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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 os
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from .sync_op import all_gather
from huggingface_hub import PyTorchModelHubMixin, PYTORCH_WEIGHTS_NAME, hf_hub_download
__all__ = ['StyleGANGenerator']
# Resolutions allowed.
_RESOLUTIONS_ALLOWED = [8, 16, 32, 64, 128, 256, 512, 1024]
# Initial resolution.
_INIT_RES = 4
# Fused-scale options allowed.
_FUSED_SCALE_ALLOWED = [True, False, 'auto']
# Minimal resolution for `auto` fused-scale strategy.
_AUTO_FUSED_SCALE_MIN_RES = 128
# Default gain factor for weight scaling.
_WSCALE_GAIN = np.sqrt(2.0)
_STYLEMOD_WSCALE_GAIN = 1.0
class StyleGANGenerator(nn.Module, PyTorchModelHubMixin):
"""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_space_dim: Dimension of the input latent space, Z. (default: 512)
(2) w_space_dim: Dimension of the outout latent space, W. (default: 512)
(3) label_size: Size of the additional label for conditional generation.
(default: 0)
(4）mapping_layers: Number of layers of the mapping network. (default: 8)
(5) mapping_fmaps: Number of hidden channels of the mapping network.
(default: 512)
(6) mapping_lr_mul: Learning rate multiplier for the mapping network.
(default: 0.01)
(7) repeat_w: Repeat w-code for different layers.
Settings for the synthesis network:
(1) resolution: The resolution of the output image.
(2) image_channels: Number of channels of the output image. (default: 3)
(3) final_tanh: Whether to use `tanh` to control the final pixel range.
(default: False)
(4) const_input: Whether to use a constant in the first convolutional layer.
(default: True)
(5) fused_scale: Whether to fused `upsample` and `conv2d` together,
resulting in `conv2d_transpose`. (default: `auto`)
(6) use_wscale: Whether to use weight scaling. (default: True)
(7) fmaps_base: Factor to control number of feature maps for each layer.
(default: 16 << 10)
(8) fmaps_max: Maximum number of feature maps in each layer. (default: 512)
"""
def __init__(self,
resolution,
z_space_dim=512,
w_space_dim=512,
label_size=0,
mapping_layers=8,
mapping_fmaps=512,
mapping_lr_mul=0.01,
repeat_w=True,
image_channels=3,
final_tanh=False,
const_input=True,
fused_scale='auto',
use_wscale=True,
fmaps_base=16 << 10,
fmaps_max=512,
**kwargs):
"""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.init_res = _INIT_RES
self.resolution = resolution
self.z_space_dim = z_space_dim
self.w_space_dim = w_space_dim
self.label_size = label_size
self.mapping_layers = mapping_layers
self.mapping_fmaps = mapping_fmaps
self.mapping_lr_mul = mapping_lr_mul
self.repeat_w = repeat_w
self.image_channels = image_channels
self.final_tanh = final_tanh
self.const_input = const_input
self.fused_scale = fused_scale
self.use_wscale = use_wscale
self.fmaps_base = fmaps_base
self.fmaps_max = fmaps_max
self.config = kwargs.pop("config", None)
self.num_layers = int(np.log2(self.resolution // self.init_res * 2)) * 2
if self.repeat_w:
self.mapping_space_dim = self.w_space_dim
else:
self.mapping_space_dim = self.w_space_dim * self.num_layers
self.mapping = MappingModule(input_space_dim=self.z_space_dim,
hidden_space_dim=self.mapping_fmaps,
final_space_dim=self.mapping_space_dim,
label_size=self.label_size,
num_layers=self.mapping_layers,
use_wscale=self.use_wscale,
lr_mul=self.mapping_lr_mul)
self.truncation = TruncationModule(w_space_dim=self.w_space_dim,
num_layers=self.num_layers,
repeat_w=self.repeat_w)
self.synthesis = SynthesisModule(resolution=self.resolution,
init_resolution=self.init_res,
w_space_dim=self.w_space_dim,
image_channels=self.image_channels,
final_tanh=self.final_tanh,
const_input=self.const_input,
fused_scale=self.fused_scale,
use_wscale=self.use_wscale,
fmaps_base=self.fmaps_base,
