Spaces:
Build error
Build error
File size: 46,043 Bytes
2a76164 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800 801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816 817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832 833 834 835 836 837 838 839 840 841 842 843 844 845 846 847 848 849 850 851 852 853 854 855 856 857 858 859 860 861 862 863 864 865 866 867 868 869 870 871 872 873 874 875 876 877 878 879 880 881 882 883 884 885 886 887 888 889 890 891 892 893 894 895 896 897 898 899 900 901 902 903 904 905 906 907 908 909 910 911 912 913 914 915 916 917 918 919 920 921 922 923 924 925 926 927 928 929 930 931 932 933 934 935 936 937 938 939 940 941 942 943 944 945 946 947 948 949 950 951 952 953 954 955 956 957 958 959 960 961 962 963 964 965 966 967 968 969 970 971 972 973 974 975 976 977 978 979 980 981 982 983 984 985 986 987 988 989 990 991 992 993 994 995 996 997 998 999 1000 1001 1002 1003 1004 1005 1006 1007 1008 1009 1010 1011 1012 1013 1014 1015 1016 1017 1018 1019 1020 1021 1022 1023 1024 1025 1026 1027 1028 1029 1030 1031 1032 1033 1034 1035 1036 1037 1038 1039 1040 1041 1042 1043 1044 1045 1046 1047 1048 1049 1050 1051 1052 1053 1054 1055 1056 1057 1058 1059 1060 1061 1062 1063 1064 1065 1066 1067 1068 1069 1070 1071 1072 1073 1074 1075 |
# 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
import torch.nn.functional as F
from .sync_op import all_gather
__all__ = ['StyleGAN2Generator']
# Resolutions allowed.
_RESOLUTIONS_ALLOWED = [8, 16, 32, 64, 128, 256, 512, 1024]
# Initial resolution.
_INIT_RES = 4
# Architectures allowed.
_ARCHITECTURES_ALLOWED = ['resnet', 'skip', 'origin']
# Default gain factor for weight scaling.
_WSCALE_GAIN = 1.0
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_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) architecture: Type of architecture. Support `origin`, `skip`, and
`resnet`. (default: `resnet`)
(6) fused_modulate: Whether to fuse `style_modulate` and `conv2d` together.
(default: True)
(7) demodulate: Whether to perform style demodulation. (default: True)
(8) use_wscale: Whether to use weight scaling. (default: True)
(9) noise_type: Type of noise added to the convolutional results at each
layer. (default: `spatial`)
(10) fmaps_base: Factor to control number of feature maps for each layer.
(default: 32 << 10)
(11) 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,
architecture='skip',
fused_modulate=True,
demodulate=True,
use_wscale=True,
noise_type='spatial',
fmaps_base=32 << 10,
fmaps_max=512):
"""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}.')
if architecture not in _ARCHITECTURES_ALLOWED:
raise ValueError(f'Invalid architecture: `{architecture}`!\n'
f'Architectures allowed: '
f'{_ARCHITECTURES_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.architecture = architecture
self.fused_modulate = fused_modulate
self.demodulate = demodulate
self.use_wscale = use_wscale
self.noise_type = noise_type
self.fmaps_base = fmaps_base
self.fmaps_max = fmaps_max
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,
architecture=self.architecture,
fused_modulate=self.fused_modulate,
demodulate=self.demodulate,
use_wscale=self.use_wscale,
noise_type=self.noise_type,
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 set_space_of_latent(self, space_of_latent='w'):
"""Sets the space to which the latent code belong.
This function is particually 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. (default: 'w')
"""
for module in self.modules():
if isinstance(module, ModulateConvBlock):
setattr(module, 'space_of_latent', space_of_latent)
def forward(self,
z,
label=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']
if np.random.uniform() < style_mixing_prob:
mixing_cutoff = np.random.randint(1, self.num_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, randomize_noise)
return {**mapping_results, **synthesis_results}
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,
architecture='skip',
fused_modulate=True,
demodulate=True,
use_wscale=True,
noise_type='spatial',
fmaps_base=32 << 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.architecture = architecture
