import math
import random
import torch
from torch import nn
from torch.nn import functional as F
from basicsr.ops.fused_act import FusedLeakyReLU, fused_leaky_relu
from basicsr.ops.upfirdn2d import upfirdn2d
from basicsr.utils.registry import ARCH_REGISTRY
class NormStyleCode(nn.Module):
def forward(self, x):
"""Normalize the style codes.
Args:
x (Tensor): Style codes with shape (b, c).
Returns:
Tensor: Normalized tensor.
"""
return x * torch.rsqrt(torch.mean(x**2, dim=1, keepdim=True) + 1e-8)
def make_resample_kernel(k):
"""Make resampling kernel for UpFirDn.
Args:
k (list[int]): A list indicating the 1D resample kernel magnitude.
Returns:
Tensor: 2D resampled kernel.
"""
k = torch.tensor(k, dtype=torch.float32)
if k.ndim == 1:
k = k[None, :] * k[:, None] # to 2D kernel, outer product
# normalize
k /= k.sum()
return k
class UpFirDnUpsample(nn.Module):
"""Upsample, FIR filter, and downsample (upsampole version).
References:
1. https://docs.scipy.org/doc/scipy/reference/generated/scipy.signal.upfirdn.html # noqa: E501
2. http://www.ece.northwestern.edu/local-apps/matlabhelp/toolbox/signal/upfirdn.html # noqa: E501
Args:
resample_kernel (list[int]): A list indicating the 1D resample kernel
magnitude.
factor (int): Upsampling scale factor. Default: 2.
"""
def __init__(self, resample_kernel, factor=2):
super(UpFirDnUpsample, self).__init__()
self.kernel = make_resample_kernel(resample_kernel) * (factor**2)
self.factor = factor
pad = self.kernel.shape[0] - factor
self.pad = ((pad + 1) // 2 + factor - 1, pad // 2)
def forward(self, x):
out = upfirdn2d(x, self.kernel.type_as(x), up=self.factor, down=1, pad=self.pad)
return out
def __repr__(self):
return (f'{self.__class__.__name__}(factor={self.factor})')
class UpFirDnDownsample(nn.Module):
"""Upsample, FIR filter, and downsample (downsampole version).
Args:
resample_kernel (list[int]): A list indicating the 1D resample kernel
magnitude.
factor (int): Downsampling scale factor. Default: 2.
"""
def __init__(self, resample_kernel, factor=2):
super(UpFirDnDownsample, self).__init__()
self.kernel = make_resample_kernel(resample_kernel)
self.factor = factor
pad = self.kernel.shape[0] - factor
self.pad = ((pad + 1) // 2, pad // 2)
def forward(self, x):
out = upfirdn2d(x, self.kernel.type_as(x), up=1, down=self.factor, pad=self.pad)
return out
def __repr__(self):
return (f'{self.__class__.__name__}(factor={self.factor})')
class UpFirDnSmooth(nn.Module):
"""Upsample, FIR filter, and downsample (smooth version).
Args:
resample_kernel (list[int]): A list indicating the 1D resample kernel
magnitude.
upsample_factor (int): Upsampling scale factor. Default: 1.
downsample_factor (int): Downsampling scale factor. Default: 1.
kernel_size (int): Kernel size: Default: 1.
"""
def __init__(self, resample_kernel, upsample_factor=1, downsample_factor=1, kernel_size=1):
super(UpFirDnSmooth, self).__init__()
self.upsample_factor = upsample_factor
self.downsample_factor = downsample_factor
self.kernel = make_resample_kernel(resample_kernel)
if upsample_factor > 1:
self.kernel = self.kernel * (upsample_factor**2)
if upsample_factor > 1:
pad = (self.kernel.shape[0] - upsample_factor) - (kernel_size - 1)
self.pad = ((pad + 1) // 2 + upsample_factor - 1, pad // 2 + 1)
elif downsample_factor > 1:
pad = (self.kernel.shape[0] - downsample_factor) + (kernel_size - 1)
self.pad = ((pad + 1) // 2, pad // 2)
else:
raise NotImplementedError
def forward(self, x):
out = upfirdn2d(x, self.kernel.type_as(x), up=1, down=1, pad=self.pad)
return out
def __repr__(self):
return (f'{self.__class__.__name__}(upsample_factor={self.upsample_factor}'
f', downsample_factor={self.downsample_factor})')
class EqualLinear(nn.Module):
"""Equalized Linear as StyleGAN2.
