# python3.7
"""Contains the implementation of generator described in StyleGAN2.
Compared to that of StyleGAN, the generator in StyleGAN2 mainly introduces style
demodulation, adds skip connections, increases model size, and disables
progressive growth. This script ONLY supports config F in the original paper.
Paper: https://arxiv.org/pdf/1912.04958.pdf
Official TensorFlow implementation: https://github.com/NVlabs/stylegan2
"""
import numpy as np
import torch
import torch.nn as nn
from third_party.stylegan2_official_ops import fma
from third_party.stylegan2_official_ops import bias_act
from third_party.stylegan2_official_ops import upfirdn2d
from third_party.stylegan2_official_ops import conv2d_gradfix
from .utils.ops import all_gather
__all__ = ['StyleGAN2Generator']
# Resolutions allowed.
_RESOLUTIONS_ALLOWED = [8, 16, 32, 64, 128, 256, 512, 1024]
# Architectures allowed.
_ARCHITECTURES_ALLOWED = ['resnet', 'skip', 'origin']
# pylint: disable=missing-function-docstring
class StyleGAN2Generator(nn.Module):
"""Defines the generator network in StyleGAN2.
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: 1.0)
(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)
(3) embedding_bias: Whether to add bias to embedding learning.
(default: True)
(4) embedding_use_wscale: Whether to use weight scaling for embedding
learning. (default: True)
(5) embedding_wscale_gain: The factor to control weight scaling for
embedding. (default: 1.0)
(6) embedding_lr_mul: Learning rate multiplier for the embedding learning.
(default: 1.0)
(7) normalize_embedding: Whether to normalize the embedding. (default: True)
(8) normalize_embedding_latent: Whether to normalize the embedding together
with the latent. (default: False)
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) const_input: Whether to use a constant in the first convolutional layer.
(default: True)
(6) architecture: Type of architecture. Support `origin`, `skip`, and
`resnet`. (default: `skip`)
(7) demodulate: Whether to perform style demodulation. (default: True)
(8) use_wscale: Whether to use weight scaling. (default: True)
(9) wscale_gain: The factor to control weight scaling. (default: 1.0)
(10) lr_mul: Learning rate multiplier for the synthesis network.
(default: 1.0)
(11) noise_type: Type of noise added to the convolutional results at each
layer. (default: `spatial`)
(12) fmaps_base: Factor to control number of feature maps for each layer.
(default: 32 << 10)
(13) fmaps_max: Maximum number of feature maps in each layer. (default: 512)
(14) filter_kernel: Kernel used for filtering (e.g., downsampling).
(default: (1, 3, 3, 1))
(15) conv_clamp: A threshold to clamp the output of convolution layers to
avoid overflow under FP16 training. (default: None)
(16) 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) fused_modulate: Whether to fuse `style_modulate` and `conv2d` together.
(default: False)
(8) fp16_res: Layers at resolution higher than (or equal to) this field will
use `float16` precision for computation. This is merely used for
acceleration. If set as `None`, all layers will use `float32` by
default. (default: None)
(9) impl: Implementation mode of some particular ops, e.g., `filtering`,
`bias_act`, etc. `cuda` means using the official CUDA implementation
from StyleGAN2, while `ref` means using the native PyTorch ops.
(default: `cuda`)
"""
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=1.0,
mapping_lr_mul=0.01,
# Settings for conditional generation.
label_dim=0,
embedding_dim=512,
embedding_bias=True,
embedding_use_wscale=True,
embedding_wscale_gian=1.0,
embedding_lr_mul=1.0,
normalize_embedding=True,
normalize_embedding_latent=False,
# Settings for synthesis network.
resolution=-1,
init_res=4,
image_channels=3,
final_tanh=False,
const_input=True,
architecture='skip',
demodulate=True,
use_wscale=True,
wscale_gain=1.0,
lr_mul=1.0,
noise_type='spatial',
fmaps_base=32 << 10,
fmaps_max=512,
filter_kernel=(1, 3, 3, 1),
conv_clamp=None,
eps=1e-8):
"""Initializes with basic settings.