self.fused_modulate = fused_modulate
self.demodulate = demodulate
self.use_wscale = use_wscale
self.noise_type = noise_type
self.fmaps_base = fmaps_base
self.fmaps_max = fmaps_max
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
block_idx = res_log2 - self.init_res_log2
# First convolution layer for each resolution.
if res == self.init_res:
if self.const_input:
self.add_module(f'early_layer',
InputBlock(init_resolution=self.init_res,
channels=self.get_nf(res)))
self.pth_to_tf_var_mapping[f'early_layer.const'] = (
f'{res}x{res}/Const/const')
else:
self.add_module(f'early_layer',
DenseBlock(in_channels=self.w_space_dim,
out_channels=self.get_nf(res),
use_wscale=self.use_wscale))
self.pth_to_tf_var_mapping[f'early_layer.weight'] = (
f'{res}x{res}/Dense/weight')
self.pth_to_tf_var_mapping[f'early_layer.bias'] = (
f'{res}x{res}/Dense/bias')
else:
layer_name = f'layer{2 * block_idx - 1}'
self.add_module(
layer_name,
ModulateConvBlock(in_channels=self.get_nf(res // 2),
out_channels=self.get_nf(res),
resolution=res,
w_space_dim=self.w_space_dim,
scale_factor=2,
fused_modulate=self.fused_modulate,
demodulate=self.demodulate,
use_wscale=self.use_wscale,
noise_type=self.noise_type))
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}')
if self.architecture == 'resnet':
layer_name = f'layer{2 * block_idx - 1}'
self.add_module(
layer_name,
ConvBlock(in_channels=self.get_nf(res // 2),
out_channels=self.get_nf(res),
kernel_size=1,
add_bias=False,
scale_factor=2,
use_wscale=self.use_wscale,
activation_type='linear'))
self.pth_to_tf_var_mapping[f'{layer_name}.weight'] = (
f'{res}x{res}/Skip/weight')
# Second convolution layer for each resolution.
layer_name = f'layer{2 * block_idx}'
self.add_module(
layer_name,
ModulateConvBlock(in_channels=self.get_nf(res),
out_channels=self.get_nf(res),
resolution=res,
w_space_dim=self.w_space_dim,
fused_modulate=self.fused_modulate,
demodulate=self.demodulate,
use_wscale=self.use_wscale,
noise_type=self.noise_type))
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,
ModulateConvBlock(in_channels=self.get_nf(res),
out_channels=image_channels,
resolution=res,
w_space_dim=self.w_space_dim,
kernel_size=1,
fused_modulate=self.fused_modulate,
demodulate=False,
use_wscale=self.use_wscale,
add_noise=False,
activation_type='linear'))
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')
if self.architecture == 'skip':
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, randomize_noise=False, x=None, image=None, start=2, stop=None):
stop = self.num_layers - 1 if stop is None else stop
# 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!')
results = {'wp': wp}
x = self.early_layer(wp[:, 0]) if x is None else x
if self.architecture == 'origin':
for layer_idx in range(self.num_layers - 1):
x, style = self.__getattr__(f'layer{layer_idx}')(
x, wp[:, layer_idx], randomize_noise)
results[f'style{layer_idx:02d}'] = style
image, style = self.__getattr__(f'output{layer_idx // 2}')(
x, wp[:, layer_idx + 1])
results[f'output_style{layer_idx // 2}'] = style
elif self.architecture == 'skip':
# for layer_idx in range(self.num_layers - 1):
for layer_idx in range(start, stop):
x, style = self.__getattr__(f'layer{layer_idx}')(
x, wp[:, layer_idx], randomize_noise)
results[f'style{layer_idx:02d}'] = style
if layer_idx % 2 == 0:
temp, style = self.__getattr__(f'output{layer_idx // 2}')(
x, wp[:, layer_idx + 1])
results[f'output_style{layer_idx // 2}'] = style
if layer_idx == 0 or image is None:
image = temp
else:
image = temp + self.upsample(image)
elif self.architecture == 'resnet':
x, style = self.layer0(x)
results[f'style00'] = style
for layer_idx in range(1, self.num_layers - 1, 2):
residual = self.__getattr__(f'skip_layer{layer_idx // 2}')(x)
x, style = self.__getattr__(f'layer{layer_idx}')(
x, wp[:, layer_idx], randomize_noise)
results[f'style{layer_idx:02d}'] = style
x, style = self.__getattr__(f'layer{layer_idx + 1}')(
x, wp[:, layer_idx + 1], randomize_noise)
results[f'style{layer_idx + 1:02d}'] = style
x = (x + residual) / np.sqrt(2.0)
image, style = self.__getattr__(f'output{layer_idx // 2 + 1}')(
x, wp[:, layer_idx + 2])
results[f'output_style{layer_idx // 2}'] = style
results['image'] = self.final_activate(image) if stop == self.num_layers -1 else image
results['x'] = x
return results
class PixelNormLayer(nn.Module):
"""Implements pixel-wise feature vector normalization layer."""