Args:
in_channels (int): Size of each sample.
out_channels (int): Size of each output sample.
bias (bool): If set to ``False``, the layer will not learn an additive
bias. Default: ``True``.
bias_init_val (float): Bias initialized value. Default: 0.
lr_mul (float): Learning rate multiplier. Default: 1.
activation (None | str): The activation after ``linear`` operation.
Supported: 'fused_lrelu', None. Default: None.
"""
def __init__(self, in_channels, out_channels, bias=True, bias_init_val=0, lr_mul=1, activation=None):
super(EqualLinear, self).__init__()
self.in_channels = in_channels
self.out_channels = out_channels
self.lr_mul = lr_mul
self.activation = activation
if self.activation not in ['fused_lrelu', None]:
raise ValueError(f'Wrong activation value in EqualLinear: {activation}'
"Supported ones are: ['fused_lrelu', None].")
self.scale = (1 / math.sqrt(in_channels)) * lr_mul
self.weight = nn.Parameter(torch.randn(out_channels, in_channels).div_(lr_mul))
if bias:
self.bias = nn.Parameter(torch.zeros(out_channels).fill_(bias_init_val))
else:
self.register_parameter('bias', None)
def forward(self, x):
if self.bias is None:
bias = None
else:
bias = self.bias * self.lr_mul
if self.activation == 'fused_lrelu':
out = F.linear(x, self.weight * self.scale)
out = fused_leaky_relu(out, bias)
else:
out = F.linear(x, self.weight * self.scale, bias=bias)
return out
def __repr__(self):
return (f'{self.__class__.__name__}(in_channels={self.in_channels}, '
f'out_channels={self.out_channels}, bias={self.bias is not None})')
class ModulatedConv2d(nn.Module):
"""Modulated Conv2d used in StyleGAN2.
There is no bias in ModulatedConv2d.
Args:
in_channels (int): Channel number of the input.
out_channels (int): Channel number of the output.
kernel_size (int): Size of the convolving kernel.
num_style_feat (int): Channel number of style features.
demodulate (bool): Whether to demodulate in the conv layer.
Default: True.
sample_mode (str | None): Indicating 'upsample', 'downsample' or None.
Default: None.
resample_kernel (list[int]): A list indicating the 1D resample kernel
magnitude. Default: (1, 3, 3, 1).
eps (float): A value added to the denominator for numerical stability.
Default: 1e-8.
"""
def __init__(self,
in_channels,
out_channels,
kernel_size,
num_style_feat,
demodulate=True,
sample_mode=None,
resample_kernel=(1, 3, 3, 1),
eps=1e-8):
super(ModulatedConv2d, self).__init__()
self.in_channels = in_channels
self.out_channels = out_channels
self.kernel_size = kernel_size
self.demodulate = demodulate
self.sample_mode = sample_mode
self.eps = eps
if self.sample_mode == 'upsample':
self.smooth = UpFirDnSmooth(
resample_kernel, upsample_factor=2, downsample_factor=1, kernel_size=kernel_size)
elif self.sample_mode == 'downsample':
self.smooth = UpFirDnSmooth(
resample_kernel, upsample_factor=1, downsample_factor=2, kernel_size=kernel_size)
elif self.sample_mode is None:
pass
else:
raise ValueError(f'Wrong sample mode {self.sample_mode}, '
"supported ones are ['upsample', 'downsample', None].")
self.scale = 1 / math.sqrt(in_channels * kernel_size**2)
# modulation inside each modulated conv
self.modulation = EqualLinear(
num_style_feat, in_channels, bias=True, bias_init_val=1, lr_mul=1, activation=None)
self.weight = nn.Parameter(torch.randn(1, out_channels, in_channels, kernel_size, kernel_size))
self.padding = kernel_size // 2
def forward(self, x, style):
"""Forward function.