fmaps_max=self.fmaps_max)
self.pth_to_tf_var_mapping = {}
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.truncation.pth_to_tf_var_mapping.items():
self.pth_to_tf_var_mapping[f'truncation.{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 forward(self,
z,
label=None,
lod=None,
w_moving_decay=0.995,
style_mixing_prob=0.9,
trunc_psi=None,
trunc_layers=None,
randomize_noise=False,
**_unused_kwargs):
mapping_results = self.mapping(z, label)
w = mapping_results['w']
if self.training and w_moving_decay < 1:
batch_w_avg = all_gather(w).mean(dim=0)
self.truncation.w_avg.copy_(
self.truncation.w_avg * w_moving_decay +
batch_w_avg * (1 - w_moving_decay))
if self.training and style_mixing_prob > 0:
new_z = torch.randn_like(z)
new_w = self.mapping(new_z, label)['w']
lod = self.synthesis.lod.cpu().tolist() if lod is None else lod
current_layers = self.num_layers - int(lod) * 2
if np.random.uniform() < style_mixing_prob:
mixing_cutoff = np.random.randint(1, current_layers)
w = self.truncation(w)
new_w = self.truncation(new_w)
w[:, mixing_cutoff:] = new_w[:, mixing_cutoff:]
wp = self.truncation(w, trunc_psi, trunc_layers)
synthesis_results = self.synthesis(wp, lod, randomize_noise)
return {**mapping_results, **synthesis_results}
@classmethod
def _from_pretrained(
cls,
model_id,
revision,
cache_dir,
force_download,
proxies,
resume_download,
local_files_only,
use_auth_token,
map_location="cpu",
strict=False,
**model_kwargs,
):
"""
Overwrite this method in case you wish to initialize your model in a
different way.
"""
map_location = torch.device(map_location)
if os.path.isdir(model_id):
print("Loading weights from local directory")
model_file = os.path.join(model_id, PYTORCH_WEIGHTS_NAME)
else:
model_file = hf_hub_download(
repo_id=model_id,
filename=PYTORCH_WEIGHTS_NAME,
revision=revision,
cache_dir=cache_dir,
force_download=force_download,
proxies=proxies,
resume_download=resume_download,
use_auth_token=use_auth_token,
local_files_only=local_files_only,
)
pretrained = torch.load(model_file, map_location=map_location)
return pretrained
class MappingModule(nn.Module):
"""Implements the latent space mapping module.
Basically, this module executes several dense layers in sequence.
"""
def __init__(self,
input_space_dim=512,
hidden_space_dim=512,
final_space_dim=512,
label_size=0,
num_layers=8,
normalize_input=True,
use_wscale=True,
lr_mul=0.01):
super().__init__()
self.input_space_dim = input_space_dim
self.hidden_space_dim = hidden_space_dim
self.final_space_dim = final_space_dim
self.label_size = label_size
self.num_layers = num_layers
self.normalize_input = normalize_input
self.use_wscale = use_wscale
self.lr_mul = lr_mul
self.norm = PixelNormLayer() if self.normalize_input else nn.Identity()
self.pth_to_tf_var_mapping = {}
for i in range(num_layers):
dim_mul = 2 if label_size else 1
in_channels = (input_space_dim * dim_mul if i == 0 else
hidden_space_dim)
out_channels = (final_space_dim if i == (num_layers - 1) else
hidden_space_dim)
self.add_module(f'dense{i}',
DenseBlock(in_channels=in_channels,
out_channels=out_channels,
use_wscale=self.use_wscale,
lr_mul=self.lr_mul))
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'
if label_size:
self.label_weight = nn.Parameter(
torch.randn(label_size, input_space_dim))
self.pth_to_tf_var_mapping[f'label_weight'] = f'LabelConcat/weight'
def forward(self, z, label=None):
if z.ndim != 2 or z.shape[1] != self.input_space_dim:
raise ValueError(f'Input latent code should be with shape '
f'[batch_size, input_dim], where '
f'`input_dim` equals to {self.input_space_dim}!\n'
f'But `{z.shape}` is received!')
if self.label_size:
if label is None:
raise ValueError(f'Model requires an additional label '
f'(with size {self.label_size}) as input, '
f'but no label is received!')