def __init__(self, dim=1, epsilon=1e-8):
super().__init__()
self.dim = dim
self.eps = epsilon
def forward(self, x):
norm = torch.sqrt(
torch.mean(x ** 2, dim=self.dim, keepdim=True) + self.eps)
return x / norm
class UpsamplingLayer(nn.Module):
"""Implements the upsampling layer.
This layer can also be used as filtering by setting `scale_factor` as 1.
"""
def __init__(self,
scale_factor=2,
kernel=(1, 3, 3, 1),
extra_padding=0,
kernel_gain=None):
super().__init__()
assert scale_factor >= 1
self.scale_factor = scale_factor
if extra_padding != 0:
assert scale_factor == 1
if kernel is None:
kernel = np.ones((scale_factor), dtype=np.float32)
else:
kernel = np.array(kernel, dtype=np.float32)
assert kernel.ndim == 1
kernel = np.outer(kernel, kernel)
kernel = kernel / np.sum(kernel)
if kernel_gain is None:
kernel = kernel * (scale_factor ** 2)
else:
assert kernel_gain > 0
kernel = kernel * (kernel_gain ** 2)
assert kernel.ndim == 2
assert kernel.shape[0] == kernel.shape[1]
kernel = kernel[np.newaxis, np.newaxis]
self.register_buffer('kernel', torch.from_numpy(kernel))
self.kernel = self.kernel.flip(0, 1)
self.upsample_padding = (0, scale_factor - 1, # Width padding.
0, 0, # Width.
0, scale_factor - 1, # Height padding.
0, 0, # Height.
0, 0, # Channel.
0, 0) # Batch size.
padding = kernel.shape[2] - scale_factor + extra_padding
self.padding = ((padding + 1) // 2 + scale_factor - 1, padding // 2,
(padding + 1) // 2 + scale_factor - 1, padding // 2)
def forward(self, x):
assert x.ndim == 4
channels = x.shape[1]
if self.scale_factor > 1:
x = x.view(-1, channels, x.shape[2], 1, x.shape[3], 1)
x = F.pad(x, self.upsample_padding, mode='constant', value=0)
x = x.view(-1, channels, x.shape[2] * self.scale_factor,
x.shape[4] * self.scale_factor)
x = x.view(-1, 1, x.shape[2], x.shape[3])
x = F.pad(x, self.padding, mode='constant', value=0)
x = F.conv2d(x, self.kernel, stride=1)
x = x.view(-1, channels, x.shape[2], x.shape[3])
return x
class InputBlock(nn.Module):
"""Implements the input block.
Basically, this block starts from a const input, which is with shape
`(channels, init_resolution, init_resolution)`.
"""
def __init__(self, init_resolution, channels):
super().__init__()
self.const = nn.Parameter(
torch.randn(1, channels, init_resolution, init_resolution))
def forward(self, w):
x = self.const.repeat(w.shape[0], 1, 1, 1)
return x
class ConvBlock(nn.Module):
"""Implements the convolutional block (no style modulation).
Basically, this block executes, convolutional layer, filtering layer (if
needed), and activation layer in sequence.
NOTE: This block is particularly used for skip-connection branch in the
`resnet` structure.
"""
def __init__(self,
in_channels,
out_channels,
kernel_size=3,
add_bias=True,
scale_factor=1,
filtering_kernel=(1, 3, 3, 1),
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.
kernel_size: Size of the convolutional kernels. (default: 3)
add_bias: Whether to add bias onto the convolutional result.
(default: True)
scale_factor: Scale factor for upsampling. `1` means skip
upsampling. (default: 1)
filtering_kernel: Kernel used for filtering after upsampling.
