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models.py
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import torch
import torch.nn as nn
from torch.nn.utils import spectral_norm as sn
class Lambda(nn.Module):
def __init__(self, func):
super(Lambda, self).__init__()
self.func = func
def forward(self, x):
return self.func(x)
class Generator(nn.Module):
def __init__(self, nz, n_gen, n_div):
super(Generator, self).__init__()
self.nz = nz
self.n_gen = n_gen
self.n_div = n_div
def forward(self, z, g_idx):
batch_size = z.size(0)
images = torch.stack([self.gens[i](z) for i in range(self.n_gen)], dim=1)
images = images[torch.arange(batch_size), g_idx]
return images
class Discriminator(nn.Module):
def __init__(self, n_gen):
super(Discriminator, self).__init__()
def forward(self, input):
x = self.latent(input)
score = self.score(x)
posterior = self.posterior(x)
return score, posterior
class MNISTGenerator(Generator):
def __init__(self, *args, **kwargs):
super(MNISTGenerator, self).__init__(*args, **kwargs)
n_div = self.n_div
self.gens = nn.ModuleList([
nn.Sequential(
nn.Linear(self.nz, 1024 // n_div),
nn.LeakyReLU(0.2),
nn.Linear(1024 // n_div, 128 // n_div * 7 * 7),
nn.LeakyReLU(0.2),
Lambda(lambda x: x.view(-1, 128 // n_div, 7, 7)), # B X 32 X 7 X 7
nn.ConvTranspose2d(128 // n_div, 64 // n_div, 4, 2, 1), # B X 16 X 14 X 14
nn.LeakyReLU(0.2),
nn.ConvTranspose2d(64 // n_div, 32 // n_div, 4, 2, 1), # B X 8 X 28 X 28
nn.LeakyReLU(0.2),
nn.ConvTranspose2d(32 // n_div, 1, 3, 1, 1), # B X 1 X 28 X 28
nn.Tanh(),
)
for i in range(self.n_gen)
])
class MNISTShareGenerator(Generator):
def __init__(self, *args, **kwargs):
super(MNISTShareGenerator, self).__init__(*args, **kwargs)
self.common = nn.Sequential(
nn.Linear(self.nz, 1024),
nn.LeakyReLU(0.2),
nn.Linear(1024, 128 * 7 * 7),
nn.LeakyReLU(0.2),
Lambda(lambda x: x.view(-1, 128, 7, 7)), # B X 32 X 7 X 7
)
n_div = self.n_div
self.gens = nn.ModuleList([
nn.Sequential(
nn.ConvTranspose2d(128, 64 // n_div, 4, 2, 1), # B X 16 X 14 X 14
nn.LeakyReLU(0.2),
nn.ConvTranspose2d(64 // n_div, 32 // n_div, 4, 2, 1), # B X 8 X 28 X 28
nn.LeakyReLU(0.2),
nn.ConvTranspose2d(32 // n_div, 1, 3, 1, 1), # B X 1 X 28 X 28
nn.Tanh(),
)
for i in range(self.n_gen)
])
def forward(self, z, g_idx):
batch_size = z.size(0)
latent = self.common(z)
images = torch.stack([self.gens[i](latent) for i in range(self.n_gen)], dim=1)
images = images[torch.arange(batch_size), g_idx]
return images
class MNISTDiscriminator(Discriminator):
def __init__(self, n_gen):
super(MNISTDiscriminator, self).__init__(n_gen)
self.latent = nn.Sequential(
sn(nn.Conv2d(1, 64, 4, 2, 1)), # B X 64 X 14 X 14
nn.BatchNorm2d(64),
nn.LeakyReLU(0.2),
sn(nn.Conv2d(64, 128, 4, 2, 1)), # B X 128 X 7 X 7
nn.BatchNorm2d(128),
nn.LeakyReLU(0.2),
Lambda(lambda x: x.view(-1, 128 * 7 * 7)),
)