Args:
x (Tensor): Tensor with shape (b, c, h, w).
style (Tensor): Tensor with shape (b, num_style_feat).
Returns:
Tensor: Modulated tensor after convolution.
"""
b, c, h, w = x.shape # c = c_in
# weight modulation
style = self.modulation(style).view(b, 1, c, 1, 1)
# self.weight: (1, c_out, c_in, k, k); style: (b, 1, c, 1, 1)
weight = self.scale * self.weight * style # (b, c_out, c_in, k, k)
if self.demodulate:
demod = torch.rsqrt(weight.pow(2).sum([2, 3, 4]) + self.eps)
weight = weight * demod.view(b, self.out_channels, 1, 1, 1)
weight = weight.view(b * self.out_channels, c, self.kernel_size, self.kernel_size)
if self.sample_mode == 'upsample':
x = x.view(1, b * c, h, w)
weight = weight.view(b, self.out_channels, c, self.kernel_size, self.kernel_size)
weight = weight.transpose(1, 2).reshape(b * c, self.out_channels, self.kernel_size, self.kernel_size)
out = F.conv_transpose2d(x, weight, padding=0, stride=2, groups=b)
out = out.view(b, self.out_channels, *out.shape[2:4])
out = self.smooth(out)
elif self.sample_mode == 'downsample':
x = self.smooth(x)
x = x.view(1, b * c, *x.shape[2:4])
out = F.conv2d(x, weight, padding=0, stride=2, groups=b)
out = out.view(b, self.out_channels, *out.shape[2:4])
else:
x = x.view(1, b * c, h, w)
# weight: (b*c_out, c_in, k, k), groups=b
out = F.conv2d(x, weight, padding=self.padding, groups=b)
out = out.view(b, self.out_channels, *out.shape[2:4])
return out
def __repr__(self):
return (f'{self.__class__.__name__}(in_channels={self.in_channels}, '
f'out_channels={self.out_channels}, '
f'kernel_size={self.kernel_size}, '
f'demodulate={self.demodulate}, sample_mode={self.sample_mode})')
class StyleConv(nn.Module):
"""Style conv.
Args:
in_channels (int): Channel number of the input.
out_channels (int): Channel number of the output.
kernel_size (int): Size of the convolving kernel.
num_style_feat (int): Channel number of style features.
demodulate (bool): Whether demodulate in the conv layer. Default: True.
sample_mode (str | None): Indicating 'upsample', 'downsample' or None.
Default: None.
resample_kernel (list[int]): A list indicating the 1D resample kernel
magnitude. Default: (1, 3, 3, 1).
"""
def __init__(self,
in_channels,
out_channels,
kernel_size,
num_style_feat,
demodulate=True,
sample_mode=None,
resample_kernel=(1, 3, 3, 1)):
super(StyleConv, self).__init__()
self.modulated_conv = ModulatedConv2d(
in_channels,
out_channels,
kernel_size,
num_style_feat,
demodulate=demodulate,
sample_mode=sample_mode,
resample_kernel=resample_kernel)
self.weight = nn.Parameter(torch.zeros(1)) # for noise injection
self.activate = FusedLeakyReLU(out_channels)
def forward(self, x, style, noise=None):
# modulate
out = self.modulated_conv(x, style)
# noise injection
if noise is None:
b, _, h, w = out.shape
noise = out.new_empty(b, 1, h, w).normal_()
out = out + self.weight * noise
# activation (with bias)
out = self.activate(out)
return out
class ToRGB(nn.Module):
"""To RGB from features.