(default: (1, 3, 3, 1))
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__()
if scale_factor > 1:
self.use_conv2d_transpose = True
extra_padding = scale_factor - kernel_size
self.filter = UpsamplingLayer(scale_factor=1,
kernel=filtering_kernel,
extra_padding=extra_padding,
kernel_gain=scale_factor)
self.stride = scale_factor
self.padding = 0 # Padding is done in `UpsamplingLayer`.
else:
self.use_conv2d_transpose = False
assert kernel_size % 2 == 1
self.stride = 1
self.padding = kernel_size // 2
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))
else:
self.bias = None
self.bscale = lr_mul
if activation_type == 'linear':
self.activate = nn.Identity()
self.activate_scale = 1.0
elif activation_type == 'lrelu':
self.activate = nn.LeakyReLU(negative_slope=0.2, inplace=True)
self.activate_scale = np.sqrt(2.0)
else:
raise NotImplementedError(f'Not implemented activation function: '
f'`{activation_type}`!')
def forward(self, x):
weight = self.weight * self.wscale
bias = self.bias * self.bscale if self.bias is not None else None
if self.use_conv2d_transpose:
weight = weight.permute(1, 0, 2, 3).flip(2, 3)
x = F.conv_transpose2d(x,
weight=weight,
bias=bias,
stride=self.scale_factor,
padding=self.padding)
x = self.filter(x)
else:
x = F.conv2d(x,
weight=weight,
bias=bias,
stride=self.stride,
padding=self.padding)
x = self.activate(x) * self.activate_scale
return x
class ModulateConvBlock(nn.Module):
"""Implements the convolutional block with style modulation."""
def __init__(self,
in_channels,
out_channels,
resolution,
w_space_dim,
kernel_size=3,
add_bias=True,
scale_factor=1,
filtering_kernel=(1, 3, 3, 1),
fused_modulate=True,
demodulate=True,
use_wscale=True,
wscale_gain=_WSCALE_GAIN,
lr_mul=1.0,
add_noise=True,
noise_type='spatial',
activation_type='lrelu',
epsilon=1e-8):
"""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.
kernel_size: Size of the convolutional kernels. (default: 3)
add_bias: Whether to add bias onto the convolutional result.
(default: True)
scale_factor: Scale factor for upsampling. `1` means skip
upsampling. (default: 1)
filtering_kernel: Kernel used for filtering after upsampling.
(default: (1, 3, 3, 1))
fused_modulate: Whether to fuse `style_modulate` and `conv2d`
together. (default: True)
demodulate: Whether to perform style demodulation. (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)
add_noise: Whether to add noise onto the output tensor. (default:
True)
noise_type: Type of noise added to the feature map after the
convolution (if needed). Support `spatial` and `channel`.
(default: `spatial`)
activation_type: Type of activation. Support `linear` and `lrelu`.
(default: `lrelu`)
epsilon: Small number to avoid `divide by zero`. (default: 1e-8)
Raises:
NotImplementedError: If the `activation_type` is not supported.
"""
super().__init__()
self.in_c = in_channels
self.out_c = out_channels
self.res = resolution
self.w_space_dim = w_space_dim
self.ksize = kernel_size
self.eps = epsilon
self.space_of_latent = 'w'
if scale_factor > 1:
self.use_conv2d_transpose = True
extra_padding = scale_factor - kernel_size
self.filter = UpsamplingLayer(scale_factor=1,
kernel=filtering_kernel,
extra_padding=extra_padding,
kernel_gain=scale_factor)
self.stride = scale_factor
self.padding = 0 # Padding is done in `UpsamplingLayer`.
else:
self.use_conv2d_transpose = False
assert kernel_size % 2 == 1
self.stride = 1
self.padding = kernel_size // 2
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
self.style = DenseBlock(in_channels=w_space_dim,
out_channels=in_channels,
additional_bias=1.0,
use_wscale=use_wscale,
activation_type='linear')
self.fused_modulate = fused_modulate
self.demodulate = demodulate
if add_bias:
self.bias = nn.Parameter(torch.zeros(out_channels))
else:
self.bias = None
self.bscale = lr_mul
if activation_type == 'linear':
self.activate = nn.Identity()
self.activate_scale = 1.0
elif activation_type == 'lrelu':
self.activate = nn.LeakyReLU(negative_slope=0.2, inplace=True)
self.activate_scale = np.sqrt(2.0)
else:
raise NotImplementedError(f'Not implemented activation function: '
f'`{activation_type}`!')
self.add_noise = add_noise
if self.add_noise:
self.noise_type = noise_type.lower()
if self.noise_type == 'spatial':
self.register_buffer('noise',
torch.randn(1, 1, self.res, self.res))
elif self.noise_type == 'channel':
self.register_buffer('noise',
torch.randn(1, self.channels, 1, 1))
else:
raise NotImplementedError(f'Not implemented noise type: '
f'`{self.noise_type}`!')
self.noise_strength = nn.Parameter(torch.zeros(()))
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.
