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# python3.7
"""Contains the implementation of generator described in StyleGAN.
Paper: https://arxiv.org/pdf/1812.04948.pdf
Official TensorFlow implementation: https://github.com/NVlabs/stylegan
"""
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.cuda.amp import autocast
from .utils.ops import all_gather
__all__ = ['StyleGANGenerator']
# Resolutions allowed.
_RESOLUTIONS_ALLOWED = [8, 16, 32, 64, 128, 256, 512, 1024]
# Fused-scale options allowed.
_FUSED_SCALE_ALLOWED = [True, False, 'auto']
# pylint: disable=missing-function-docstring
class StyleGANGenerator(nn.Module):
"""Defines the generator network in StyleGAN.
NOTE: The synthesized images are with `RGB` channel order and pixel range
[-1, 1].
Settings for the mapping network:
(1) z_dim: Dimension of the input latent space, Z. (default: 512)
(2) w_dim: Dimension of the output latent space, W. (default: 512)
(3) repeat_w: Repeat w-code for different layers. (default: True)
(4) normalize_z: Whether to normalize the z-code. (default: True)
(5) mapping_layers: Number of layers of the mapping network. (default: 8)
(6) mapping_fmaps: Number of hidden channels of the mapping network.
(default: 512)
(7) mapping_use_wscale: Whether to use weight scaling for the mapping
network. (default: True)
(8) mapping_wscale_gain: The factor to control weight scaling for the
mapping network (default: sqrt(2.0))
(9) mapping_lr_mul: Learning rate multiplier for the mapping network.
(default: 0.01)
Settings for conditional generation:
(1) label_dim: Dimension of the additional label for conditional generation.
In one-hot conditioning case, it is equal to the number of classes. If
set to 0, conditioning training will be disabled. (default: 0)
(2) embedding_dim: Dimension of the embedding space, if needed.
(default: 512)
Settings for the synthesis network:
(1) resolution: The resolution of the output image. (default: -1)
(2) init_res: The initial resolution to start with convolution. (default: 4)
(3) image_channels: Number of channels of the output image. (default: 3)
(4) final_tanh: Whether to use `tanh` to control the final pixel range.
(default: False)
(5) fused_scale: The strategy of fusing `upsample` and `conv2d` as one
operator. `True` means blocks from all resolutions will fuse. `False`
means blocks from all resolutions will not fuse. `auto` means blocks
from resolutions higher than (or equal to) `fused_scale_res` will fuse.
(default: `auto`)
(6) fused_scale_res: Minimum resolution to fuse `conv2d` and `downsample`
as one operator. This field only takes effect if `fused_scale` is set
as `auto`. (default: 128)
(7) use_wscale: Whether to use weight scaling. (default: True)
(8) wscale_gain: The factor to control weight scaling. (default: sqrt(2.0))
(9) lr_mul: Learning rate multiplier for the synthesis network.
(default: 1.0)
(10) noise_type: Type of noise added to the convolutional results at each
layer. (default: `spatial`)
(11) fmaps_base: Factor to control number of feature maps for each layer.
(default: 16 << 10)
(12) fmaps_max: Maximum number of feature maps in each layer. (default: 512)
(13) filter_kernel: Kernel used for filtering (e.g., downsampling).
(default: (1, 2, 1))
(14) eps: A small value to avoid divide overflow. (default: 1e-8)
Runtime settings:
(1) w_moving_decay: Decay factor for updating `w_avg`, which is used for
training only. Set `None` to disable. (default: None)
(2) sync_w_avg: Synchronizing the stats of `w_avg` across replicas. If set
as `True`, the stats will be more accurate, yet the speed maybe a little
bit slower. (default: False)
(3) style_mixing_prob: Probability to perform style mixing as a training
regularization. Set `None` to disable. (default: None)
(4) trunc_psi: Truncation psi, set `None` to disable. (default: None)
(5) trunc_layers: Number of layers to perform truncation. (default: None)
(6) noise_mode: Mode of the layer-wise noise. Support `none`, `random`,
`const`. (default: `const`)
(7) enable_amp: Whether to enable automatic mixed precision training.