Args:
in_channels (int): Channel number of input.
num_style_feat (int): Channel number of style features.
upsample (bool): Whether to upsample. Default: True.
resample_kernel (list[int]): A list indicating the 1D resample kernel
magnitude. Default: (1, 3, 3, 1).
"""
def __init__(self, in_channels, num_style_feat, upsample=True, resample_kernel=(1, 3, 3, 1)):
super(ToRGB, self).__init__()
if upsample:
self.upsample = UpFirDnUpsample(resample_kernel, factor=2)
else:
self.upsample = None
self.modulated_conv = ModulatedConv2d(
in_channels, 3, kernel_size=1, num_style_feat=num_style_feat, demodulate=False, sample_mode=None)
self.bias = nn.Parameter(torch.zeros(1, 3, 1, 1))
def forward(self, x, style, skip=None):
"""Forward function.
Args:
x (Tensor): Feature tensor with shape (b, c, h, w).
style (Tensor): Tensor with shape (b, num_style_feat).
skip (Tensor): Base/skip tensor. Default: None.
Returns:
Tensor: RGB images.
"""
out = self.modulated_conv(x, style)
out = out + self.bias
if skip is not None:
if self.upsample:
skip = self.upsample(skip)
out = out + skip
return out
class ConstantInput(nn.Module):
"""Constant input.
Args:
num_channel (int): Channel number of constant input.
size (int): Spatial size of constant input.
"""
def __init__(self, num_channel, size):
super(ConstantInput, self).__init__()
self.weight = nn.Parameter(torch.randn(1, num_channel, size, size))
def forward(self, batch):
out = self.weight.repeat(batch, 1, 1, 1)
return out
@ARCH_REGISTRY.register()
class StyleGAN2Generator(nn.Module):
"""StyleGAN2 Generator.
Args:
out_size (int): The spatial size of outputs.
num_style_feat (int): Channel number of style features. Default: 512.
num_mlp (int): Layer number of MLP style layers. Default: 8.
channel_multiplier (int): Channel multiplier for large networks of
StyleGAN2. Default: 2.
resample_kernel (list[int]): A list indicating the 1D resample kernel
magnitude. A cross production will be applied to extent 1D resample
kernel to 2D resample kernel. Default: (1, 3, 3, 1).
lr_mlp (float): Learning rate multiplier for mlp layers. Default: 0.01.
narrow (float): Narrow ratio for channels. Default: 1.0.
"""
def __init__(self,
out_size,
num_style_feat=512,
num_mlp=8,
channel_multiplier=2,
resample_kernel=(1, 3, 3, 1),
lr_mlp=0.01,
narrow=1):
super(StyleGAN2Generator, self).__init__()
# Style MLP layers
self.num_style_feat = num_style_feat
style_mlp_layers = [NormStyleCode()]
for i in range(num_mlp):
style_mlp_layers.append(
EqualLinear(
num_style_feat, num_style_feat, bias=True, bias_init_val=0, lr_mul=lr_mlp,
activation='fused_lrelu'))
self.style_mlp = nn.Sequential(*style_mlp_layers)
channels = {
'4': int(512 * narrow),
'8': int(512 * narrow),
'16': int(512 * narrow),
'32': int(512 * narrow),
'64': int(256 * channel_multiplier * narrow),
'128': int(128 * channel_multiplier * narrow),
'256': int(64 * channel_multiplier * narrow),
'512': int(32 * channel_multiplier * narrow),
'1024': int(16 * channel_multiplier * narrow)
}
self.channels = channels
self.constant_input = ConstantInput(channels['4'], size=4)
self.style_conv1 = StyleConv(
channels['4'],
channels['4'],
kernel_size=3,
num_style_feat=num_style_feat,
demodulate=True,
sample_mode=None,
resample_kernel=resample_kernel)
self.to_rgb1 = ToRGB(channels['4'], num_style_feat, upsample=False, resample_kernel=resample_kernel)
self.log_size = int(math.log(out_size, 2))
self.num_layers = (self.log_size - 2) * 2 + 1
self.num_latent = self.log_size * 2 - 2
self.style_convs = nn.ModuleList()
self.to_rgbs = nn.ModuleList()
self.noises = nn.Module()
in_channels = channels['4']
# noise
for layer_idx in range(self.num_layers):
resolution = 2**((layer_idx + 5) // 2)
