2, 1), # B, 32, 8, 8
nn.ReLU(True),
nn.ConvTranspose2d(32, 32, 4, 2, 1), # B, 32, 16, 16
nn.ReLU(True),
nn.ConvTranspose2d(32, 32, 4, 2, 1), # B, 32, 32, 32
nn.ReLU(True),
nn.ConvTranspose2d(32, nc, 4, 2, 1), # B, nc, 64, 64
nn.Sigmoid()
)
self.weight_init()
class DAE_net(base_model):
def __init__(self, z_dim=100, nc=3):
super(DAE_net, self).__init__(z_dim, nc)
self.encoder = nn.Sequential(
nn.Conv2d(nc, 32, 4, 2, 1), # B, 32, 32, 32
nn.ReLU(True),
nn.Conv2d(32, 32, 4, 2, 1), # B, 32, 16, 16
nn.ReLU(True),
nn.Conv2d(32, 64, 4, 2, 1), # B, 64, 8, 8
nn.ReLU(True),
nn.Conv2d(64, 64, 4, 2, 1), # B, 64, 4, 4
nn.ReLU(True),
View((-1, 1024)), # B, 1024
nn.Linear(1024, z_dim), # B, z_dim
)
self.decoder = nn.Sequential(
nn.Linear(z_dim, 1024), # B, 1024
View((-1, 64, 4, 4)), # B, 64, 4, 4
nn.ReLU(True),
nn.ConvTranspose2d(64, 64, 4, 2, 1), # B, 64, 8, 8
nn.ReLU(True),
nn.ConvTranspose2d(64, 32, 4, 2, 1), # B, 32, 16, 16
nn.ReLU(True),
nn.ConvTranspose2d(32, 32, 4, 2, 1), # B, 32, 32, 32
nn.ReLU(True),
nn.ConvTranspose2d(32, nc, 4, 2, 1), # B, nc, 64, 64
nn.Sigmoid()
)
self.weight_init()
def forward(self, x):
x_encoded = self._encode(x)
x_recon = self._decode(x_encoded)
return x_recon
def _decode(self, z):
return self.decoder(z)
class SCAN_net(AutoEncoder):
"""Model proposed in SCAN: Learning Hierarchical Compositional Visual Concepts, Higgins et al., ICLR 2018."""
def __init__(self, z_dim=32, nc=40):
super(SCAN_net, self).__init__(z_dim, nc)
self.encoder = nn.Sequential(
nn.Linear(nc, 500), # B, 500
nn.ReLU(True),
nn.Linear(500, 500), # B, 500
nn.ReLU(True),
nn.Linear(500, self.z_dim * 2), # B, z_dim*2
)
self.decoder = nn.Sequential(
nn.Linear(z_dim, 500), # B, 500
nn.ReLU(True),
nn.Linear(500, 500), # B, 500
nn.ReLU(True),
nn.Linear(500, nc), # B, nc
nn.Sigmoid(),
)
self.weight_init()