(default: False)
"""
def __init__(self,
# Settings for mapping network.
z_dim=512,
w_dim=512,
repeat_w=True,
normalize_z=True,
mapping_layers=8,
mapping_fmaps=512,
mapping_use_wscale=True,
mapping_wscale_gain=np.sqrt(2.0),
mapping_lr_mul=0.01,
# Settings for conditional generation.
label_dim=0,
embedding_dim=512,
# Settings for synthesis network.
resolution=-1,
init_res=4,
image_channels=3,
final_tanh=False,
fused_scale='auto',
fused_scale_res=128,
use_wscale=True,
wscale_gain=np.sqrt(2.0),
lr_mul=1.0,
noise_type='spatial',
fmaps_base=16 << 10,
fmaps_max=512,
filter_kernel=(1, 2, 1),
eps=1e-8):
"""Initializes with basic settings.
Raises:
ValueError: If the `resolution` is not supported, or `fused_scale`
is not supported.
"""
super().__init__()
if resolution not in _RESOLUTIONS_ALLOWED:
raise ValueError(f'Invalid resolution: `{resolution}`!\n'
f'Resolutions allowed: {_RESOLUTIONS_ALLOWED}.')
if fused_scale not in _FUSED_SCALE_ALLOWED:
raise ValueError(f'Invalid fused-scale option: `{fused_scale}`!\n'
f'Options allowed: {_FUSED_SCALE_ALLOWED}.')
self.z_dim = z_dim
self.w_dim = w_dim
self.repeat_w = repeat_w
self.normalize_z = normalize_z
self.mapping_layers = mapping_layers
self.mapping_fmaps = mapping_fmaps
self.mapping_use_wscale = mapping_use_wscale
self.mapping_wscale_gain = mapping_wscale_gain
self.mapping_lr_mul = mapping_lr_mul
self.label_dim = label_dim
self.embedding_dim = embedding_dim
self.resolution = resolution
self.init_res = init_res
self.image_channels = image_channels
self.final_tanh = final_tanh
self.fused_scale = fused_scale
self.fused_scale_res = fused_scale_res
self.use_wscale = use_wscale
self.wscale_gain = wscale_gain
self.lr_mul = lr_mul
self.noise_type = noise_type.lower()
self.fmaps_base = fmaps_base
self.fmaps_max = fmaps_max
self.filter_kernel = filter_kernel
self.eps = eps
# Dimension of latent space, which is convenient for sampling.
self.latent_dim = (z_dim,)
# Number of synthesis (convolutional) layers.
self.num_layers = int(np.log2(resolution // init_res * 2)) * 2
self.mapping = MappingNetwork(input_dim=z_dim,
output_dim=w_dim,
num_outputs=self.num_layers,
repeat_output=repeat_w,
normalize_input=normalize_z,
num_layers=mapping_layers,
hidden_dim=mapping_fmaps,
use_wscale=mapping_use_wscale,
wscale_gain=mapping_wscale_gain,
lr_mul=mapping_lr_mul,
label_dim=label_dim,
embedding_dim=embedding_dim,
eps=eps)
# This is used for truncation trick.
if self.repeat_w:
self.register_buffer('w_avg', torch.zeros(w_dim))
else:
self.register_buffer('w_avg', torch.zeros(self.num_layers * w_dim))
self.synthesis = SynthesisNetwork(resolution=resolution,
init_res=init_res,
w_dim=w_dim,
image_channels=image_channels,
final_tanh=final_tanh,
fused_scale=fused_scale,
fused_scale_res=fused_scale_res,
use_wscale=use_wscale,
wscale_gain=wscale_gain,
lr_mul=lr_mul,
noise_type=noise_type,
fmaps_base=fmaps_base,
fmaps_max=fmaps_max,
filter_kernel=filter_kernel,
eps=eps)
self.pth_to_tf_var_mapping = {'w_avg': 'dlatent_avg'}
for key, val in self.mapping.pth_to_tf_var_mapping.items():
self.pth_to_tf_var_mapping[f'mapping.{key}'] = val
for key, val in self.synthesis.pth_to_tf_var_mapping.items():
self.pth_to_tf_var_mapping[f'synthesis.{key}'] = val
def set_space_of_latent(self, space_of_latent):
"""Sets the space to which the latent code belong.
See `SynthesisNetwork` for more details.