shape = [1, 1, resolution, resolution]
self.noises.register_buffer(f'noise{layer_idx}', torch.randn(*shape))
# style convs and to_rgbs
for i in range(3, self.log_size + 1):
out_channels = channels[f'{2**i}']
self.style_convs.append(
StyleConv(
in_channels,
out_channels,
kernel_size=3,
num_style_feat=num_style_feat,
demodulate=True,
sample_mode='upsample',
resample_kernel=resample_kernel,
))
self.style_convs.append(
StyleConv(
out_channels,
out_channels,
kernel_size=3,
num_style_feat=num_style_feat,
demodulate=True,
sample_mode=None,
resample_kernel=resample_kernel))
self.to_rgbs.append(ToRGB(out_channels, num_style_feat, upsample=True, resample_kernel=resample_kernel))
in_channels = out_channels
def make_noise(self):
"""Make noise for noise injection."""
device = self.constant_input.weight.device
noises = [torch.randn(1, 1, 4, 4, device=device)]
for i in range(3, self.log_size + 1):
for _ in range(2):
noises.append(torch.randn(1, 1, 2**i, 2**i, device=device))
return noises
def get_latent(self, x):
return self.style_mlp(x)
def mean_latent(self, num_latent):
latent_in = torch.randn(num_latent, self.num_style_feat, device=self.constant_input.weight.device)
latent = self.style_mlp(latent_in).mean(0, keepdim=True)
return latent
def forward(self,
styles,
input_is_latent=False,
noise=None,
randomize_noise=True,
truncation=1,
truncation_latent=None,
inject_index=None,
return_latents=False):
"""Forward function for StyleGAN2Generator.
Args:
styles (list[Tensor]): Sample codes of styles.
input_is_latent (bool): Whether input is latent style.
Default: False.
noise (Tensor | None): Input noise or None. Default: None.
randomize_noise (bool): Randomize noise, used when 'noise' is
False. Default: True.
truncation (float): TODO. Default: 1.
truncation_latent (Tensor | None): TODO. Default: None.
inject_index (int | None): The injection index for mixing noise.
Default: None.
return_latents (bool): Whether to return style latents.
Default: False.
"""
# style codes -> latents with Style MLP layer
if not input_is_latent:
styles = [self.style_mlp(s) for s in styles]
# noises
if noise is None:
if randomize_noise:
noise = [None] * self.num_layers # for each style conv layer
else: # use the stored noise
noise = [getattr(self.noises, f'noise{i}') for i in range(self.num_layers)]
# style truncation
if truncation < 1:
style_truncation = []
for style in styles:
style_truncation.append(truncation_latent + truncation * (style - truncation_latent))
styles = style_truncation
# get style latent with injection
if len(styles) == 1:
inject_index = self.num_latent
if styles[0].ndim < 3:
# repeat latent code for all the layers
latent = styles[0].unsqueeze(1).repeat(1, inject_index, 1)
else: # used for encoder with different latent code for each layer
latent = styles[0]
elif len(styles) == 2: # mixing noises
if inject_index is None:
inject_index = random.randint(1, self.num_latent - 1)
latent1 = styles[0].unsqueeze(1).repeat(1, inject_index, 1)
latent2 = styles[1].unsqueeze(1).repeat(1, self.num_latent - inject_index, 1)
latent = torch.cat([latent1, latent2], 1)
# main generation
out = self.constant_input(latent.shape[0])
out = self.style_conv1(out, latent[:, 0], noise=noise[0])
skip = self.to_rgb1(out, latent[:, 1])
i = 1
for conv1, conv2, noise1, noise2, to_rgb in zip(self.style_convs[::2], self.style_convs[1::2], noise[1::2],
noise[2::2], self.to_rgbs):
out = conv1(out, latent[:, i], noise=noise1)
out = conv2(out, latent[:, i + 1], noise=noise2)
skip = to_rgb(out, latent[:, i + 2], skip)
i += 2
image = skip
if return_latents:
return image, latent
else:
return image, None
class ScaledLeakyReLU(nn.Module):
"""Scaled LeakyReLU.