self.score = nn.Sequential(
sn(nn.Linear(128 * 7 * 7, 1024)),
nn.BatchNorm1d(1024),
nn.LeakyReLU(0.2),
sn(nn.Linear(1024, 1)),
)
self.posterior = nn.Sequential(
nn.Linear(128 * 7 * 7, 1024),
nn.BatchNorm1d(1024),
nn.LeakyReLU(0.2),
nn.Linear(1024, n_gen),
)
class ChairsGenerator(Generator):
def __init__(self, *args, **kwargs):
super(ChairsGenerator, self).__init__(*args, **kwargs)
n_div = self.n_div
self.gens = nn.ModuleList([
nn.Sequential(
nn.Linear(self.nz, 256 // n_div),
nn.LeakyReLU(0.2),
nn.Linear(256 // n_div, 128 // n_div * 8 * 8),
nn.LeakyReLU(0.2),
Lambda(lambda x: x.view(-1, 128 // n_div, 8, 8)), # B X 32 X 8 X 8
nn.ConvTranspose2d(128 // n_div, 64 // n_div, 4, 2, 1), # B X 16 X 16 X 16
nn.LeakyReLU(0.2),
nn.ConvTranspose2d(64 // n_div, 32 // n_div, 4, 2, 1), # B X 8 X 32 X 32
nn.LeakyReLU(0.2),
nn.ConvTranspose2d(32 // n_div, 16 // n_div, 4, 2, 1), # B X 4 X 64 X 64
nn.LeakyReLU(0.2),
nn.ConvTranspose2d(16 // n_div, 1, 3, 1, 1), # B X 1 X 64 X 64
nn.Tanh(),
)
for i in range(self.n_gen)
])
class ChairsShareGenerator(Generator):
def __init__(self, *args, **kwargs):
super(ChairsShareGenerator, self).__init__(*args, **kwargs)
self.common = nn.Sequential(
nn.Linear(self.nz, 256),
nn.BatchNorm1d(256),
nn.LeakyReLU(0.2),
nn.Linear(256, 128 * 8 * 8),
nn.BatchNorm1d(128 * 8 * 8),
nn.LeakyReLU(0.2),
Lambda(lambda x: x.view(-1, 128, 8, 8)), # B X 32 X 8 X 8
nn.ConvTranspose2d(128, 64, 4, 2, 1), # B X 16 X 16 X 16
nn.BatchNorm2d(64),
nn.LeakyReLU(0.2),
nn.ConvTranspose2d(64, 32 // n_div, 4, 2, 1), # B X 8 X 32 X 32
)
n_div = self.n_div
self.gens = nn.ModuleList([
nn.Sequential(
nn.BatchNorm2d(32 // n_div),
nn.LeakyReLU(0.2),
nn.ConvTranspose2d(32 // n_div, 16 // n_div, 4, 2, 1), # B X 4 X 64 X 64
nn.BatchNorm2d(16 // n_div),
nn.LeakyReLU(0.2),
nn.ConvTranspose2d(16 // n_div, 1, 3, 1, 1), # B X 1 X 64 X 64
nn.Tanh(),
)
for i in range(self.n_gen)
])
def forward(self, z, g_idx):
batch_size = z.size(0)
latent = self.common(z)
images = torch.stack([self.gens[i](latent) for i in range(self.n_gen)], dim=1)
images = images[torch.arange(batch_size), g_idx]
return images
class ChairsDiscriminator(Discriminator):
def __init__(self, n_gen):
super(ChairsDiscriminator, self).__init__(n_gen)
self.latent = nn.Sequential(
sn(nn.Conv2d(1, 64, 4, 2, 1)), # B X 64 X 32 X 32
nn.BatchNorm2d(64),
nn.LeakyReLU(0.2),
sn(nn.Conv2d(64, 128, 4, 2, 1)), # B X 128 X 16 X 16
nn.BatchNorm2d(128),
nn.LeakyReLU(0.2),
sn(nn.Conv2d(128, 128, 4, 2, 1)), # B X 128 X 8 X 8
nn.BatchNorm2d(128),
nn.LeakyReLU(0.2),
Lambda(lambda x: x.view(-1, 128 * 8 * 8)),
)
self.score = nn.Sequential(
sn(nn.Linear(128 * 8 * 8, 1024)),
nn.BatchNorm1d(1024),
nn.LeakyReLU(0.2),
sn(nn.Linear(1024, 1)),
)
self.posterior = nn.Sequential(
nn.Linear(128 * 8 * 8, 1024),
nn.BatchNorm1d(1024),
nn.LeakyReLU(0.2),
nn.Linear(1024, n_gen),
)