"""
self.synthesis.set_space_of_latent(space_of_latent)
def forward(self,
z,
label=None,
lod=None,
w_moving_decay=None,
sync_w_avg=False,
style_mixing_prob=None,
trunc_psi=None,
trunc_layers=None,
noise_mode='const',
enable_amp=False):
mapping_results = self.mapping(z, label)
w = mapping_results['w']
if self.training and w_moving_decay is not None:
if sync_w_avg:
batch_w_avg = all_gather(w.detach()).mean(dim=0)
else:
batch_w_avg = w.detach().mean(dim=0)
self.w_avg.copy_(batch_w_avg.lerp(self.w_avg, w_moving_decay))
wp = mapping_results.pop('wp')
if self.training and style_mixing_prob is not None:
if np.random.uniform() < style_mixing_prob:
new_z = torch.randn_like(z)
new_wp = self.mapping(new_z, label)['wp']
lod = self.synthesis.lod.item() if lod is None else lod
current_layers = self.num_layers - int(lod) * 2
mixing_cutoff = np.random.randint(1, current_layers)
wp[:, mixing_cutoff:] = new_wp[:, mixing_cutoff:]
if not self.training:
trunc_psi = 1.0 if trunc_psi is None else trunc_psi
trunc_layers = 0 if trunc_layers is None else trunc_layers
if trunc_psi < 1.0 and trunc_layers > 0:
w_avg = self.w_avg.reshape(1, -1, self.w_dim)[:, :trunc_layers]
wp[:, :trunc_layers] = w_avg.lerp(
wp[:, :trunc_layers], trunc_psi)
with autocast(enabled=enable_amp):
synthesis_results = self.synthesis(wp,
lod=lod,
noise_mode=noise_mode)
return {**mapping_results, **synthesis_results}
class MappingNetwork(nn.Module):
"""Implements the latent space mapping module.
Basically, this module executes several dense layers in sequence, and the
label embedding if needed.
"""
def __init__(self,
input_dim,
output_dim,
num_outputs,
repeat_output,
normalize_input,
num_layers,
hidden_dim,
use_wscale,
wscale_gain,
lr_mul,
label_dim,
embedding_dim,
eps):
super().__init__()
self.input_dim = input_dim
self.output_dim = output_dim
self.num_outputs = num_outputs
self.repeat_output = repeat_output
self.normalize_input = normalize_input
self.num_layers = num_layers
self.hidden_dim = hidden_dim
self.use_wscale = use_wscale
self.wscale_gain = wscale_gain
self.lr_mul = lr_mul
self.label_dim = label_dim
self.embedding_dim = embedding_dim
self.eps = eps
self.pth_to_tf_var_mapping = {}
if normalize_input:
self.norm = PixelNormLayer(dim=1, eps=eps)
if self.label_dim > 0:
input_dim = input_dim + embedding_dim
self.embedding = nn.Parameter(
torch.randn(label_dim, embedding_dim))
self.pth_to_tf_var_mapping['embedding'] = 'LabelConcat/weight'
if num_outputs is not None and not repeat_output:
output_dim = output_dim * num_outputs
for i in range(num_layers):
in_channels = (input_dim if i == 0 else hidden_dim)
out_channels = (output_dim if i == (num_layers - 1) else hidden_dim)
self.add_module(f'dense{i}',
DenseLayer(in_channels=in_channels,
out_channels=out_channels,
add_bias=True,
use_wscale=use_wscale,
wscale_gain=wscale_gain,
lr_mul=lr_mul,
activation_type='lrelu'))
self.pth_to_tf_var_mapping[f'dense{i}.weight'] = f'Dense{i}/weight'
self.pth_to_tf_var_mapping[f'dense{i}.bias'] = f'Dense{i}/bias'
def forward(self, z, label=None):
if z.ndim != 2 or z.shape[1] != self.input_dim:
raise ValueError(f'Input latent code should be with shape '
f'[batch_size, input_dim], where '
f'`input_dim` equals to {self.input_dim}!\n'
f'But `{z.shape}` is received!')
if self.label_dim > 0:
if label is None:
raise ValueError(f'Model requires an additional label '
f'(with dimension {self.label_dim}) as input, '
f'but no label is received!')
if label.ndim != 2 or label.shape != (z.shape[0], self.label_dim):
raise ValueError(f'Input label should be with shape '
f'[batch_size, label_dim], where '
f'`batch_size` equals to that of '
f'latent codes ({z.shape[0]}) and '
f'`label_dim` equals to {self.label_dim}!\n'
f'But `{label.shape}` is received!')