if label.ndim != 2 or label.shape != (z.shape[0], self.label_size):
raise ValueError(f'Input label should be with shape '
f'[batch_size, label_size], where '
f'`batch_size` equals to that of '
f'latent codes ({z.shape[0]}) and '
f'`label_size` equals to {self.label_size}!\n'
f'But `{label.shape}` is received!')
embedding = torch.matmul(label, self.label_weight)
z = torch.cat((z, embedding), dim=1)
z = self.norm(z)
w = z
for i in range(self.num_layers):
w = self.__getattr__(f'dense{i}')(w)
results = {
'z': z,
'label': label,
'w': w,
}
if self.label_size:
results['embedding'] = embedding
return results
class TruncationModule(nn.Module):
"""Implements the truncation module.
Truncation is executed 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)
NOTE: The returned tensor is layer-wise style codes.
"""
def __init__(self, w_space_dim, num_layers, repeat_w=True):
super().__init__()
self.num_layers = num_layers
self.w_space_dim = w_space_dim
self.repeat_w = repeat_w
if self.repeat_w:
self.register_buffer('w_avg', torch.zeros(w_space_dim))
else:
self.register_buffer('w_avg', torch.zeros(num_layers * w_space_dim))
self.pth_to_tf_var_mapping = {'w_avg': 'dlatent_avg'}
def forward(self, w, trunc_psi=None, trunc_layers=None):
if w.ndim == 2:
if self.repeat_w and w.shape[1] == self.w_space_dim:
w = w.view(-1, 1, self.w_space_dim)
wp = w.repeat(1, self.num_layers, 1)
else:
assert w.shape[1] == self.w_space_dim * self.num_layers
wp = w.view(-1, self.num_layers, self.w_space_dim)
else:
wp = w
assert wp.ndim == 3
assert wp.shape[1:] == (self.num_layers, self.w_space_dim)
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:
layer_idx = np.arange(self.num_layers).reshape(1, -1, 1)
coefs = np.ones_like(layer_idx, dtype=np.float32)
coefs[layer_idx < trunc_layers] *= trunc_psi
coefs = torch.from_numpy(coefs).to(wp)
w_avg = self.w_avg.view(1, -1, self.w_space_dim)
wp = w_avg + (wp - w_avg) * coefs
return wp
class SynthesisModule(nn.Module):
"""Implements the image synthesis module.
Basically, this module executes several convolutional layers in sequence.
"""
def __init__(self,
resolution=1024,
init_resolution=4,
w_space_dim=512,
image_channels=3,
final_tanh=False,
const_input=True,
fused_scale='auto',
use_wscale=True,
fmaps_base=16 << 10,
fmaps_max=512):
super().__init__()
self.init_res = init_resolution
self.init_res_log2 = int(np.log2(self.init_res))
self.resolution = resolution
self.final_res_log2 = int(np.log2(self.resolution))
self.w_space_dim = w_space_dim
self.image_channels = image_channels
self.final_tanh = final_tanh
self.const_input = const_input
self.fused_scale = fused_scale
self.use_wscale = use_wscale
self.fmaps_base = fmaps_base
self.fmaps_max = fmaps_max
self.num_layers = (self.final_res_log2 - self.init_res_log2 + 1) * 2
# Level of detail (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
block_idx = res_log2 - self.init_res_log2
# First convolution layer for each resolution.
layer_name = f'layer{2 * block_idx}'
if res == self.init_res:
if self.const_input:
self.add_module(layer_name,
ConvBlock(in_channels=self.get_nf(res),
out_channels=self.get_nf(res),
resolution=self.init_res,
w_space_dim=self.w_space_dim,
position='const_init',
use_wscale=self.use_wscale))
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,
ConvBlock(in_channels=self.w_space_dim,
out_channels=self.get_nf(res),
resolution=self.init_res,
w_space_dim=self.w_space_dim,
kernel_size=self.init_res,
padding=self.init_res - 1,
use_wscale=self.use_wscale))
tf_layer_name = 'Dense'
self.pth_to_tf_var_mapping[f'{layer_name}.weight'] = (
f'{res}x{res}/{tf_layer_name}/weight')
else:
if self.fused_scale == 'auto':
fused_scale = (res >= _AUTO_FUSED_SCALE_MIN_RES)
else:
fused_scale = self.fused_scale
self.add_module(layer_name,
ConvBlock(in_channels=self.get_nf(res // 2),
out_channels=self.get_nf(res),
resolution=res,
w_space_dim=self.w_space_dim,
upsample=True,
fused_scale=fused_scale,
use_wscale=self.use_wscale))
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}.apply_noise.weight'] = (
f'{res}x{res}/{tf_layer_name}/Noise/weight')
self.pth_to_tf_var_mapping[f'{layer_name}.apply_noise.noise'] = (
f'noise{2 * block_idx}')
