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model.py
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model.py
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import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.init as init
class View(nn.Module):
def __init__(self, size):
super(View, self).__init__()
self.size = size
def forward(self, tensor):
return tensor.view(self.size)
class VAE(nn.Module):
def __init__(self, z_dim=32, nc=3, num_classes = 10):
print(" task is ", num_classes, " classification")
super(VAE, self).__init__()
self.z_dim = z_dim
self.nc = nc
self.encoder = nn.Sequential(
nn.Conv2d(nc, 128, 4, 2, 1, bias=False), # B, 128, 32, 32
nn.BatchNorm2d(128),
nn.ReLU(True),
nn.Conv2d(128, 256, 4, 2, 1, bias=False), # B, 256, 16, 16
nn.BatchNorm2d(256),
nn.ReLU(True),
nn.Conv2d(256, 512, 4, 2, 1, bias=False), # B, 512, 8, 8
nn.BatchNorm2d(512),
nn.ReLU(True),
nn.Conv2d(512, 1024, 4, 2, 1, bias=False), # B, 1024, 4, 4
nn.BatchNorm2d(1024),
nn.ReLU(True),
View((-1, 1024*2*2)), # B, 1024*4*4
)
self.fc_mu = nn.Linear(1024*2*2, z_dim) # B, z_dim
self.fc_logvar = nn.Linear(1024*2*2, z_dim) # B, z_dim
self.decoder = nn.Sequential(
nn.Linear(z_dim//2, 1024*4*4), # B, 1024*8*8
View((-1, 1024, 4, 4)), # B, 1024, 8, 8
nn.ConvTranspose2d(1024, 512, 4, 2, 1, bias=False), # B, 512, 16, 16
nn.BatchNorm2d(512),
nn.ConvTranspose2d(512, 256, 4, 2, 1, bias=False), # B, 256, 32, 32
nn.ReLU(True),
nn.BatchNorm2d(256),
nn.ReLU(True),
nn.ConvTranspose2d(256, 128, 4, 2, 1, bias=False), # B, 128, 64, 64
nn.BatchNorm2d(128),
nn.ReLU(True),
nn.ConvTranspose2d(128, nc, 1), # B, nc, 64, 64
)
self.classifier = nn.Sequential(
nn.Linear(z_dim//2, 1024),
nn.ReLU(True),
nn.Linear(1024, 256),
nn.ReLU(True),
nn.Linear(256, num_classes), # B, nc, 64, 64
)
self.weight_init()
def weight_init(self):
for block in self._modules:
try:
for m in self._modules[block]:
kaiming_init(m)
except:
kaiming_init(block)
def forward(self, x, labeled = 1):
z = self._encode(x)
mu, logvar = self.fc_mu(z), self.fc_logvar(z)
z = self.reparameterize(mu, logvar)
# Split the latent variable into two, one for reconstruction and another for targer learner.
# The mu is the unified representation.
m1, m2 = torch.split(z, [self.z_dim//2,self.z_dim//2], dim=1)
x_recon = self._decode(m1)
if(labeled == 1):
pred_label = self._classi(m2)
return x_recon, z, mu, logvar, pred_label
else:
return x_recon, z, mu, logvar
def reparameterize(self, mu, logvar):
stds = (0.5 * logvar).exp()
epsilon = torch.randn(*mu.size())
if mu.is_cuda:
stds, epsilon = stds.cuda(), epsilon.cuda()
latents = epsilon * stds + mu
return latents
def _encode(self, x):
return self.encoder(x)
def _decode(self, z):
return self.decoder(z)
def _classi(self, z):
return self.classifier(z)
class Discriminator(nn.Module):
def __init__(self, z_dim=10):
super(Discriminator, self).__init__()
self.z_dim = z_dim
self.net = nn.Sequential(
nn.Linear(z_dim, 512),
nn.ReLU(True),
nn.Linear(512, 512),
nn.ReLU(True),
nn.Linear(512, 512),
nn.ReLU(True),
nn.Linear(512, 512),
nn.ReLU(True),
nn.Linear(512, 1),
nn.Sigmoid()
)
self.weight_init()
def weight_init(self):
for block in self._modules:
for m in self._modules[block]:
kaiming_init(m)
def forward(self, z):
return self.net(z)
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)
def normal_init(m, mean, std):
if isinstance(m, (nn.Linear, nn.Conv2d)):
m.weight.data.normal_(mean, std)
if m.bias.data is not None:
m.bias.data.zero_()
elif isinstance(m, (nn.BatchNorm2d, nn.BatchNorm1d)):
m.weight.data.fill_(1)
if m.bias.data is not None:
m.bias.data.zero_()