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# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# All contributions by Andy Brock:
# Copyright (c) 2019 Andy Brock
#
# MIT License
#
# BigGAN V1:
# This is now deprecated code used for porting the TFHub modules to pytorch,
# included here for reference only.
import numpy as np
import torch
from scipy.stats import truncnorm
from torch import nn
from torch.nn import Parameter
from torch.nn import functional as F
def l2normalize(v, eps=1e-4):
return v / (v.norm() + eps)
def truncated_z_sample(batch_size, z_dim, truncation=0.5, seed=None):
state = None if seed is None else np.random.RandomState(seed)
values = truncnorm.rvs(-2, 2, size=(batch_size, z_dim), random_state=state)
return truncation * values
def denorm(x):
out = (x + 1) / 2
return out.clamp_(0, 1)
class SpectralNorm(nn.Module):
def __init__(self, module, name="weight", power_iterations=1):
super(SpectralNorm, self).__init__()
self.module = module
self.name = name
self.power_iterations = power_iterations
if not self._made_params():
self._make_params()
def _update_u_v(self):
u = getattr(self.module, self.name + "_u")
v = getattr(self.module, self.name + "_v")
w = getattr(self.module, self.name + "_bar")
height = w.data.shape[0]
_w = w.view(height, -1)
for _ in range(self.power_iterations):
v = l2normalize(torch.matmul(_w.t(), u))
u = l2normalize(torch.matmul(_w, v))
sigma = u.dot((_w).mv(v))
setattr(self.module, self.name, w / sigma.expand_as(w))
def _made_params(self):
try:
getattr(self.module, self.name + "_u")
getattr(self.module, self.name + "_v")
getattr(self.module, self.name + "_bar")
return True
except AttributeError:
return False
def _make_params(self):
w = getattr(self.module, self.name)
height = w.data.shape[0]
width = w.view(height, -1).data.shape[1]
u = Parameter(w.data.new(height).normal_(0, 1), requires_grad=False)
v = Parameter(w.data.new(height).normal_(0, 1), requires_grad=False)
u.data = l2normalize(u.data)
v.data = l2normalize(v.data)
w_bar = Parameter(w.data)
del self.module._parameters[self.name]
self.module.register_parameter(self.name + "_u", u)
self.module.register_parameter(self.name + "_v", v)
self.module.register_parameter(self.name + "_bar", w_bar)
def forward(self, *args):
self._update_u_v()
return self.module.forward(*args)
class SelfAttention(nn.Module):
""" Self Attention Layer"""
def __init__(self, in_dim, activation=F.relu):
super().__init__()
self.chanel_in = in_dim
self.activation = activation
self.theta = SpectralNorm(
nn.Conv2d(
in_channels=in_dim, out_channels=in_dim // 8, kernel_size=1, bias=False
)
)
self.phi = SpectralNorm(
nn.Conv2d(
in_channels=in_dim, out_channels=in_dim // 8, kernel_size=1, bias=False
)
)
self.pool = nn.MaxPool2d(2, 2)
self.g = SpectralNorm(
nn.Conv2d(
in_channels=in_dim, out_channels=in_dim // 2, kernel_size=1, bias=False
)
)
self.o_conv = SpectralNorm(
nn.Conv2d(
in_channels=in_dim // 2, out_channels=in_dim, kernel_size=1, bias=False
)
)
self.gamma = nn.Parameter(torch.zeros(1))
self.softmax = nn.Softmax(dim=-1)
def forward(self, x):
m_batchsize, C, width, height = x.size()
N = height * width
theta = self.theta(x)
phi = self.phi(x)
phi = self.pool(phi)
phi = phi.view(m_batchsize, -1, N // 4)
theta = theta.view(m_batchsize, -1, N)
theta = theta.permute(0, 2, 1)
attention = self.softmax(torch.bmm(theta, phi))
g = self.pool(self.g(x)).view(m_batchsize, -1, N // 4)
attn_g = torch.bmm(g, attention.permute(0, 2, 1)).view(
m_batchsize, -1, width, height
)
out = self.o_conv(attn_g)
return self.gamma * out + x
class ConditionalBatchNorm2d(nn.Module):
def __init__(self, num_features, num_classes, eps=1e-4, momentum=0.1):
super().__init__()
self.num_features = num_features