Raises:
ValueError: If the `resolution` is not supported, or `architecture`
is not supported.
"""
super().__init__()
if resolution not in _RESOLUTIONS_ALLOWED:
raise ValueError(f'Invalid resolution: `{resolution}`!\n'
f'Resolutions allowed: {_RESOLUTIONS_ALLOWED}.')
architecture = architecture.lower()
if architecture not in _ARCHITECTURES_ALLOWED:
raise ValueError(f'Invalid architecture: `{architecture}`!\n'
f'Architectures allowed: '
f'{_ARCHITECTURES_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.embedding_bias = embedding_bias
self.embedding_use_wscale = embedding_use_wscale
self.embedding_wscale_gain = embedding_wscale_gian
self.embedding_lr_mul = embedding_lr_mul
self.normalize_embedding = normalize_embedding
self.normalize_embedding_latent = normalize_embedding_latent
self.resolution = resolution
self.init_res = init_res
self.image_channels = image_channels
self.final_tanh = final_tanh
self.const_input = const_input
self.architecture = architecture
self.demodulate = demodulate
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.conv_clamp = conv_clamp
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,
embedding_bias=embedding_bias,
embedding_use_wscale=embedding_use_wscale,
embedding_wscale_gian=embedding_wscale_gian,
embedding_lr_mul=embedding_lr_mul,
normalize_embedding=normalize_embedding,
normalize_embedding_latent=normalize_embedding_latent,
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,
const_input=const_input,
architecture=architecture,
demodulate=demodulate,
use_wscale=use_wscale,
wscale_gain=wscale_gain,
lr_mul=lr_mul,
noise_type=noise_type,
fmaps_base=fmaps_base,
filter_kernel=filter_kernel,
fmaps_max=fmaps_max,
conv_clamp=conv_clamp,
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,
w_moving_decay=None,
sync_w_avg=False,
style_mixing_prob=None,
trunc_psi=None,
trunc_layers=None,
noise_mode='const',
fused_modulate=False,
fp16_res=None,
impl='cuda'):
"""Connects mapping network and synthesis network.
This forward function will also update the average `w_code`, perform
style mixing as a training regularizer, and do truncation trick, which
is specially designed for inference.
Concretely, the truncation trick acts as follows:
For layers in range [0, truncation_layers), the truncated w-code is
computed as
w_new = w_avg + (w - w_avg) * truncation_psi
To disable truncation, please set
(1) truncation_psi = 1.0 (None) OR
(2) truncation_layers = 0 (None)
"""
mapping_results = self.mapping(z, label, impl=impl)
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, impl=impl)['wp']
mixing_cutoff = np.random.randint(1, self.num_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)
synthesis_results = self.synthesis(wp,
noise_mode=noise_mode,
fused_modulate=fused_modulate,
impl=impl,
fp16_res=fp16_res)
return {**mapping_results, **synthesis_results}
class MappingNetwork(nn.Module):
"""Implements the latent space mapping network.
Basically, this network 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,
embedding_bias,
embedding_use_wscale,
embedding_wscale_gian,
embedding_lr_mul,
normalize_embedding,
normalize_embedding_latent,
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.embedding_bias = embedding_bias
self.embedding_use_wscale = embedding_use_wscale
self.embedding_wscale_gian = embedding_wscale_gian
self.embedding_lr_mul = embedding_lr_mul
self.normalize_embedding = normalize_embedding
self.normalize_embedding_latent = normalize_embedding_latent
self.eps = eps
self.pth_to_tf_var_mapping = {}
self.norm = PixelNormLayer(dim=1, eps=eps)
if self.label_dim > 0:
input_dim = input_dim + embedding_dim
self.embedding = DenseLayer(in_channels=label_dim,
out_channels=embedding_dim,
add_bias=embedding_bias,
init_bias=0.0,
use_wscale=embedding_use_wscale,
wscale_gain=embedding_wscale_gian,
lr_mul=embedding_lr_mul,
activation_type='linear')
self.pth_to_tf_var_mapping['embedding.weight'] = 'LabelEmbed/weight'
if self.embedding_bias:
self.pth_to_tf_var_mapping['embedding.bias'] = 'LabelEmbed/bias'
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,
init_bias=0.0,
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, impl='cuda'):
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.normalize_input:
z = self.norm(z)
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 = self.embedding(label, impl=impl)
if self.normalize_embedding:
embedding = self.norm(embedding)
w = torch.cat((z, embedding), dim=1)
else:
w = z
if self.label_dim > 0 and self.normalize_embedding_latent:
w = self.norm(w)
for i in range(self.num_layers):
w = getattr(self, f'dense{i}')(w, impl=impl)
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 network.