label = label.to(dtype=torch.float32)
embedding = torch.matmul(label, self.embedding)
z = torch.cat((z, embedding), dim=1)
if self.normalize_input:
w = self.norm(z)
else:
w = z
for i in range(self.num_layers):
w = getattr(self, f'dense{i}')(w)
wp = None
if self.num_outputs is not None:
if self.repeat_output:
wp = w.unsqueeze(1).repeat((1, self.num_outputs, 1))
else:
wp = w.reshape(-1, self.num_outputs, self.output_dim)
results = {
'z': z,
'label': label,
'w': w,
'wp': wp,
}
if self.label_dim > 0:
results['embedding'] = embedding
return results
class SynthesisNetwork(nn.Module):
"""Implements the image synthesis module.
Basically, this module executes several convolutional layers in sequence.
"""
def __init__(self,
resolution,
init_res,
w_dim,
image_channels,
final_tanh,
fused_scale,
fused_scale_res,
use_wscale,
wscale_gain,
lr_mul,
noise_type,
fmaps_base,
fmaps_max,
filter_kernel,
eps):
super().__init__()
self.init_res = init_res
self.init_res_log2 = int(np.log2(init_res))
self.resolution = resolution
self.final_res_log2 = int(np.log2(resolution))
self.w_dim = w_dim
self.image_channels = image_channels
self.final_tanh = final_tanh
self.fused_scale = fused_scale
self.fused_scale_res = fused_scale_res
self.use_wscale = use_wscale
self.wscale_gain = wscale_gain
self.lr_mul = lr_mul
self.noise_type = noise_type.lower()
self.fmaps_base = fmaps_base
self.fmaps_max = fmaps_max
self.eps = eps
self.num_layers = (self.final_res_log2 - self.init_res_log2 + 1) * 2
# Level-of-details (used for progressive training).
self.register_buffer('lod', torch.zeros(()))
self.pth_to_tf_var_mapping = {'lod': 'lod'}
for res_log2 in range(self.init_res_log2, self.final_res_log2 + 1):
res = 2 ** res_log2
in_channels = self.get_nf(res // 2)
out_channels = self.get_nf(res)
block_idx = res_log2 - self.init_res_log2
# First layer (kernel 3x3) with upsampling
layer_name = f'layer{2 * block_idx}'
if res == self.init_res:
self.add_module(layer_name,
ModulateConvLayer(in_channels=0,
out_channels=out_channels,
resolution=res,
w_dim=w_dim,
kernel_size=None,
add_bias=True,
scale_factor=None,
fused_scale=None,
filter_kernel=None,
use_wscale=use_wscale,
wscale_gain=wscale_gain,
lr_mul=lr_mul,
noise_type=noise_type,
activation_type='lrelu',
use_style=True,
eps=eps))
tf_layer_name = 'Const'
self.pth_to_tf_var_mapping[f'{layer_name}.const'] = (
f'{res}x{res}/{tf_layer_name}/const')
else:
self.add_module(
layer_name,
ModulateConvLayer(in_channels=in_channels,
out_channels=out_channels,
resolution=res,
w_dim=w_dim,
kernel_size=3,
add_bias=True,
scale_factor=2,
fused_scale=(res >= fused_scale_res
if fused_scale == 'auto'
else fused_scale),
filter_kernel=filter_kernel,
use_wscale=use_wscale,
wscale_gain=wscale_gain,
lr_mul=lr_mul,
noise_type=noise_type,
activation_type='lrelu',
use_style=True,
eps=eps))
tf_layer_name = 'Conv0_up'
self.pth_to_tf_var_mapping[f'{layer_name}.weight'] = (
f'{res}x{res}/{tf_layer_name}/weight')
self.pth_to_tf_var_mapping[f'{layer_name}.bias'] = (
f'{res}x{res}/{tf_layer_name}/bias')
self.pth_to_tf_var_mapping[f'{layer_name}.style.weight'] = (
f'{res}x{res}/{tf_layer_name}/StyleMod/weight')
self.pth_to_tf_var_mapping[f'{layer_name}.style.bias'] = (
f'{res}x{res}/{tf_layer_name}/StyleMod/bias')
self.pth_to_tf_var_mapping[f'{layer_name}.noise_strength'] = (
f'{res}x{res}/{tf_layer_name}/Noise/weight')
self.pth_to_tf_var_mapping[f'{layer_name}.noise'] = (
f'noise{2 * block_idx}')