self.bn = nn.BatchNorm2d(num_features, affine=False, eps=eps, momentum=momentum)
self.gamma_embed = SpectralNorm(
nn.Linear(num_classes, num_features, bias=False)
)
self.beta_embed = SpectralNorm(nn.Linear(num_classes, num_features, bias=False))
def forward(self, x, y):
out = self.bn(x)
gamma = self.gamma_embed(y) + 1
beta = self.beta_embed(y)
out = gamma.view(-1, self.num_features, 1, 1) * out + beta.view(
-1, self.num_features, 1, 1
)
return out
class GBlock(nn.Module):
def __init__(
self,
in_channel,
out_channel,
kernel_size=[3, 3],
padding=1,
stride=1,
n_class=None,
bn=True,
activation=F.relu,
upsample=True,
downsample=False,
z_dim=148,
):
super().__init__()
self.conv0 = SpectralNorm(
nn.Conv2d(
in_channel,
out_channel,
kernel_size,
stride,
padding,
bias=True if bn else True,
)
)
self.conv1 = SpectralNorm(
nn.Conv2d(
out_channel,
out_channel,
kernel_size,
stride,
padding,
bias=True if bn else True,
)
)
self.skip_proj = False
if in_channel != out_channel or upsample or downsample:
self.conv_sc = SpectralNorm(nn.Conv2d(in_channel, out_channel, 1, 1, 0))
self.skip_proj = True
self.upsample = upsample
self.downsample = downsample
self.activation = activation
self.bn = bn
if bn:
self.HyperBN = ConditionalBatchNorm2d(in_channel, z_dim)
self.HyperBN_1 = ConditionalBatchNorm2d(out_channel, z_dim)
def forward(self, input, condition=None):
out = input
if self.bn:
out = self.HyperBN(out, condition)
out = self.activation(out)
if self.upsample:
out = F.interpolate(out, scale_factor=2)
out = self.conv0(out)
if self.bn:
out = self.HyperBN_1(out, condition)
out = self.activation(out)
out = self.conv1(out)
if self.downsample:
out = F.avg_pool2d(out, 2)
if self.skip_proj:
skip = input
if self.upsample:
skip = F.interpolate(skip, scale_factor=2)
skip = self.conv_sc(skip)
if self.downsample:
skip = F.avg_pool2d(skip, 2)
else:
skip = input
return out + skip
class Generator128(nn.Module):
def __init__(self, code_dim=120, n_class=1000, chn=96, debug=False):
super().__init__()
self.linear = nn.Linear(n_class, 128, bias=False)
if debug:
chn = 8
self.first_view = 16 * chn
self.G_linear = SpectralNorm(nn.Linear(20, 4 * 4 * 16 * chn))
z_dim = code_dim + 28
self.GBlock = nn.ModuleList(
[
GBlock(16 * chn, 16 * chn, n_class=n_class, z_dim=z_dim),
GBlock(16 * chn, 8 * chn, n_class=n_class, z_dim=z_dim),
GBlock(8 * chn, 4 * chn, n_class=n_class, z_dim=z_dim),
GBlock(4 * chn, 2 * chn, n_class=n_class, z_dim=z_dim),
GBlock(2 * chn, 1 * chn, n_class=n_class, z_dim=z_dim),
]
)
self.sa_id = 4
self.num_split = len(self.GBlock) + 1
self.attention = SelfAttention(2 * chn)
self.ScaledCrossReplicaBN = nn.BatchNorm2d(1 * chn, eps=1e-4)
self.colorize = SpectralNorm(nn.Conv2d(1 * chn, 3, [3, 3], padding=1))
def forward(self, input, class_id):
codes = torch.chunk(input, self.num_split, 1)
class_emb = self.linear(class_id) # 128
out = self.G_linear(codes[0])
out = out.view(-1, 4, 4, self.first_view).permute(0, 3, 1, 2)
for i, (code, GBlock) in enumerate(zip(codes[1:], self.GBlock)):
if i == self.sa_id:
out = self.attention(out)
condition = torch.cat([code, class_emb], 1)
out = GBlock(out, condition)
out = self.ScaledCrossReplicaBN(out)
out = F.relu(out)
out = self.colorize(out)
return torch.tanh(out)
class Generator256(nn.Module):
def __init__(self, code_dim=140, n_class=1000, chn=96, debug=False):
super().__init__()
self.linear = nn.Linear(n_class, 128, bias=False)
if debug:
chn = 8
self.first_view = 16 * chn
self.G_linear = SpectralNorm(nn.Linear(20, 4 * 4 * 16 * chn))
self.GBlock = nn.ModuleList(
[
GBlock(16 * chn, 16 * chn, n_class=n_class),
GBlock(16 * chn, 8 * chn, n_class=n_class),
GBlock(8 * chn, 8 * chn, n_class=n_class),
GBlock(8 * chn, 4 * chn, n_class=n_class),