Basically, this network executes several convolutional layers in sequence.
"""
def __init__(self,
resolution,
init_res,
w_dim,
image_channels,
final_tanh,
const_input,
architecture,
demodulate,
use_wscale,
wscale_gain,
lr_mul,
noise_type,
fmaps_base,
fmaps_max,
filter_kernel,
conv_clamp,
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.const_input = const_input
self.architecture = architecture.lower()
self.demodulate = demodulate
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.conv_clamp = conv_clamp
self.eps = eps
self.num_layers = (self.final_res_log2 - self.init_res_log2 + 1) * 2
self.pth_to_tf_var_mapping = {}
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
# Early layer.
if res == init_res:
if self.const_input:
self.add_module('early_layer',
InputLayer(init_res=res,
channels=out_channels))
self.pth_to_tf_var_mapping['early_layer.const'] = (
f'{res}x{res}/Const/const')
else:
channels = out_channels * res * res
self.add_module('early_layer',
DenseLayer(in_channels=w_dim,
out_channels=channels,
add_bias=True,
init_bias=0.0,
use_wscale=use_wscale,
wscale_gain=wscale_gain,
lr_mul=lr_mul,
activation_type='lrelu'))
self.pth_to_tf_var_mapping['early_layer.weight'] = (
f'{res}x{res}/Dense/weight')
self.pth_to_tf_var_mapping['early_layer.bias'] = (
f'{res}x{res}/Dense/bias')
else:
# Residual branch (kernel 1x1) with upsampling, without bias,
# with linear activation.
if self.architecture == 'resnet':
layer_name = f'residual{block_idx}'
self.add_module(layer_name,
ConvLayer(in_channels=in_channels,
out_channels=out_channels,
kernel_size=1,
add_bias=False,
scale_factor=2,
filter_kernel=filter_kernel,
use_wscale=use_wscale,
wscale_gain=wscale_gain,
lr_mul=lr_mul,
activation_type='linear',
conv_clamp=None))
self.pth_to_tf_var_mapping[f'{layer_name}.weight'] = (
f'{res}x{res}/Skip/weight')
# First layer (kernel 3x3) with upsampling.
layer_name = f'layer{2 * block_idx - 1}'
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,
filter_kernel=filter_kernel,
demodulate=demodulate,
use_wscale=use_wscale,
wscale_gain=wscale_gain,
lr_mul=lr_mul,
noise_type=noise_type,
activation_type='lrelu',
conv_clamp=conv_clamp,
eps=eps))
self.pth_to_tf_var_mapping[f'{layer_name}.weight'] = (
f'{res}x{res}/Conv0_up/weight')
self.pth_to_tf_var_mapping[f'{layer_name}.bias'] = (
f'{res}x{res}/Conv0_up/bias')
self.pth_to_tf_var_mapping[f'{layer_name}.style.weight'] = (
f'{res}x{res}/Conv0_up/mod_weight')
self.pth_to_tf_var_mapping[f'{layer_name}.style.bias'] = (
f'{res}x{res}/Conv0_up/mod_bias')
self.pth_to_tf_var_mapping[f'{layer_name}.noise_strength'] = (
f'{res}x{res}/Conv0_up/noise_strength')
self.pth_to_tf_var_mapping[f'{layer_name}.noise'] = (
f'noise{2 * block_idx - 1}')
