Spaces:
Running
on
Zero
Running
on
Zero
File size: 22,966 Bytes
11e6f7b |
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 |
# SPDX-FileCopyrightText: Copyright (c) 2021-2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: LicenseRef-NvidiaProprietary
#
# NVIDIA CORPORATION, its affiliates and licensors retain all intellectual
# property and proprietary rights in and to this material, related
# documentation and any modifications thereto. Any use, reproduction,
# disclosure or distribution of this material and related documentation
# without an express license agreement from NVIDIA CORPORATION or
# its affiliates is strictly prohibited.
"""Discriminator architectures from the paper
"Efficient Geometry-aware 3D Generative Adversarial Networks"."""
import numpy as np
import torch
from utils.torch_utils import persistence
from utils.torch_utils.ops import upfirdn2d
from .networks_stylegan2 import DiscriminatorBlock, MappingNetwork, DiscriminatorEpilogue
from pdb import set_trace as st
@persistence.persistent_class
class SingleDiscriminator(torch.nn.Module):
def __init__(
self,
c_dim, # Conditioning label (C) dimensionality.
img_resolution, # Input resolution.
img_channels, # Number of input color channels.
architecture='resnet', # Architecture: 'orig', 'skip', 'resnet'.
channel_base=32768, # Overall multiplier for the number of channels.
channel_max=512, # Maximum number of channels in any layer.
num_fp16_res=4, # Use FP16 for the N highest resolutions.
conv_clamp=256, # Clamp the output of convolution layers to +-X, None = disable clamping.
cmap_dim=None, # Dimensionality of mapped conditioning label, None = default.
sr_upsample_factor=1, # Ignored for SingleDiscriminator
block_kwargs={}, # Arguments for DiscriminatorBlock.
mapping_kwargs={}, # Arguments for MappingNetwork.
epilogue_kwargs={}, # Arguments for DiscriminatorEpilogue.
):
super().__init__()
self.c_dim = c_dim
self.img_resolution = img_resolution
self.img_resolution_log2 = int(np.log2(img_resolution))
self.img_channels = img_channels
self.block_resolutions = [
2**i for i in range(self.img_resolution_log2, 2, -1)
]
channels_dict = {
res: min(channel_base // res, channel_max)
for res in self.block_resolutions + [4]
}
fp16_resolution = max(2**(self.img_resolution_log2 + 1 - num_fp16_res),
8)
if cmap_dim is None:
cmap_dim = channels_dict[4]
if c_dim == 0:
cmap_dim = 0
common_kwargs = dict(img_channels=img_channels,
architecture=architecture,
conv_clamp=conv_clamp)
cur_layer_idx = 0
for res in self.block_resolutions:
in_channels = channels_dict[res] if res < img_resolution else 0
tmp_channels = channels_dict[res]
out_channels = channels_dict[res // 2]
use_fp16 = (res >= fp16_resolution)
block = DiscriminatorBlock(in_channels,
tmp_channels,
out_channels,
resolution=res,
first_layer_idx=cur_layer_idx,
use_fp16=use_fp16,
**block_kwargs,
**common_kwargs)
setattr(self, f'b{res}', block)
cur_layer_idx += block.num_layers
if c_dim > 0:
self.mapping = MappingNetwork(z_dim=0,
c_dim=c_dim,
w_dim=cmap_dim,
num_ws=None,
w_avg_beta=None,
**mapping_kwargs)
self.b4 = DiscriminatorEpilogue(channels_dict[4],
cmap_dim=cmap_dim,
resolution=4,
**epilogue_kwargs,
**common_kwargs)
def forward(self, img, c, update_emas=False, **block_kwargs):
img = img['image']
_ = update_emas # unused
x = None
for res in self.block_resolutions:
block = getattr(self, f'b{res}')
x, img = block(x, img, **block_kwargs)
cmap = None
if self.c_dim > 0:
cmap = self.mapping(None, c)
x = self.b4(x, img, cmap)
return x
def extra_repr(self):
return f'c_dim={self.c_dim:d}, img_resolution={self.img_resolution:d}, img_channels={self.img_channels:d}'
#----------------------------------------------------------------------------
def filtered_resizing(image_orig_tensor, size, f, filter_mode='antialiased'):
if filter_mode == 'antialiased':
ada_filtered_64 = torch.nn.functional.interpolate(image_orig_tensor,
size=(size, size),
mode='bilinear',
align_corners=False,
antialias=True)
elif filter_mode == 'classic':
ada_filtered_64 = upfirdn2d.upsample2d(image_orig_tensor, f, up=2)
ada_filtered_64 = torch.nn.functional.interpolate(ada_filtered_64,
size=(size * 2 + 2,
size * 2 + 2),
mode='bilinear',
align_corners=False)
ada_filtered_64 = upfirdn2d.downsample2d(ada_filtered_64,
f,
down=2,
flip_filter=True,
padding=-1)
elif filter_mode == 'none':
ada_filtered_64 = torch.nn.functional.interpolate(image_orig_tensor,
size=(size, size),
mode='bilinear',
align_corners=False)
elif type(filter_mode) == float:
assert 0 < filter_mode < 1
filtered = torch.nn.functional.interpolate(image_orig_tensor,
size=(size, size),
mode='bilinear',
align_corners=False,
antialias=True)
aliased = torch.nn.functional.interpolate(image_orig_tensor,
size=(size, size),
mode='bilinear',
align_corners=False,
antialias=False)
ada_filtered_64 = (1 -
filter_mode) * aliased + (filter_mode) * filtered
return ada_filtered_64
#----------------------------------------------------------------------------
@persistence.persistent_class
class DualDiscriminator(torch.nn.Module):
def __init__(
self,
c_dim, # Conditioning label (C) dimensionality.