Args:
negative_slope (float): Negative slope. Default: 0.2.
"""
def __init__(self, negative_slope=0.2):
super(ScaledLeakyReLU, self).__init__()
self.negative_slope = negative_slope
def forward(self, x):
out = F.leaky_relu(x, negative_slope=self.negative_slope)
return out * math.sqrt(2)
class EqualConv2d(nn.Module):
"""Equalized Linear as StyleGAN2.
Args:
in_channels (int): Channel number of the input.
out_channels (int): Channel number of the output.
kernel_size (int): Size of the convolving kernel.
stride (int): Stride of the convolution. Default: 1
padding (int): Zero-padding added to both sides of the input.
Default: 0.
bias (bool): If ``True``, adds a learnable bias to the output.
Default: ``True``.
bias_init_val (float): Bias initialized value. Default: 0.
"""
def __init__(self, in_channels, out_channels, kernel_size, stride=1, padding=0, bias=True, bias_init_val=0):
super(EqualConv2d, self).__init__()
self.in_channels = in_channels
self.out_channels = out_channels
self.kernel_size = kernel_size
self.stride = stride
self.padding = padding
self.scale = 1 / math.sqrt(in_channels * kernel_size**2)
self.weight = nn.Parameter(torch.randn(out_channels, in_channels, kernel_size, kernel_size))
if bias:
self.bias = nn.Parameter(torch.zeros(out_channels).fill_(bias_init_val))
else:
self.register_parameter('bias', None)
def forward(self, x):
out = F.conv2d(
x,
self.weight * self.scale,
bias=self.bias,
stride=self.stride,
padding=self.padding,
)
return out
def __repr__(self):
return (f'{self.__class__.__name__}(in_channels={self.in_channels}, '
f'out_channels={self.out_channels}, '
f'kernel_size={self.kernel_size},'
f' stride={self.stride}, padding={self.padding}, '
f'bias={self.bias is not None})')
class ConvLayer(nn.Sequential):
"""Conv Layer used in StyleGAN2 Discriminator.
Args:
in_channels (int): Channel number of the input.
out_channels (int): Channel number of the output.
kernel_size (int): Kernel size.
downsample (bool): Whether downsample by a factor of 2.
Default: False.
resample_kernel (list[int]): A list indicating the 1D resample
kernel magnitude. A cross production will be applied to
extent 1D resample kernel to 2D resample kernel.
Default: (1, 3, 3, 1).
bias (bool): Whether with bias. Default: True.
activate (bool): Whether use activateion. Default: True.
"""
def __init__(self,
in_channels,
out_channels,
kernel_size,
downsample=False,
resample_kernel=(1, 3, 3, 1),
bias=True,
activate=True):
layers = []
# downsample
if downsample:
layers.append(
UpFirDnSmooth(resample_kernel, upsample_factor=1, downsample_factor=2, kernel_size=kernel_size))
stride = 2
self.padding = 0
else:
stride = 1
self.padding = kernel_size // 2
# conv
layers.append(
EqualConv2d(
in_channels, out_channels, kernel_size, stride=stride, padding=self.padding, bias=bias
and not activate))
# activation
if activate:
if bias:
layers.append(FusedLeakyReLU(out_channels))
else:
layers.append(ScaledLeakyReLU(0.2))
super(ConvLayer, self).__init__(*layers)
class ResBlock(nn.Module):
"""Residual block used in StyleGAN2 Discriminator.