"""
if self.space_of_latent == '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 '
f'{self.w_space_dim}!\n'
f'But `{w.shape}` is received!')
style = self.style(w)
elif self.space_of_latent == 'y':
if w.ndim != 2 or w.shape[1] < self.in_c:
raise ValueError(f'The input tensor should be with shape '
f'[batch_size, y_space_dim], where '
f'`y_space_dim` equals to {self.in_c}!\n'
f'But `{w.shape}` is received!')
style = w[:, :self.in_c]
return style
def forward(self, x, w, randomize_noise=False):
batch = x.shape[0]
weight = self.weight * self.wscale
weight = weight.permute(2, 3, 1, 0)
# Style modulation.
style = self.forward_style(w)
_weight = weight.view(1, self.ksize, self.ksize, self.in_c, self.out_c)
_weight = _weight * style.view(batch, 1, 1, self.in_c, 1)
# Style demodulation.
if self.demodulate:
_weight_norm = torch.sqrt(
torch.sum(_weight ** 2, dim=[1, 2, 3]) + self.eps)
_weight = _weight / _weight_norm.view(batch, 1, 1, 1, self.out_c)
if self.fused_modulate:
x = x.view(1, batch * self.in_c, x.shape[2], x.shape[3])
weight = _weight.permute(1, 2, 3, 0, 4).reshape(
self.ksize, self.ksize, self.in_c, batch * self.out_c)
else:
x = x * style.view(batch, self.in_c, 1, 1)
if self.use_conv2d_transpose:
weight = weight.flip(0, 1)
if self.fused_modulate:
weight = weight.view(
self.ksize, self.ksize, self.in_c, batch, self.out_c)
weight = weight.permute(0, 1, 4, 3, 2)
weight = weight.reshape(
self.ksize, self.ksize, self.out_c, batch * self.in_c)
weight = weight.permute(3, 2, 0, 1)
else:
weight = weight.permute(2, 3, 0, 1)
x = F.conv_transpose2d(x,
weight=weight,
bias=None,
stride=self.stride,
padding=self.padding,
groups=(batch if self.fused_modulate else 1))
x = self.filter(x)
else:
weight = weight.permute(3, 2, 0, 1)
x = F.conv2d(x,
weight=weight,
bias=None,
stride=self.stride,
padding=self.padding,
groups=(batch if self.fused_modulate else 1))
if self.fused_modulate:
x = x.view(batch, self.out_c, self.res, self.res)
elif self.demodulate:
x = x / _weight_norm.view(batch, self.out_c, 1, 1)
if self.add_noise:
if randomize_noise:
if self.noise_type == 'spatial':
noise = torch.randn(x.shape[0], 1, self.res, self.res).to(x)
elif self.noise_type == 'channel':
noise = torch.randn(x.shape[0], self.channels, 1, 1).to(x)
else:
noise = self.noise
x = x + noise * self.noise_strength.view(1, 1, 1, 1)
bias = self.bias * self.bscale if self.bias is not None else None
if bias is not None:
x = x + bias.view(1, -1, 1, 1)
x = self.activate(x) * self.activate_scale
return x, style
class DenseBlock(nn.Module):
"""Implements the dense block.
Basically, this block executes fully-connected layer and activation layer.
NOTE: This layer supports adding an additional bias beyond the trainable
bias parameter. This is specially used for the mapping from the w code to
the style code.
"""
def __init__(self,
in_channels,
out_channels,
add_bias=True,
additional_bias=0,
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)
additional_bias: The additional bias, which is independent from the
bias parameter. (default: 0.0)
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))
else:
self.bias = None
self.bscale = lr_mul
self.additional_bias = additional_bias
if activation_type == 'linear':
self.activate = nn.Identity()
self.activate_scale = 1.0
elif activation_type == 'lrelu':
self.activate = nn.LeakyReLU(negative_slope=0.2, inplace=True)
self.activate_scale = np.sqrt(2.0)
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 + self.additional_bias) * self.activate_scale
return x
|