GBlock(4 * chn, 2 * chn, n_class=n_class),
GBlock(2 * chn, 1 * chn, n_class=n_class),
]
)
self.sa_id = 5
self.num_split = len(self.GBlock) + 1
self.attention = SelfAttention(2 * chn)
self.ScaledCrossReplicaBN = nn.BatchNorm2d(1 * chn, eps=1e-4)
self.colorize = SpectralNorm(nn.Conv2d(1 * chn, 3, [3, 3], padding=1))
def forward(self, input, class_id):
codes = torch.chunk(input, self.num_split, 1)
class_emb = self.linear(class_id) # 128
out = self.G_linear(codes[0])
out = out.view(-1, 4, 4, self.first_view).permute(0, 3, 1, 2)
for i, (code, GBlock) in enumerate(zip(codes[1:], self.GBlock)):
if i == self.sa_id:
out = self.attention(out)
condition = torch.cat([code, class_emb], 1)
out = GBlock(out, condition)
out = self.ScaledCrossReplicaBN(out)
out = F.relu(out)
out = self.colorize(out)
return torch.tanh(out)
class Generator512(nn.Module):
def __init__(self, code_dim=128, n_class=1000, chn=96, debug=False):
super().__init__()
self.linear = nn.Linear(n_class, 128, bias=False)
if debug:
chn = 8
self.first_view = 16 * chn
self.G_linear = SpectralNorm(nn.Linear(16, 4 * 4 * 16 * chn))
z_dim = code_dim + 16
self.GBlock = nn.ModuleList(
[
GBlock(16 * chn, 16 * chn, n_class=n_class, z_dim=z_dim),
GBlock(16 * chn, 8 * chn, n_class=n_class, z_dim=z_dim),
GBlock(8 * chn, 8 * chn, n_class=n_class, z_dim=z_dim),
GBlock(8 * chn, 4 * chn, n_class=n_class, z_dim=z_dim),
GBlock(4 * chn, 2 * chn, n_class=n_class, z_dim=z_dim),
GBlock(2 * chn, 1 * chn, n_class=n_class, z_dim=z_dim),
GBlock(1 * chn, 1 * chn, n_class=n_class, z_dim=z_dim),
]
)
self.sa_id = 4
self.num_split = len(self.GBlock) + 1
self.attention = SelfAttention(4 * chn)
self.ScaledCrossReplicaBN = nn.BatchNorm2d(1 * chn)
self.colorize = SpectralNorm(nn.Conv2d(1 * chn, 3, [3, 3], padding=1))
def forward(self, input, class_id):
codes = torch.chunk(input, self.num_split, 1)
class_emb = self.linear(class_id) # 128
out = self.G_linear(codes[0])
out = out.view(-1, 4, 4, self.first_view).permute(0, 3, 1, 2)
for i, (code, GBlock) in enumerate(zip(codes[1:], self.GBlock)):
if i == self.sa_id:
out = self.attention(out)
condition = torch.cat([code, class_emb], 1)
out = GBlock(out, condition)
out = self.ScaledCrossReplicaBN(out)
out = F.relu(out)
out = self.colorize(out)
return torch.tanh(out)
class Discriminator(nn.Module):
def __init__(self, n_class=1000, chn=96, debug=False):
super().__init__()
def conv(in_channel, out_channel, downsample=True):
return GBlock(
in_channel, out_channel, bn=False, upsample=False, downsample=downsample
)
if debug:
chn = 8
self.debug = debug
self.pre_conv = nn.Sequential(
SpectralNorm(nn.Conv2d(3, 1 * chn, 3, padding=1)),
nn.ReLU(),
SpectralNorm(nn.Conv2d(1 * chn, 1 * chn, 3, padding=1)),
nn.AvgPool2d(2),
)
self.pre_skip = SpectralNorm(nn.Conv2d(3, 1 * chn, 1))
self.conv = nn.Sequential(
conv(1 * chn, 1 * chn, downsample=True),
conv(1 * chn, 2 * chn, downsample=True),
SelfAttention(2 * chn),
conv(2 * chn, 2 * chn, downsample=True),
conv(2 * chn, 4 * chn, downsample=True),
conv(4 * chn, 8 * chn, downsample=True),
conv(8 * chn, 8 * chn, downsample=True),
conv(8 * chn, 16 * chn, downsample=True),
conv(16 * chn, 16 * chn, downsample=False),
)
self.linear = SpectralNorm(nn.Linear(16 * chn, 1))
self.embed = nn.Embedding(n_class, 16 * chn)
self.embed.weight.data.uniform_(-0.1, 0.1)
self.embed = SpectralNorm(self.embed)
def forward(self, input, class_id):
out = self.pre_conv(input)
out += self.pre_skip(F.avg_pool2d(input, 2))
out = self.conv(out)
out = F.relu(out)
out = out.view(out.size(0), out.size(1), -1)
out = out.sum(2)
out_linear = self.linear(out).squeeze(1)
embed = self.embed(class_id)
prod = (out * embed).sum(1)
return out_linear + prod
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