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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):
"""Encoder-Decoder architecture for both WAE-MMD and WAE-GAN."""
#def __init__(self, z_dim=32, nc=3):
# 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, 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.ReLU(True),
# nn.ConvTranspose2d(512, 256, 4, 2, 1, bias=False), # B, 256, 32, 32
# 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.weight_init()
def __init__(self, z_dim=32, nc=1):
super(VAE, self).__init__()
self.z_dim = z_dim
self.nc = nc
self.encoder = nn.Sequential(
nn.Linear(nc, 4),
nn.ReLU(True),
nn.Linear(4, 8),
nn.ReLU(True),
nn.Linear(8, 16),
nn.ReLU(True),
nn.Linear(16, 32),
nn.ReLU(True),
nn.Linear(32, 64),
)
self.fc_mu = nn.Linear(64, z_dim)
self.fc_logvar = nn.Linear(64, z_dim)
self.decoder = nn.Sequential(
nn.Linear(z_dim, 32),
nn.ReLU(True),
nn.Linear(32, 16),
nn.ReLU(True),
nn.Linear(16, 8),
nn.ReLU(True),
nn.Linear(8, 4),
nn.ReLU(True),
nn.Linear(4, 2)
)
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):
z = self._encode(x)
mu, logvar = self.fc_mu(z), self.fc_logvar(z)
z = self.reparameterize(mu, logvar)
x_recon = self._decode(z)
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)
class Discriminator(nn.Module):
"""Adversary architecture(Discriminator) for WAE-GAN."""
#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, 11),
# nn.Softmax()
# )
# self.weight_init()
"""Adversary architecture(Discriminator) for WAE-GAN."""
def __init__(self, z_dim=8):
super(Discriminator, self).__init__()
self.z_dim = z_dim
self.net = nn.Sequential(
nn.Linear(z_dim, 32),
nn.ReLU(True),
nn.Linear(32, 32),
# nn.ReLU(True),
# nn.Linear(128, 128),
# nn.ReLU(True),
# nn.Linear(128, 128),
nn.ReLU(True),
nn.Linear(32, 5),
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)
class FCNet(nn.Module):
def __init__(self, num_classes=5, input_dim=2):
super(FCNet, self).__init__()
self.input_dim = input_dim
self.net = nn.Sequential(
nn.Linear(input_dim, 32),
nn.ReLU(True),
nn.Linear(32, 32),
nn.ReLU(True),
nn.Linear(32, 32),
nn.ReLU(True),
nn.Linear(32, 32),
nn.ReLU(True),
nn.Linear(32, num_classes),
nn.Softmax()
)
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_()