# Second layer (kernel 3x3) without upsampling.
layer_name = f'layer{2 * block_idx}'
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,
filter_kernel=None,
demodulate=demodulate,
use_wscale=use_wscale,
wscale_gain=wscale_gain,
lr_mul=lr_mul,
noise_type=noise_type,
activation_type='lrelu',
conv_clamp=conv_clamp,
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}/mod_weight')
self.pth_to_tf_var_mapping[f'{layer_name}.style.bias'] = (
f'{res}x{res}/{tf_layer_name}/mod_bias')
self.pth_to_tf_var_mapping[f'{layer_name}.noise_strength'] = (
f'{res}x{res}/{tf_layer_name}/noise_strength')
self.pth_to_tf_var_mapping[f'{layer_name}.noise'] = (
f'noise{2 * block_idx}')
# Output convolution layer for each resolution (if needed).
if res_log2 == self.final_res_log2 or self.architecture == 'skip':
layer_name = f'output{block_idx}'
self.add_module(layer_name,
ModulateConvLayer(in_channels=out_channels,
out_channels=image_channels,
resolution=res,
w_dim=w_dim,
kernel_size=1,
add_bias=True,
scale_factor=1,
filter_kernel=None,
demodulate=False,
use_wscale=use_wscale,
wscale_gain=wscale_gain,
lr_mul=lr_mul,
noise_type='none',
activation_type='linear',
conv_clamp=conv_clamp,
eps=eps))
self.pth_to_tf_var_mapping[f'{layer_name}.weight'] = (
f'{res}x{res}/ToRGB/weight')
self.pth_to_tf_var_mapping[f'{layer_name}.bias'] = (
f'{res}x{res}/ToRGB/bias')
self.pth_to_tf_var_mapping[f'{layer_name}.style.weight'] = (
f'{res}x{res}/ToRGB/mod_weight')
self.pth_to_tf_var_mapping[f'{layer_name}.style.bias'] = (
f'{res}x{res}/ToRGB/mod_bias')
# Used for upsampling output images for each resolution block for sum.
if self.architecture == 'skip':
self.register_buffer(
'filter', upfirdn2d.setup_filter(filter_kernel))
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):
setattr(module, 'space_of_latent', space_of_latent)
def forward(self,
wp,
noise_mode='const',
fused_modulate=False,
fp16_res=None,
impl='cuda'):
results = {'wp': wp}
if self.const_input:
x = self.early_layer(wp[:, 0])
else:
x = self.early_layer(wp[:, 0], impl=impl)
# Cast to `torch.float16` if needed.
if fp16_res is not None and self.init_res >= fp16_res:
x = x.to(torch.float16)
if self.architecture == 'origin':
for layer_idx in range(self.num_layers - 1):
layer = getattr(self, f'layer{layer_idx}')
x, style = layer(x,
wp[:, layer_idx],
noise_mode=noise_mode,
fused_modulate=fused_modulate,
impl=impl)
results[f'style{layer_idx}'] = style
# Cast to `torch.float16` if needed.
if layer_idx % 2 == 0 and layer_idx != self.num_layers - 2:
res = self.init_res * (2 ** (layer_idx // 2))
if fp16_res is not None and res * 2 >= fp16_res:
x = x.to(torch.float16)
else:
x = x.to(torch.float32)
output_layer = getattr(self, f'output{layer_idx // 2}')
image, style = output_layer(x,
wp[:, layer_idx + 1],
fused_modulate=fused_modulate,
impl=impl)
image = image.to(torch.float32)
results[f'output_style{layer_idx // 2}'] = style
elif self.architecture == 'skip':
for layer_idx in range(self.num_layers - 1):
layer = getattr(self, f'layer{layer_idx}')
x, style = layer(x,
wp[:, layer_idx],
noise_mode=noise_mode,
fused_modulate=fused_modulate,
impl=impl)
results[f'style{layer_idx}'] = style
if layer_idx % 2 == 0:
output_layer = getattr(self, f'output{layer_idx // 2}')
y, style = output_layer(x,
wp[:, layer_idx + 1],
fused_modulate=fused_modulate,
impl=impl)
results[f'output_style{layer_idx // 2}'] = style
if layer_idx == 0:
image = y.to(torch.float32)
else:
image = y.to(torch.float32) + upfirdn2d.upsample2d(
image, self.filter, impl=impl)