# Second layer (kernel 3x3) without upsampling.
layer_name = f'layer{2 * block_idx + 1}'
self.add_module(layer_name,
ModulateConvLayer(in_channels=out_channels,
out_channels=out_channels,
resolution=res,
w_dim=w_dim,
kernel_size=3,
add_bias=True,
scale_factor=1,
fused_scale=False,
filter_kernel=None,
use_wscale=use_wscale,
wscale_gain=wscale_gain,
lr_mul=lr_mul,
noise_type=noise_type,
activation_type='lrelu',
use_style=True,
eps=eps))
tf_layer_name = 'Conv' if res == self.init_res else 'Conv1'
self.pth_to_tf_var_mapping[f'{layer_name}.weight'] = (
f'{res}x{res}/{tf_layer_name}/weight')
self.pth_to_tf_var_mapping[f'{layer_name}.bias'] = (
f'{res}x{res}/{tf_layer_name}/bias')
self.pth_to_tf_var_mapping[f'{layer_name}.style.weight'] = (
f'{res}x{res}/{tf_layer_name}/StyleMod/weight')
self.pth_to_tf_var_mapping[f'{layer_name}.style.bias'] = (
f'{res}x{res}/{tf_layer_name}/StyleMod/bias')
self.pth_to_tf_var_mapping[f'{layer_name}.noise_strength'] = (
f'{res}x{res}/{tf_layer_name}/Noise/weight')
self.pth_to_tf_var_mapping[f'{layer_name}.noise'] = (
f'noise{2 * block_idx + 1}')
# Output convolution layer for each resolution.
self.add_module(f'output{block_idx}',
ModulateConvLayer(in_channels=out_channels,
out_channels=image_channels,
resolution=res,
w_dim=w_dim,
kernel_size=1,
add_bias=True,
scale_factor=1,
fused_scale=False,
filter_kernel=None,
use_wscale=use_wscale,
wscale_gain=1.0,
lr_mul=lr_mul,
noise_type='none',
activation_type='linear',
use_style=False,
eps=eps))
self.pth_to_tf_var_mapping[f'output{block_idx}.weight'] = (
f'ToRGB_lod{self.final_res_log2 - res_log2}/weight')
self.pth_to_tf_var_mapping[f'output{block_idx}.bias'] = (
f'ToRGB_lod{self.final_res_log2 - res_log2}/bias')
def get_nf(self, res):
"""Gets number of feature maps according to the given resolution."""
return min(self.fmaps_base // res, self.fmaps_max)
def set_space_of_latent(self, space_of_latent):
"""Sets the space to which the latent code belong.
This function is particularly used for choosing how to inject the latent
code into the convolutional layers. The original generator will take a
W-Space code and apply it for style modulation after an affine
transformation. But, sometimes, it may need to directly feed an already
affine-transformed code into the convolutional layer, e.g., when
training an encoder for GAN inversion. We term the transformed space as
Style Space (or Y-Space). This function is designed to tell the
convolutional layers how to use the input code.
Args:
space_of_latent: The space to which the latent code belong. Case
insensitive. Support `W` and `Y`.
"""
space_of_latent = space_of_latent.upper()
for module in self.modules():
if isinstance(module, ModulateConvLayer) and module.use_style:
setattr(module, 'space_of_latent', space_of_latent)
def forward(self, wp, lod=None, noise_mode='const'):
lod = self.lod.item() if lod is None else lod
if lod + self.init_res_log2 > self.final_res_log2:
raise ValueError(f'Maximum level-of-details (lod) is '
f'{self.final_res_log2 - self.init_res_log2}, '
f'but `{lod}` is received!')