img_resolution, # Input resolution.
img_channels, # Number of input color channels.
architecture='resnet', # Architecture: 'orig', 'skip', 'resnet'.
channel_base=32768, # Overall multiplier for the number of channels.
channel_max=512, # Maximum number of channels in any layer.
num_fp16_res=4, # Use FP16 for the N highest resolutions.
conv_clamp=256, # Clamp the output of convolution layers to +-X, None = disable clamping.
cmap_dim=None, # Dimensionality of mapped conditioning label, None = default.
disc_c_noise=0, # Corrupt camera parameters with X std dev of noise before disc. pose conditioning.
block_kwargs={}, # Arguments for DiscriminatorBlock.
mapping_kwargs={}, # Arguments for MappingNetwork.
epilogue_kwargs={}, # Arguments for DiscriminatorEpilogue.
):
super().__init__()
# img_channels *= 2
if img_channels == 3:
img_channels *= 2
self.c_dim = c_dim
self.img_resolution = img_resolution
self.img_resolution_log2 = int(np.log2(img_resolution))
self.img_channels = img_channels
self.block_resolutions = [
2**i for i in range(self.img_resolution_log2, 2, -1)
]
channels_dict = {
res: min(channel_base // res, channel_max)
for res in self.block_resolutions + [4]
}
fp16_resolution = max(2**(self.img_resolution_log2 + 1 - num_fp16_res),
8)
if cmap_dim is None:
cmap_dim = channels_dict[4]
if c_dim == 0:
cmap_dim = 0
common_kwargs = dict(img_channels=img_channels,
architecture=architecture,
conv_clamp=conv_clamp)
cur_layer_idx = 0
for res in self.block_resolutions:
in_channels = channels_dict[res] if res < img_resolution else 0
tmp_channels = channels_dict[res]
out_channels = channels_dict[res // 2]
use_fp16 = (res >= fp16_resolution)
block = DiscriminatorBlock(in_channels,
tmp_channels,
out_channels,
resolution=res,
first_layer_idx=cur_layer_idx,
use_fp16=use_fp16,
**block_kwargs,
**common_kwargs)
setattr(self, f'b{res}', block)
cur_layer_idx += block.num_layers
if c_dim > 0:
self.mapping = MappingNetwork(z_dim=0,
c_dim=c_dim,
w_dim=cmap_dim,
num_ws=None,
w_avg_beta=None,
**mapping_kwargs)
self.b4 = DiscriminatorEpilogue(channels_dict[4],
cmap_dim=cmap_dim,
resolution=4,
**epilogue_kwargs,
**common_kwargs)
self.register_buffer('resample_filter',
upfirdn2d.setup_filter([1, 3, 3, 1]))
self.disc_c_noise = disc_c_noise
def forward(self, img, c, update_emas=False, **block_kwargs):
image_raw = filtered_resizing(img['image_raw'],
# size=img['image'].shape[-1],
size=img['image_sr'].shape[-1],
f=self.resample_filter)
# img = torch.cat([img['image'], image_raw], 1)
img = torch.cat([img['image_sr'], image_raw], 1)
_ = update_emas # unused
x = None
for res in self.block_resolutions:
block = getattr(self, f'b{res}')
x, img = block(x, img, **block_kwargs)
cmap = None
if self.c_dim > 0:
if self.disc_c_noise > 0:
c += torch.randn_like(c) * c.std(0) * self.disc_c_noise
cmap = self.mapping(None, c)
x = self.b4(x, img, cmap)
return x
def extra_repr(self):
return f'c_dim={self.c_dim:d}, img_resolution={self.img_resolution:d}, img_channels={self.img_channels:d}'
@persistence.persistent_class
class GeoDualDiscriminator(DualDiscriminator):
def __init__(self, c_dim, img_resolution, img_channels, architecture='resnet', channel_base=32768, channel_max=512, num_fp16_res=4, conv_clamp=256, cmap_dim=None, disc_c_noise=0, block_kwargs={}, mapping_kwargs={}, epilogue_kwargs={}, normal_condition=False):
super().__init__(c_dim, img_resolution, img_channels, architecture, channel_base, channel_max, num_fp16_res, conv_clamp, cmap_dim, disc_c_noise, block_kwargs, mapping_kwargs, epilogue_kwargs)
self.normal_condition = normal_condition
def forward(self, img, c, update_emas=False, **block_kwargs):
image= img['image']
image_raw = filtered_resizing(img['image_raw'],
size=img['image'].shape[-1],
f=self.resample_filter)
D_input_img = torch.cat([image, image_raw], 1)
image_depth = filtered_resizing(img['image_depth'], size=img['image'].shape[-1], f=self.resample_filter)
if self.normal_condition and 'normal' in img:
image_normal = filtered_resizing(img['normal'], size=img['image'].shape[-1], f=self.resample_filter)
D_input_img = torch.cat([D_input_img, image_depth, image_normal], 1)
else:
D_input_img = torch.cat([D_input_img, image_depth], 1)
img = D_input_img
_ = update_emas # unused
x = None
for res in self.block_resolutions:
block = getattr(self, f'b{res}')
x, img = block(x, img, **block_kwargs)
cmap = None
if self.c_dim > 0:
if self.disc_c_noise > 0:
c += torch.randn_like(c) * c.std(0) * self.disc_c_noise
cmap = self.mapping(None, c)
x = self.b4(x, img, cmap)
return x
#----------------------------------------------------------------------------
@persistence.persistent_class
class DummyDualDiscriminator(torch.nn.Module):
def __init__(
self,
c_dim, # Conditioning label (C) dimensionality.
img_resolution, # Input resolution.
img_channels, # Number of input color channels.
architecture='resnet', # Architecture: 'orig', 'skip', 'resnet'.
channel_base=32768, # Overall multiplier for the number of channels.
channel_max=512, # Maximum number of channels in any layer.
num_fp16_res=4, # Use FP16 for the N highest resolutions.
conv_clamp=256, # Clamp the output of convolution layers to +-X, None = disable clamping.
cmap_dim=None, # Dimensionality of mapped conditioning label, None = default.
block_kwargs={}, # Arguments for DiscriminatorBlock.
mapping_kwargs={}, # Arguments for MappingNetwork.
epilogue_kwargs={}, # Arguments for DiscriminatorEpilogue.
):
super().__init__()
img_channels *= 2
self.c_dim = c_dim
self.img_resolution = img_resolution
self.img_resolution_log2 = int(np.log2(img_resolution))
self.img_channels = img_channels
self.block_resolutions = [
2**i for i in range(self.img_resolution_log2, 2, -1)
]
channels_dict = {
res: min(channel_base // res, channel_max)
for res in self.block_resolutions + [4]
}
fp16_resolution = max(2**(self.img_resolution_log2 + 1 - num_fp16_res),
8)
if cmap_dim is None:
cmap_dim = channels_dict[4]
if c_dim == 0:
cmap_dim = 0
common_kwargs = dict(img_channels=img_channels,
architecture=architecture,
conv_clamp=conv_clamp)
cur_layer_idx = 0
for res in self.block_resolutions:
in_channels = channels_dict[res] if res < img_resolution else 0
tmp_channels = channels_dict[res]
out_channels = channels_dict[res // 2]
use_fp16 = (res >= fp16_resolution)
block = DiscriminatorBlock(in_channels,
tmp_channels,
out_channels,
resolution=res,
first_layer_idx=cur_layer_idx,
use_fp16=use_fp16,
**block_kwargs,
**common_kwargs)
setattr(self, f'b{res}', block)
cur_layer_idx += block.num_layers
if c_dim > 0:
self.mapping = MappingNetwork(z_dim=0,
c_dim=c_dim,
w_dim=cmap_dim,
num_ws=None,
w_avg_beta=None,
**mapping_kwargs)
self.b4 = DiscriminatorEpilogue(channels_dict[4],
cmap_dim=cmap_dim,
resolution=4,
**epilogue_kwargs,
**common_kwargs)
self.register_buffer('resample_filter',
upfirdn2d.setup_filter([1, 3, 3, 1]))
self.raw_fade = 1
def forward(self, img, c, update_emas=False, **block_kwargs):
self.raw_fade = max(0, self.raw_fade - 1 / (500000 / 32))
image_raw = filtered_resizing(img['image_raw'],
size=img['image'].shape[-1],
f=self.resample_filter) * self.raw_fade
img = torch.cat([img['image'], image_raw], 1)
_ = update_emas # unused
x = None
for res in self.block_resolutions:
block = getattr(self, f'b{res}')
x, img = block(x, img, **block_kwargs)
cmap = None
if self.c_dim > 0:
cmap = self.mapping(None, c)
x = self.b4(x, img, cmap)
return x
def extra_repr(self):
return f'c_dim={self.c_dim:d}, img_resolution={self.img_resolution:d}, img_channels={self.img_channels:d}'
#----------------------------------------------------------------------------
# panohead
# Tri-discriminator: upsampled image, super-resolved image, and segmentation mask
# V2: first concatenate imgs and seg mask, using only one conv block
@persistence.persistent_class
class MaskDualDiscriminatorV2(torch.nn.Module):
def __init__(self,
c_dim, # Conditioning label (C) dimensionality.