# Cast to `torch.float16` if needed.
if layer_idx != self.num_layers - 2:
res = self.init_res * (2 ** (layer_idx // 2))
if fp16_res is not None and res * 2 >= fp16_res:
x = x.to(torch.float16)
else:
x = x.to(torch.float32)
elif self.architecture == 'resnet':
x, style = self.layer0(x,
wp[:, 0],
noise_mode=noise_mode,
fused_modulate=fused_modulate,
impl=impl)
results['style0'] = style
for layer_idx in range(1, self.num_layers - 1, 2):
# Cast to `torch.float16` if needed.
if layer_idx % 2 == 1:
res = self.init_res * (2 ** (layer_idx // 2))
if fp16_res is not None and res * 2 >= fp16_res:
x = x.to(torch.float16)
else:
x = x.to(torch.float32)
skip_layer = getattr(self, f'residual{layer_idx // 2 + 1}')
residual = skip_layer(x, runtime_gain=np.sqrt(0.5), impl=impl)
layer = getattr(self, f'layer{layer_idx}')
x, style = layer(x,
wp[:, layer_idx],
noise_mode=noise_mode,
fused_modulate=fused_modulate,
impl=impl)
results[f'style{layer_idx}'] = style
layer = getattr(self, f'layer{layer_idx + 1}')
x, style = layer(x,
wp[:, layer_idx + 1],
runtime_gain=np.sqrt(0.5),
noise_mode=noise_mode,
fused_modulate=fused_modulate,
impl=impl)
results[f'style{layer_idx + 1}'] = style
x = x + residual
output_layer = getattr(self, f'output{layer_idx // 2 + 1}')
image, style = output_layer(x,
wp[:, layer_idx + 2],
fused_modulate=fused_modulate,
impl=impl)
image = image.to(torch.float32)
results[f'output_style{layer_idx // 2}'] = style
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 InputLayer(nn.Module):
"""Implements the input layer to start convolution with.
Basically, this block starts from a const input, which is with shape
`(channels, init_res, init_res)`.
"""
def __init__(self, init_res, channels):
super().__init__()
self.const = nn.Parameter(torch.randn(1, channels, init_res, init_res))
def forward(self, w):
x = self.const.repeat(w.shape[0], 1, 1, 1)
return x
class ConvLayer(nn.Module):
"""Implements the convolutional layer.
If upsampling is needed (i.e., `scale_factor = 2`), the feature map will
be filtered with `filter_kernel` after convolution. This layer will only be
used for skip connection in `resnet` architecture.
"""
def __init__(self,
in_channels,
out_channels,
kernel_size,
add_bias,
scale_factor,
filter_kernel,
use_wscale,
wscale_gain,
lr_mul,
activation_type,
conv_clamp):
"""Initializes with layer settings.
Args:
in_channels: Number of channels of the input tensor.
out_channels: Number of channels of the output tensor.
kernel_size: Size of the convolutional kernels.
add_bias: Whether to add bias onto the convolutional result.
scale_factor: Scale factor for upsampling.
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.
activation_type: Type of activation.
conv_clamp: A threshold to clamp the output of convolution layers to
avoid overflow under FP16 training.