results = {'wp': wp}
x = None
for res_log2 in range(self.init_res_log2, self.final_res_log2 + 1):
current_lod = self.final_res_log2 - res_log2
block_idx = res_log2 - self.init_res_log2
if lod < current_lod + 1:
layer = getattr(self, f'layer{2 * block_idx}')
x, style = layer(x, wp[:, 2 * block_idx], noise_mode)
results[f'style{2 * block_idx}'] = style
layer = getattr(self, f'layer{2 * block_idx + 1}')
x, style = layer(x, wp[:, 2 * block_idx + 1], noise_mode)
results[f'style{2 * block_idx + 1}'] = style
if current_lod - 1 < lod <= current_lod:
image = getattr(self, f'output{block_idx}')(x)
elif current_lod < lod < current_lod + 1:
alpha = np.ceil(lod) - lod
temp = getattr(self, f'output{block_idx}')(x)
image = F.interpolate(image, scale_factor=2, mode='nearest')
image = temp * alpha + image * (1 - alpha)
elif lod >= current_lod + 1:
image = F.interpolate(image, scale_factor=2, mode='nearest')
if self.final_tanh:
image = torch.tanh(image)
results['image'] = image
return results
class PixelNormLayer(nn.Module):
"""Implements pixel-wise feature vector normalization layer."""
def __init__(self, dim, eps):
super().__init__()
self.dim = dim
self.eps = eps
def extra_repr(self):
return f'dim={self.dim}, epsilon={self.eps}'
def forward(self, x):
scale = (x.square().mean(dim=self.dim, keepdim=True) + self.eps).rsqrt()
return x * scale
class Blur(torch.autograd.Function):
"""Defines blur operation with customized gradient computation."""
@staticmethod
def forward(ctx, x, kernel):
assert kernel.shape[2] == 3 and kernel.shape[3] == 3
ctx.save_for_backward(kernel)
y = F.conv2d(input=x,
weight=kernel,
bias=None,
stride=1,
padding=1,
groups=x.shape[1])
return y
@staticmethod
def backward(ctx, dy):
kernel, = ctx.saved_tensors
dx = F.conv2d(input=dy,
weight=kernel.flip((2, 3)),
bias=None,
stride=1,
padding=1,
groups=dy.shape[1])
return dx, None, None
class ModulateConvLayer(nn.Module):
"""Implements the convolutional layer with style modulation."""
def __init__(self,
in_channels,
out_channels,
resolution,
w_dim,
kernel_size,
add_bias,
scale_factor,
fused_scale,
filter_kernel,
use_wscale,
wscale_gain,
lr_mul,
noise_type,
activation_type,
use_style,
eps):
"""Initializes with layer settings.
Args:
in_channels: Number of channels of the input tensor.
out_channels: Number of channels of the output tensor.
resolution: Resolution of the output tensor.
w_dim: Dimension of W space for style modulation.
kernel_size: Size of the convolutional kernels.
add_bias: Whether to add bias onto the convolutional result.
scale_factor: Scale factor for upsampling.
fused_scale: Whether to fuse `upsample` and `conv2d` as one
operator, using transpose convolution.
filter_kernel: Kernel used for filtering.
use_wscale: Whether to use weight scaling.
wscale_gain: Gain factor for weight scaling.
lr_mul: Learning multiplier for both weight and bias.
noise_type: Type of noise added to the feature map after the
convolution (if needed). Support `none`, `spatial` and
`channel`.
activation_type: Type of activation.
use_style: Whether to apply style modulation.
eps: A small value to avoid divide overflow.
"""
super().__init__()
self.in_channels = in_channels
self.out_channels = out_channels
self.resolution = resolution
self.w_dim = w_dim
self.kernel_size = kernel_size
self.add_bias = add_bias
self.scale_factor = scale_factor
self.fused_scale = fused_scale
self.filter_kernel = filter_kernel
self.use_wscale = use_wscale
self.wscale_gain = wscale_gain
self.lr_mul = lr_mul
self.noise_type = noise_type.lower()
self.activation_type = activation_type
self.use_style = use_style
self.eps = eps
# Set up noise.
if self.noise_type == 'none':
pass
elif self.noise_type == 'spatial':
self.register_buffer(
'noise', torch.randn(1, 1, resolution, resolution))
self.noise_strength = nn.Parameter(
torch.zeros(1, out_channels, 1, 1))
elif self.noise_type == 'channel':
self.register_buffer(
'noise', torch.randn(1, out_channels, 1, 1))
self.noise_strength = nn.Parameter(
torch.zeros(1, 1, resolution, resolution))
else:
raise NotImplementedError(f'Not implemented noise type: '
f'`{noise_type}`!')