# Second convolution layer for each resolution.
layer_name = f'layer{2 * block_idx + 1}'
self.add_module(layer_name,
ConvBlock(in_channels=self.get_nf(res),
out_channels=self.get_nf(res),
resolution=res,
w_space_dim=self.w_space_dim,
use_wscale=self.use_wscale))
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}.apply_noise.weight'] = (
f'{res}x{res}/{tf_layer_name}/Noise/weight')
self.pth_to_tf_var_mapping[f'{layer_name}.apply_noise.noise'] = (
f'noise{2 * block_idx + 1}')
# Output convolution layer for each resolution.
self.add_module(f'output{block_idx}',
ConvBlock(in_channels=self.get_nf(res),
out_channels=self.image_channels,
resolution=res,
w_space_dim=self.w_space_dim,
position='last',
kernel_size=1,
padding=0,
use_wscale=self.use_wscale,
wscale_gain=1.0,
activation_type='linear'))
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')
self.upsample = UpsamplingLayer()
self.final_activate = nn.Tanh() if final_tanh else nn.Identity()
def get_nf(self, res):
"""Gets number of feature maps according to current resolution."""
return min(self.fmaps_base // res, self.fmaps_max)
def forward(self, wp, lod=None, randomize_noise=False):
if wp.ndim != 3 or wp.shape[1:] != (self.num_layers, self.w_space_dim):
raise ValueError(f'Input tensor should be with shape '
f'[batch_size, num_layers, w_space_dim], where '
f'`num_layers` equals to {self.num_layers}, and '
f'`w_space_dim` equals to {self.w_space_dim}!\n'
f'But `{wp.shape}` is received!')
lod = self.lod.cpu().tolist() if lod is None else lod
if lod + self.init_res_log2 > self.final_res_log2:
raise ValueError(f'Maximum level-of-detail (lod) is '
f'{self.final_res_log2 - self.init_res_log2}, '
f'but `{lod}` is received!')
results = {'wp': wp}
for res_log2 in range(self.init_res_log2, self.final_res_log2 + 1):
current_lod = self.final_res_log2 - res_log2
if lod < current_lod + 1:
block_idx = res_log2 - self.init_res_log2
if block_idx == 0:
if self.const_input:
x, style = self.layer0(None, wp[:, 0], randomize_noise)
else:
x = wp[:, 0].view(-1, self.w_space_dim, 1, 1)
x, style = self.layer0(x, wp[:, 0], randomize_noise)
else:
x, style = self.__getattr__(f'layer{2 * block_idx}')(
x, wp[:, 2 * block_idx])
results[f'style{2 * block_idx:02d}'] = style
x, style = self.__getattr__(f'layer{2 * block_idx + 1}')(
x, wp[:, 2 * block_idx + 1])
results[f'style{2 * block_idx + 1:02d}'] = style
if current_lod - 1 < lod <= current_lod:
image = self.__getattr__(f'output{block_idx}')(x, None)
elif current_lod < lod < current_lod + 1:
alpha = np.ceil(lod) - lod
image = (self.__getattr__(f'output{block_idx}')(x, None) * alpha
+ self.upsample(image) * (1 - alpha))
elif lod >= current_lod + 1:
image = self.upsample(image)
results['image'] = self.final_activate(image)
return results
class PixelNormLayer(nn.Module):
"""Implements pixel-wise feature vector normalization layer."""
def __init__(self, epsilon=1e-8):
super().__init__()
self.eps = epsilon
def forward(self, x):
norm = torch.sqrt(torch.mean(x ** 2, dim=1, keepdim=True) + self.eps)
return x / norm
class InstanceNormLayer(nn.Module):
"""Implements instance normalization layer."""
def __init__(self, epsilon=1e-8):
super().__init__()
self.eps = epsilon
def forward(self, x):
if x.ndim != 4:
raise ValueError(f'The input tensor should be with shape '
f'[batch_size, channel, height, width], '
f'but `{x.shape}` is received!')
x = x - torch.mean(x, dim=[2, 3], keepdim=True)
norm = torch.sqrt(
torch.mean(x ** 2, dim=[2, 3], keepdim=True) + self.eps)
return x / norm
class UpsamplingLayer(nn.Module):
"""Implements the upsampling layer.