Args:
in_channels (int): Channel number of the input.
out_channels (int): Channel number of the output.
resample_kernel (list[int]): A list indicating the 1D resample
kernel magnitude. A cross production will be applied to
extent 1D resample kernel to 2D resample kernel.
Default: (1, 3, 3, 1).
"""
def __init__(self, in_channels, out_channels, resample_kernel=(1, 3, 3, 1)):
super(ResBlock, self).__init__()
self.conv1 = ConvLayer(in_channels, in_channels, 3, bias=True, activate=True)
self.conv2 = ConvLayer(
in_channels, out_channels, 3, downsample=True, resample_kernel=resample_kernel, bias=True, activate=True)
self.skip = ConvLayer(
in_channels, out_channels, 1, downsample=True, resample_kernel=resample_kernel, bias=False, activate=False)
def forward(self, x):
out = self.conv1(x)
out = self.conv2(out)
skip = self.skip(x)
out = (out + skip) / math.sqrt(2)
return out
@ARCH_REGISTRY.register()
class StyleGAN2Discriminator(nn.Module):
"""StyleGAN2 Discriminator.
Args:
out_size (int): The spatial size of outputs.
channel_multiplier (int): Channel multiplier for large networks of
StyleGAN2. Default: 2.
resample_kernel (list[int]): A list indicating the 1D resample kernel
magnitude. A cross production will be applied to extent 1D resample
kernel to 2D resample kernel. Default: (1, 3, 3, 1).
stddev_group (int): For group stddev statistics. Default: 4.
narrow (float): Narrow ratio for channels. Default: 1.0.
"""
def __init__(self, out_size, channel_multiplier=2, resample_kernel=(1, 3, 3, 1), stddev_group=4, narrow=1):
super(StyleGAN2Discriminator, self).__init__()
channels = {
'4': int(512 * narrow),
'8': int(512 * narrow),
'16': int(512 * narrow),
'32': int(512 * narrow),
'64': int(256 * channel_multiplier * narrow),
'128': int(128 * channel_multiplier * narrow),
'256': int(64 * channel_multiplier * narrow),
'512': int(32 * channel_multiplier * narrow),
'1024': int(16 * channel_multiplier * narrow)
}
log_size = int(math.log(out_size, 2))
conv_body = [ConvLayer(3, channels[f'{out_size}'], 1, bias=True, activate=True)]
in_channels = channels[f'{out_size}']
for i in range(log_size, 2, -1):
out_channels = channels[f'{2**(i - 1)}']
conv_body.append(ResBlock(in_channels, out_channels, resample_kernel))
in_channels = out_channels
self.conv_body = nn.Sequential(*conv_body)
self.final_conv = ConvLayer(in_channels + 1, channels['4'], 3, bias=True, activate=True)
self.final_linear = nn.Sequential(
EqualLinear(
channels['4'] * 4 * 4, channels['4'], bias=True, bias_init_val=0, lr_mul=1, activation='fused_lrelu'),
EqualLinear(channels['4'], 1, bias=True, bias_init_val=0, lr_mul=1, activation=None),
)
self.stddev_group = stddev_group
self.stddev_feat = 1
def forward(self, x):
out = self.conv_body(x)
b, c, h, w = out.shape
# concatenate a group stddev statistics to out
group = min(b, self.stddev_group) # Minibatch must be divisible by (or smaller than) group_size
stddev = out.view(group, -1, self.stddev_feat, c // self.stddev_feat, h, w)
stddev = torch.sqrt(stddev.var(0, unbiased=False) + 1e-8)
stddev = stddev.mean([2, 3, 4], keepdims=True).squeeze(2)
stddev = stddev.repeat(group, 1, h, w)
out = torch.cat([out, stddev], 1)
out = self.final_conv(out)
out = out.view(b, -1)
out = self.final_linear(out)
return out