# Set up bias.
if add_bias:
self.bias = nn.Parameter(torch.zeros(out_channels))
self.bscale = lr_mul
else:
self.bias = None
# Set up activation.
assert activation_type in ['linear', 'relu', 'lrelu']
# Set up style.
if use_style:
self.space_of_latent = 'W'
self.style = DenseLayer(in_channels=w_dim,
out_channels=out_channels * 2,
add_bias=True,
use_wscale=use_wscale,
wscale_gain=1.0,
lr_mul=1.0,
activation_type='linear')
if in_channels == 0: # First layer.
self.const = nn.Parameter(
torch.ones(1, out_channels, resolution, resolution))
return
# Set up weight.
weight_shape = (out_channels, in_channels, kernel_size, kernel_size)
fan_in = kernel_size * kernel_size * in_channels
wscale = wscale_gain / np.sqrt(fan_in)
if use_wscale:
self.weight = nn.Parameter(torch.randn(*weight_shape) / lr_mul)
self.wscale = wscale * lr_mul
else:
self.weight = nn.Parameter(
torch.randn(*weight_shape) * wscale / lr_mul)
self.wscale = lr_mul
# Set up upsampling filter (if needed).
if scale_factor > 1:
assert filter_kernel is not None
kernel = np.array(filter_kernel, dtype=np.float32).reshape(1, -1)
kernel = kernel.T.dot(kernel)
kernel = kernel / np.sum(kernel)
kernel = kernel[np.newaxis, np.newaxis]
self.register_buffer('filter', torch.from_numpy(kernel))
if scale_factor > 1 and fused_scale: # use transpose convolution.
self.stride = scale_factor
else:
self.stride = 1
self.padding = kernel_size // 2
def extra_repr(self):
return (f'in_ch={self.in_channels}, '
f'out_ch={self.out_channels}, '
f'ksize={self.kernel_size}, '
f'wscale_gain={self.wscale_gain:.3f}, '
f'bias={self.add_bias}, '
f'lr_mul={self.lr_mul:.3f}, '
f'upsample={self.scale_factor}, '
f'fused_scale={self.fused_scale}, '
f'upsample_filter={self.filter_kernel}, '
f'noise_type={self.noise_type}, '
f'act={self.activation_type}, '
f'use_style={self.use_style}')
def forward_style(self, w):
"""Gets style code from the given input.
More specifically, if the input is from W-Space, it will be projected by
an affine transformation. If it is from the Style Space (Y-Space), no
operation is required.
NOTE: For codes from Y-Space, we use slicing to make sure the dimension
is correct, in case that the code is padded before fed into this layer.
"""
space_of_latent = self.space_of_latent.upper()
if space_of_latent == 'W':
if w.ndim != 2 or w.shape[1] != self.w_dim:
raise ValueError(f'The input tensor should be with shape '
f'[batch_size, w_dim], where '
f'`w_dim` equals to {self.w_dim}!\n'
f'But `{w.shape}` is received!')
style = self.style(w)
elif space_of_latent == 'Y':
if w.ndim != 2 or w.shape[1] < self.out_channels * 2:
raise ValueError(f'The input tensor should be with shape '
f'[batch_size, y_dim], where '
f'`y_dim` equals to {self.out_channels * 2}!\n'
f'But `{w.shape}` is received!')
style = w[:, :self.out_channels * 2]
else:
raise NotImplementedError(f'Not implemented `space_of_latent`: '
f'`{space_of_latent}`!')
return style
def forward(self, x, w=None, noise_mode='const'):
if self.in_channels == 0:
assert x is None
x = self.const.repeat(w.shape[0], 1, 1, 1)
else:
weight = self.weight
if self.wscale != 1.0:
weight = weight * self.wscale
if self.scale_factor > 1 and self.fused_scale:
weight = F.pad(weight, (1, 1, 1, 1, 0, 0, 0, 0), 'constant', 0)
weight = (weight[:, :, 1:, 1:] + weight[:, :, :-1, 1:] +
weight[:, :, 1:, :-1] + weight[:, :, :-1, :-1])
x = F.conv_transpose2d(x,
weight=weight.transpose(0, 1),
bias=None,
stride=self.stride,
padding=self.padding)
else:
if self.scale_factor > 1:
up = self.scale_factor
x = F.interpolate(x, scale_factor=up, mode='nearest')
x = F.conv2d(x,
weight=weight,
bias=None,
stride=self.stride,
padding=self.padding)
if self.scale_factor > 1:
# Disable `autocast` for customized autograd function.
# Please check reference:
# https://pytorch.org/docs/stable/notes/amp_examples.html#autocast-and-custom-autograd-functions
with autocast(enabled=False):
f = self.filter.repeat(self.out_channels, 1, 1, 1)
x = Blur.apply(x.float(), f) # Always use FP32.