Basically, this layer can be used to upsample feature maps with nearest
neighbor interpolation.
"""
def __init__(self, scale_factor=2):
super().__init__()
self.scale_factor = scale_factor
def forward(self, x):
if self.scale_factor <= 1:
return x
return F.interpolate(x, scale_factor=self.scale_factor, mode='nearest')
class Blur(torch.autograd.Function):
"""Defines blur operation with customized gradient computation."""
@staticmethod
def forward(ctx, x, kernel):
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 BlurLayer(nn.Module):
"""Implements the blur layer."""
def __init__(self,
channels,
kernel=(1, 2, 1),
normalize=True):
super().__init__()
kernel = np.array(kernel, dtype=np.float32).reshape(1, -1)
kernel = kernel.T.dot(kernel)
if normalize:
kernel /= np.sum(kernel)
kernel = kernel[np.newaxis, np.newaxis]
kernel = np.tile(kernel, [channels, 1, 1, 1])
self.register_buffer('kernel', torch.from_numpy(kernel))
def forward(self, x):
return Blur.apply(x, self.kernel)
class NoiseApplyingLayer(nn.Module):
"""Implements the noise applying layer."""
def __init__(self, resolution, channels):
super().__init__()
self.res = resolution
self.register_buffer('noise', torch.randn(1, 1, self.res, self.res))
self.weight = nn.Parameter(torch.zeros(channels))
def forward(self, x, randomize_noise=False):
if x.ndim != 4:
raise ValueError(f'The input tensor should be with shape '
f'[batch_size, channel, height, width], '
f'but `{x.shape}` is received!')
if randomize_noise:
noise = torch.randn(x.shape[0], 1, self.res, self.res).to(x)
else:
noise = self.noise
return x + noise * self.weight.view(1, -1, 1, 1)
class StyleModLayer(nn.Module):
"""Implements the style modulation layer."""
def __init__(self,
w_space_dim,
out_channels,
use_wscale=True):
super().__init__()
self.w_space_dim = w_space_dim
self.out_channels = out_channels
weight_shape = (self.out_channels * 2, self.w_space_dim)
wscale = _STYLEMOD_WSCALE_GAIN / np.sqrt(self.w_space_dim)
if use_wscale:
self.weight = nn.Parameter(torch.randn(*weight_shape))
self.wscale = wscale
else:
self.weight = nn.Parameter(torch.randn(*weight_shape) * wscale)
self.wscale = 1.0
self.bias = nn.Parameter(torch.zeros(self.out_channels * 2))
def forward(self, x, w):
if w.ndim != 2 or w.shape[1] != self.w_space_dim:
raise ValueError(f'The input tensor should be with shape '
f'[batch_size, w_space_dim], where '
f'`w_space_dim` equals to {self.w_space_dim}!\n'
f'But `{w.shape}` is received!')
style = F.linear(w, weight=self.weight * self.wscale, bias=self.bias)
style_split = style.view(-1, 2, self.out_channels, 1, 1)
x = x * (style_split[:, 0] + 1) + style_split[:, 1]
return x, style
class ConvBlock(nn.Module):
"""Implements the normal convolutional block.
Basically, this block executes upsampling layer (if needed), convolutional
layer, blurring layer, noise applying layer, activation layer, instance
normalization layer, and style modulation layer in sequence.
"""
def __init__(self,
in_channels,
out_channels,
resolution,
w_space_dim,
position=None,
kernel_size=3,
stride=1,
padding=1,
add_bias=True,
upsample=False,
fused_scale=False,
use_wscale=True,
wscale_gain=_WSCALE_GAIN,
lr_mul=1.0,
activation_type='lrelu'):
"""Initializes with block 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_space_dim: Dimension of W space for style modulation.
position: Position of the layer. `const_init`, `last` would lead to
different behavior. (default: None)
kernel_size: Size of the convolutional kernels. (default: 3)
stride: Stride parameter for convolution operation. (default: 1)
padding: Padding parameter for convolution operation. (default: 1)
add_bias: Whether to add bias onto the convolutional result.