"""
super().__init__()
self.in_channels = in_channels
self.out_channels = out_channels
self.kernel_size = kernel_size
self.add_bias = add_bias
self.scale_factor = scale_factor
self.filter_kernel = filter_kernel
self.use_wscale = use_wscale
self.wscale_gain = wscale_gain
self.lr_mul = lr_mul
self.activation_type = activation_type
self.conv_clamp = conv_clamp
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
if add_bias:
self.bias = nn.Parameter(torch.zeros(out_channels))
self.bscale = lr_mul
else:
self.bias = None
self.act_gain = bias_act.activation_funcs[activation_type].def_gain
if scale_factor > 1:
assert filter_kernel is not None
self.register_buffer(
'filter', upfirdn2d.setup_filter(filter_kernel))
fh, fw = self.filter.shape
self.filter_padding = (
kernel_size // 2 + (fw + scale_factor - 1) // 2,
kernel_size // 2 + (fw - scale_factor) // 2,
kernel_size // 2 + (fh + scale_factor - 1) // 2,
kernel_size // 2 + (fh - scale_factor) // 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'upsample_filter={self.filter_kernel}, '
f'act={self.activation_type}, '
f'clamp={self.conv_clamp}')
def forward(self, x, runtime_gain=1.0, impl='cuda'):
dtype = x.dtype
weight = self.weight
if self.wscale != 1.0:
weight = weight * self.wscale
bias = None
if self.bias is not None:
bias = self.bias.to(dtype)
if self.bscale != 1.0:
bias = bias * self.bscale
if self.scale_factor == 1: # Native convolution without upsampling.
padding = self.kernel_size // 2
x = conv2d_gradfix.conv2d(
x, weight.to(dtype), stride=1, padding=padding, impl=impl)
else: # Convolution with upsampling.
up = self.scale_factor
f = self.filter
# When kernel size = 1, use filtering function for upsampling.
if self.kernel_size == 1:
padding = self.filter_padding
x = conv2d_gradfix.conv2d(
x, weight.to(dtype), stride=1, padding=0, impl=impl)
x = upfirdn2d.upfirdn2d(
x, f, up=up, padding=padding, gain=up ** 2, impl=impl)
# When kernel size != 1, use transpose convolution for upsampling.
else:
# Following codes are borrowed from
# https://github.com/NVlabs/stylegan2-ada-pytorch
px0, px1, py0, py1 = self.filter_padding
kh, kw = weight.shape[2:]
px0 = px0 - (kw - 1)
px1 = px1 - (kw - up)
py0 = py0 - (kh - 1)
py1 = py1 - (kh - up)
pxt = max(min(-px0, -px1), 0)
pyt = max(min(-py0, -py1), 0)
weight = weight.transpose(0, 1)
padding = (pyt, pxt)
x = conv2d_gradfix.conv_transpose2d(
x, weight.to(dtype), stride=up, padding=padding, impl=impl)
padding = (px0 + pxt, px1 + pxt, py0 + pyt, py1 + pyt)
x = upfirdn2d.upfirdn2d(
x, f, up=1, padding=padding, gain=up ** 2, impl=impl)
act_gain = self.act_gain * runtime_gain
act_clamp = None
if self.conv_clamp is not None:
act_clamp = self.conv_clamp * runtime_gain
x = bias_act.bias_act(x, bias,
act=self.activation_type,
gain=act_gain,
clamp=act_clamp,
impl=impl)
assert x.dtype == dtype
return x
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,
filter_kernel,
demodulate,
use_wscale,
wscale_gain,
lr_mul,
noise_type,
activation_type,
conv_clamp,
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.
filter_kernel: Kernel used for filtering.
demodulate: Whether to perform style demodulation.
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.
conv_clamp: A threshold to clamp the output of convolution layers to
avoid overflow under FP16 training.