# Prepare noise.
noise_mode = noise_mode.lower()
if self.noise_type != 'none' and noise_mode != 'none':
if noise_mode == 'random':
noise = torch.randn(
(x.shape[0], *self.noise.shape[1:]), device=x.device)
elif noise_mode == 'const':
noise = self.noise
else:
raise ValueError(f'Unknown noise mode `{noise_mode}`!')
x = x + noise * self.noise_strength
if self.bias is not None:
bias = self.bias
if self.bscale != 1.0:
bias = bias * self.bscale
x = x + bias.reshape(1, self.out_channels, 1, 1)
if self.activation_type == 'linear':
pass
elif self.activation_type == 'relu':
x = F.relu(x, inplace=True)
elif self.activation_type == 'lrelu':
x = F.leaky_relu(x, negative_slope=0.2, inplace=True)
else:
raise NotImplementedError(f'Not implemented activation type '
f'`{self.activation_type}`!')
if not self.use_style:
return x
# Instance normalization.
x = x - x.mean(dim=(2, 3), keepdim=True)
scale = (x.square().mean(dim=(2, 3), keepdim=True) + self.eps).rsqrt()
x = x * scale
# Style modulation.
style = self.forward_style(w)
style_split = style.unsqueeze(2).unsqueeze(3).chunk(2, dim=1)
x = x * (style_split[0] + 1) + style_split[1]
return x, style
class DenseLayer(nn.Module):
"""Implements the dense layer."""
def __init__(self,
in_channels,
out_channels,
add_bias,
use_wscale,
wscale_gain,
lr_mul,
activation_type):
"""Initializes with layer settings.
Args:
in_channels: Number of channels of the input tensor.
out_channels: Number of channels of the output tensor.
add_bias: Whether to add bias onto the fully-connected result.
use_wscale: Whether to use weight scaling.
wscale_gain: Gain factor for weight scaling.
lr_mul: Learning multiplier for both weight and bias.
activation_type: Type of activation.
"""
super().__init__()
self.in_channels = in_channels
self.out_channels = out_channels
self.add_bias = add_bias
self.use_wscale = use_wscale
self.wscale_gain = wscale_gain
self.lr_mul = lr_mul
self.activation_type = activation_type
weight_shape = (out_channels, in_channels)
wscale = wscale_gain / np.sqrt(in_channels)
if use_wscale:
self.weight = nn.Parameter(torch.randn(*weight_shape) / lr_mul)
self.wscale = wscale * lr_mul
else:
self.weight = nn.Parameter(
torch.randn(*weight_shape) * wscale / lr_mul)
self.wscale = lr_mul
if add_bias:
self.bias = nn.Parameter(torch.zeros(out_channels))
self.bscale = lr_mul
else:
self.bias = None
assert activation_type in ['linear', 'relu', 'lrelu']
def extra_repr(self):
return (f'in_ch={self.in_channels}, '
f'out_ch={self.out_channels}, '
f'wscale_gain={self.wscale_gain:.3f}, '
f'bias={self.add_bias}, '
f'lr_mul={self.lr_mul:.3f}, '
f'act={self.activation_type}')
def forward(self, x):
if x.ndim != 2:
x = x.flatten(start_dim=1)
weight = self.weight
if self.wscale != 1.0:
weight = weight * self.wscale
bias = None
if self.bias is not None:
bias = self.bias
if self.bscale != 1.0:
bias = bias * self.bscale
x = F.linear(x, weight=weight, bias=bias)
if self.activation_type == 'linear':
pass
elif self.activation_type == 'relu':
x = F.relu(x, inplace=True)
elif self.activation_type == 'lrelu':
x = F.leaky_relu(x, negative_slope=0.2, inplace=True)
else:
raise NotImplementedError(f'Not implemented activation type '
f'`{self.activation_type}`!')
return x
# pylint: enable=missing-function-docstring
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