(default: True)
upsample: Whether to upsample the input tensor before convolution.
(default: False)
fused_scale: Whether to fused `upsample` and `conv2d` together,
resulting in `conv2d_transpose`. (default: False)
use_wscale: Whether to use weight scaling. (default: True)
wscale_gain: Gain factor for weight scaling. (default: _WSCALE_GAIN)
lr_mul: Learning multiplier for both weight and bias. (default: 1.0)
activation_type: Type of activation. Support `linear` and `lrelu`.
(default: `lrelu`)
Raises:
NotImplementedError: If the `activation_type` is not supported.
"""
super().__init__()
self.position = position
if add_bias:
self.bias = nn.Parameter(torch.zeros(out_channels))
self.bscale = lr_mul
else:
self.bias = None
if activation_type == 'linear':
self.activate = nn.Identity()
elif activation_type == 'lrelu':
self.activate = nn.LeakyReLU(negative_slope=0.2, inplace=True)
else:
raise NotImplementedError(f'Not implemented activation function: '
f'`{activation_type}`!')
if self.position != 'last':
self.apply_noise = NoiseApplyingLayer(resolution, out_channels)
self.normalize = InstanceNormLayer()
self.style = StyleModLayer(w_space_dim, out_channels, use_wscale)
if self.position == 'const_init':
self.const = nn.Parameter(
torch.ones(1, in_channels, resolution, resolution))
return
self.blur = BlurLayer(out_channels) if upsample else nn.Identity()
if upsample and not fused_scale:
self.upsample = UpsamplingLayer()
else:
self.upsample = nn.Identity()
if upsample and fused_scale:
self.use_conv2d_transpose = True
self.stride = 2
self.padding = 1
else:
self.use_conv2d_transpose = False
self.stride = stride
self.padding = padding
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
def forward(self, x, w, randomize_noise=False):
if self.position != 'const_init':
x = self.upsample(x)
weight = self.weight * self.wscale
if self.use_conv2d_transpose:
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])
weight = weight.permute(1, 0, 2, 3)
x = F.conv_transpose2d(x,
weight=weight,
bias=None,
stride=self.stride,
padding=self.padding)
else:
x = F.conv2d(x,
weight=weight,
bias=None,
stride=self.stride,
padding=self.padding)
x = self.blur(x)
else:
x = self.const.repeat(w.shape[0], 1, 1, 1)
bias = self.bias * self.bscale if self.bias is not None else None
if self.position == 'last':
if bias is not None:
x = x + bias.view(1, -1, 1, 1)
return x
x = self.apply_noise(x, randomize_noise)
if bias is not None:
x = x + bias.view(1, -1, 1, 1)
x = self.activate(x)
x = self.normalize(x)
x, style = self.style(x, w)
return x, style
class DenseBlock(nn.Module):
"""Implements the dense block.
Basically, this block executes fully-connected layer and activation layer.
"""
def __init__(self,
in_channels,
out_channels,
add_bias=True,
use_wscale=True,
wscale_gain=_WSCALE_GAIN,
lr_mul=1.0,
activation_type='lrelu'):
"""Initializes with block 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.
(default: True)
use_wscale: Whether to use weight scaling. (default: True)
wscale_gain: Gain factor for weight scaling. (default: _WSCALE_GAIN)
lr_mul: Learning multiplier for both weight and bias. (default: 1.0)
activation_type: Type of activation. Support `linear` and `lrelu`.
(default: `lrelu`)
Raises:
NotImplementedError: If the `activation_type` is not supported.
"""
super().__init__()
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
if activation_type == 'linear':
self.activate = nn.Identity()
elif activation_type == 'lrelu':
self.activate = nn.LeakyReLU(negative_slope=0.2, inplace=True)
else:
raise NotImplementedError(f'Not implemented activation function: '
f'`{activation_type}`!')
def forward(self, x):
if x.ndim != 2:
x = x.view(x.shape[0], -1)
bias = self.bias * self.bscale if self.bias is not None else None
x = F.linear(x, weight=self.weight * self.wscale, bias=bias)
x = self.activate(x)
return x
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