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.filter_kernel = filter_kernel
self.demodulate = demodulate
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.conv_clamp = conv_clamp
self.eps = eps
self.space_of_latent = 'W'
# 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 bias.
if add_bias:
self.bias = nn.Parameter(torch.zeros(out_channels))
self.bscale = lr_mul
else:
self.bias = None
self.act_gain = bias_act.activation_funcs[activation_type].def_gain
# Set up style.
self.style = DenseLayer(in_channels=w_dim,
out_channels=in_channels,
add_bias=True,
init_bias=1.0,
use_wscale=use_wscale,
wscale_gain=wscale_gain,
lr_mul=lr_mul,
activation_type='linear')
# Set up noise.
if self.noise_type != 'none':
self.noise_strength = nn.Parameter(torch.zeros(()))
if self.noise_type == 'spatial':
self.register_buffer(
'noise', torch.randn(1, 1, resolution, resolution))
elif self.noise_type == 'channel':
self.register_buffer(
'noise', torch.randn(1, out_channels, 1, 1))
else:
raise NotImplementedError(f'Not implemented noise type: '
f'`{self.noise_type}`!')
if scale_factor > 1:
assert filter_kernel is not None
self.register_buffer(
'filter', upfirdn2d.setup_filter(filter_kernel))
fh, fw = self.filter.shape
self.filter_padding = (
kernel_size // 2 + (fw + scale_factor - 1) // 2,
kernel_size // 2 + (fw - scale_factor) // 2,
kernel_size // 2 + (fh + scale_factor - 1) // 2,
kernel_size // 2 + (fh - scale_factor) // 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'upsample_filter={self.filter_kernel}, '
f'demodulate={self.demodulate}, '
f'noise_type={self.noise_type}, '
f'act={self.activation_type}, '
f'clamp={self.conv_clamp}')
def forward_style(self, w, impl='cuda'):
"""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, impl=impl)
elif space_of_latent == 'Y':
if w.ndim != 2 or w.shape[1] < self.in_channels:
raise ValueError(f'The input tensor should be with shape '
f'[batch_size, y_dim], where '
f'`y_dim` equals to {self.in_channels}!\n'
f'But `{w.shape}` is received!')
style = w[:, :self.in_channels]
else:
raise NotImplementedError(f'Not implemented `space_of_latent`: '
f'`{space_of_latent}`!')
return style
def forward(self,
x,
w,
runtime_gain=1.0,
noise_mode='const',
fused_modulate=False,
impl='cuda'):
dtype = x.dtype
N, C, H, W = x.shape
fused_modulate = (fused_modulate and
not self.training and
(dtype == torch.float32 or N == 1))
weight = self.weight
out_ch, in_ch, kh, kw = weight.shape
assert in_ch == C
# Affine on `w`.
style = self.forward_style(w, impl=impl)
if not self.demodulate:
_style = style * self.wscale # Equivalent to scaling weight.
else:
_style = style
# Prepare noise.
noise = None
noise_mode = noise_mode.lower()
if self.noise_type != 'none' and noise_mode != 'none':
if noise_mode == 'random':
noise = torch.randn((N, *self.noise.shape[1:]), device=x.device)
elif noise_mode == 'const':
noise = self.noise
else:
raise ValueError(f'Unknown noise mode `{noise_mode}`!')
noise = (noise * self.noise_strength).to(dtype)
# Pre-normalize inputs to avoid FP16 overflow.
if dtype == torch.float16 and self.demodulate:
weight_max = weight.norm(float('inf'), dim=(1, 2, 3), keepdim=True)
weight = weight * (self.wscale / weight_max)
style_max = _style.norm(float('inf'), dim=1, keepdim=True)
_style = _style / style_max
if self.demodulate or fused_modulate:
_weight = weight.unsqueeze(0)
_weight = _weight * _style.reshape(N, 1, in_ch, 1, 1)
if self.demodulate:
decoef = (_weight.square().sum(dim=(2, 3, 4)) + self.eps).rsqrt()
if self.demodulate and fused_modulate:
_weight = _weight * decoef.reshape(N, out_ch, 1, 1, 1)
if not fused_modulate:
x = x * _style.to(dtype).reshape(N, in_ch, 1, 1)
w = weight.to(dtype)
groups = 1
else: # Use group convolution to fuse style modulation and convolution.
x = x.reshape(1, N * in_ch, H, W)
w = _weight.reshape(N * out_ch, in_ch, kh, kw).to(dtype)
groups = N
if self.scale_factor == 1: # Native convolution without upsampling.