img_resolution, # Input resolution.
img_channels, # Number of input color channels.
seg_resolution, # Input resolution.
seg_channels, # Number of input color channels.
architecture = 'resnet', # Architecture: 'orig', 'skip', 'resnet'.
channel_base = 32768, # Overall multiplier for the number of channels.
channel_max = 512, # Maximum number of channels in any layer.
num_fp16_res = 4, # Use FP16 for the N highest resolutions.
conv_clamp = 256, # Clamp the output of convolution layers to +-X, None = disable clamping.
cmap_dim = None, # Dimensionality of mapped conditioning label, None = default.
disc_c_noise = 0, # Corrupt camera parameters with X std dev of noise before disc. pose conditioning.
block_kwargs = {}, # Arguments for DiscriminatorBlock.
mapping_kwargs = {}, # Arguments for MappingNetwork.
epilogue_kwargs = {}, # Arguments for DiscriminatorEpilogue.
):
super().__init__()
img_channels = img_channels * 2 + seg_channels
self.c_dim = c_dim
self.img_resolution = img_resolution
self.img_resolution_log2 = int(np.log2(img_resolution))
self.img_channels = img_channels
self.block_resolutions = [2 ** i for i in range(self.img_resolution_log2, 2, -1)]
channels_dict = {res: min(channel_base // res, channel_max) for res in self.block_resolutions + [4]}
fp16_resolution = max(2 ** (self.img_resolution_log2 + 1 - num_fp16_res), 8)
if cmap_dim is None:
cmap_dim = channels_dict[4]
if c_dim == 0:
cmap_dim = 0
common_kwargs = dict(img_channels=img_channels, architecture=architecture, conv_clamp=conv_clamp)
cur_layer_idx = 0
for res in self.block_resolutions:
in_channels = channels_dict[res] if res < img_resolution else 0
tmp_channels = channels_dict[res]
out_channels = channels_dict[res // 2]
use_fp16 = (res >= fp16_resolution)
block = DiscriminatorBlock(in_channels, tmp_channels, out_channels, resolution=res,
first_layer_idx=cur_layer_idx, use_fp16=use_fp16, **block_kwargs, **common_kwargs)
setattr(self, f'b{res}', block)
cur_layer_idx += block.num_layers
if c_dim > 0:
self.mapping = MappingNetwork(z_dim=0, c_dim=c_dim, w_dim=cmap_dim, num_ws=None, w_avg_beta=None, **mapping_kwargs)
self.b4 = DiscriminatorEpilogue(channels_dict[4], cmap_dim=cmap_dim, resolution=4, **epilogue_kwargs, **common_kwargs)
self.register_buffer('resample_filter', upfirdn2d.setup_filter([1,3,3,1]))
self.disc_c_noise = disc_c_noise
def forward(self, img, c, update_emas=False, **block_kwargs):
image_raw = filtered_resizing(img['image_raw'], size=img['image'].shape[-1], f=self.resample_filter)
seg = filtered_resizing(img['image_mask'], size=img['image'].shape[-1], f=self.resample_filter)
seg = 2 * seg - 1 # normalize to [-1,1]
img = torch.cat([img['image'], image_raw, seg], 1)
_ = update_emas # unused
x = None
for res in self.block_resolutions:
block = getattr(self, f'b{res}')
x, img = block(x, img, **block_kwargs)
cmap = None
if self.c_dim > 0:
if self.disc_c_noise > 0: c += torch.randn_like(c) * c.std(0) * self.disc_c_noise
cmap = self.mapping(None, c)
x = self.b4(x, img, cmap)
return x
def extra_repr(self):
return ' '.join([
f'c_dim={self.c_dim:d},',
f'img_resolution={self.img_resolution:d}, img_channels={self.img_channels:d},',
f'seg_resolution={self.seg_resolution:d}, seg_channels={self.seg_channels:d}']) |