up = 1
padding = self.kernel_size // 2
x = conv2d_gradfix.conv2d(
x, w, stride=1, padding=padding, groups=groups, impl=impl)
else: # Convolution with upsampling.
up = self.scale_factor
f = self.filter
# When kernel size = 1, use filtering function for upsampling.
if self.kernel_size == 1:
padding = self.filter_padding
x = conv2d_gradfix.conv2d(
x, w, stride=1, padding=0, groups=groups, impl=impl)
x = upfirdn2d.upfirdn2d(
x, f, up=up, padding=padding, gain=up ** 2, impl=impl)
# When kernel size != 1, use stride convolution for upsampling.
else:
# Following codes are borrowed from
# https://github.com/NVlabs/stylegan2-ada-pytorch
px0, px1, py0, py1 = self.filter_padding
px0 = px0 - (kw - 1)
px1 = px1 - (kw - up)
py0 = py0 - (kh - 1)
py1 = py1 - (kh - up)
pxt = max(min(-px0, -px1), 0)
pyt = max(min(-py0, -py1), 0)
if groups == 1:
w = w.transpose(0, 1)
else:
w = w.reshape(N, out_ch, in_ch, kh, kw)
w = w.transpose(1, 2)
w = w.reshape(N * in_ch, out_ch, kh, kw)
padding = (pyt, pxt)
x = conv2d_gradfix.conv_transpose2d(
x, w, stride=up, padding=padding, groups=groups, impl=impl)
padding = (px0 + pxt, px1 + pxt, py0 + pyt, py1 + pyt)
x = upfirdn2d.upfirdn2d(
x, f, up=1, padding=padding, gain=up ** 2, impl=impl)
if not fused_modulate:
if self.demodulate:
decoef = decoef.to(dtype).reshape(N, out_ch, 1, 1)
if self.demodulate and noise is not None:
x = fma.fma(x, decoef, noise, impl=impl)
else:
if self.demodulate:
x = x * decoef
if noise is not None:
x = x + noise
else:
x = x.reshape(N, out_ch, H * up, W * up)
if noise is not None:
x = x + noise
bias = None
if self.bias is not None:
bias = self.bias.to(dtype)
if self.bscale != 1.0:
bias = bias * self.bscale
if self.activation_type == 'linear': # Shortcut for output layer.
x = bias_act.bias_act(
x, bias, act='linear', clamp=self.conv_clamp, impl=impl)
else:
act_gain = self.act_gain * runtime_gain
act_clamp = None
if self.conv_clamp is not None:
act_clamp = self.conv_clamp * runtime_gain
x = bias_act.bias_act(x, bias,
act=self.activation_type,
gain=act_gain,
clamp=act_clamp,
impl=impl)
assert x.dtype == dtype
assert style.dtype == torch.float32
return x, style
class DenseLayer(nn.Module):
"""Implements the dense layer."""
def __init__(self,
in_channels,
out_channels,
add_bias,
init_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.
init_bias: The initial bias value before training.
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.init_bias = init_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:
init_bias = np.float32(init_bias) / lr_mul
self.bias = nn.Parameter(torch.full([out_channels], init_bias))
self.bscale = lr_mul
else:
self.bias = None
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'init_bias={self.init_bias}, '
f'lr_mul={self.lr_mul:.3f}, '
f'act={self.activation_type}')
def forward(self, x, impl='cuda'):
dtype = x.dtype
if x.ndim != 2:
x = x.flatten(start_dim=1)
weight = self.weight.to(dtype) * self.wscale
bias = None
if self.bias is not None:
bias = self.bias.to(dtype)
if self.bscale != 1.0:
bias = bias * self.bscale
# Fast pass for linear activation.
if self.activation_type == 'linear' and bias is not None:
x = torch.addmm(bias.unsqueeze(0), x, weight.t())
else:
x = x.matmul(weight.t())
x = bias_act.bias_act(x, bias, act=self.activation_type, impl=impl)
assert x.dtype == dtype
return x
# pylint: enable=missing-function-docstring