File size: 16,095 Bytes
ff2b8e3 |
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 |
# python3.7
"""Contains the implementation of discriminator described in PGGAN.
Paper: https://arxiv.org/pdf/1710.10196.pdf
Official TensorFlow implementation:
https://github.com/tkarras/progressive_growing_of_gans
"""
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
__all__ = ['PGGANDiscriminator']
# Resolutions allowed.
_RESOLUTIONS_ALLOWED = [8, 16, 32, 64, 128, 256, 512, 1024]
# Initial resolution.
_INIT_RES = 4
# Default gain factor for weight scaling.
_WSCALE_GAIN = np.sqrt(2.0)
class PGGANDiscriminator(nn.Module):
"""Defines the discriminator network in PGGAN.
NOTE: The discriminator takes images with `RGB` channel order and pixel
range [-1, 1] as inputs.
Settings for the network:
(1) resolution: The resolution of the input image.
(2) image_channels: Number of channels of the input image. (default: 3)
(3) label_size: Size of the additional label for conditional generation.
(default: 0)
(4) fused_scale: Whether to fused `conv2d` and `downsample` together,
resulting in `conv2d` with strides. (default: False)
(5) use_wscale: Whether to use weight scaling. (default: True)
(6) minibatch_std_group_size: Group size for the minibatch standard
deviation layer. 0 means disable. (default: 16)
(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,
image_channels=3,
label_size=0,
fused_scale=False,
use_wscale=True,
minibatch_std_group_size=16,
fmaps_base=16 << 10,
fmaps_max=512):
"""Initializes with basic settings.
Raises:
ValueError: If the `resolution` is not supported.
"""
super().__init__()
if resolution not in _RESOLUTIONS_ALLOWED:
raise ValueError(f'Invalid resolution: `{resolution}`!\n'
f'Resolutions allowed: {_RESOLUTIONS_ALLOWED}.')
self.init_res = _INIT_RES
self.init_res_log2 = int(np.log2(self.init_res))
self.resolution = resolution
self.final_res_log2 = int(np.log2(self.resolution))
self.image_channels = image_channels
self.label_size = label_size
self.fused_scale = fused_scale
self.use_wscale = use_wscale
self.minibatch_std_group_size = minibatch_std_group_size
self.fmaps_base = fmaps_base
self.fmaps_max = fmaps_max
# 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.final_res_log2, self.init_res_log2 - 1, -1):
res = 2 ** res_log2
block_idx = self.final_res_log2 - res_log2
# Input convolution layer for each resolution.
self.add_module(
f'input{block_idx}',
ConvBlock(in_channels=self.image_channels,
out_channels=self.get_nf(res),
kernel_size=1,
padding=0,
use_wscale=self.use_wscale))
self.pth_to_tf_var_mapping[f'input{block_idx}.weight'] = (
f'FromRGB_lod{block_idx}/weight')
self.pth_to_tf_var_mapping[f'input{block_idx}.bias'] = (
f'FromRGB_lod{block_idx}/bias')
# Convolution block for each resolution (except the last one).
if res != self.init_res:
self.add_module(
f'layer{2 * block_idx}',
ConvBlock(in_channels=self.get_nf(res),
out_channels=self.get_nf(res),
use_wscale=self.use_wscale))
tf_layer0_name = 'Conv0'
self.add_module(
f'layer{2 * block_idx + 1}',
ConvBlock(in_channels=self.get_nf(res),
out_channels=self.get_nf(res // 2),
downsample=True,
fused_scale=self.fused_scale,
use_wscale=self.use_wscale))
tf_layer1_name = 'Conv1_down' if self.fused_scale else 'Conv1'
# Convolution block for last resolution.
else:
self.add_module(
f'layer{2 * block_idx}',
ConvBlock(
in_channels=self.get_nf(res),
out_channels=self.get_nf(res),
use_wscale=self.use_wscale,
minibatch_std_group_size=self.minibatch_std_group_size))
tf_layer0_name = 'Conv'
self.add_module(
f'layer{2 * block_idx + 1}',
DenseBlock(in_channels=self.get_nf(res) * res * res,
out_channels=self.get_nf(res // 2),
use_wscale=self.use_wscale))
tf_layer1_name = 'Dense0'
self.pth_to_tf_var_mapping[f'layer{2 * block_idx}.weight'] = (
f'{res}x{res}/{tf_layer0_name}/weight')
self.pth_to_tf_var_mapping[f'layer{2 * block_idx}.bias'] = (
f'{res}x{res}/{tf_layer0_name}/bias')
self.pth_to_tf_var_mapping[f'layer{2 * block_idx + 1}.weight'] = (
f'{res}x{res}/{tf_layer1_name}/weight')
self.pth_to_tf_var_mapping[f'layer{2 * block_idx + 1}.bias'] = (
f'{res}x{res}/{tf_layer1_name}/bias')
# Final dense block.
self.add_module(
f'layer{2 * block_idx + 2}',
DenseBlock(in_channels=self.get_nf(res // 2),
out_channels=1 + self.label_size,
use_wscale=self.use_wscale,
wscale_gain=1.0,
activation_type='linear'))
self.pth_to_tf_var_mapping[f'layer{2 * block_idx + 2}.weight'] = (
f'{res}x{res}/Dense1/weight')
self.pth_to_tf_var_mapping[f'layer{2 * block_idx + 2}.bias'] = (
f'{res}x{res}/Dense1/bias')
self.downsample = DownsamplingLayer()
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, image, lod=None, **_unused_kwargs):
expected_shape = (self.image_channels, self.resolution, self.resolution)
if image.ndim != 4 or image.shape[1:] != expected_shape:
raise ValueError(f'The input tensor should be with shape '
f'[batch_size, channel, height, width], where '
f'`channel` equals to {self.image_channels}, '
f'`height`, `width` equal to {self.resolution}!\n'
f'But `{image.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!')
lod = self.lod.cpu().tolist()
for res_log2 in range(self.final_res_log2, self.init_res_log2 - 1, -1):
block_idx = current_lod = self.final_res_log2 - res_log2
if current_lod <= lod < current_lod + 1:
x = self.__getattr__(f'input{block_idx}')(image)
elif current_lod - 1 < lod < current_lod:
alpha = lod - np.floor(lod)
x = (self.__getattr__(f'input{block_idx}')(image) * alpha +
x * (1 - alpha))
if lod < current_lod + 1:
x = self.__getattr__(f'layer{2 * block_idx}')(x)
x = self.__getattr__(f'layer{2 * block_idx + 1}')(x)
if lod > current_lod:
image = self.downsample(image)
x = self.__getattr__(f'layer{2 * block_idx + 2}')(x)
return x
class MiniBatchSTDLayer(nn.Module):
"""Implements the minibatch standard deviation layer."""
def __init__(self, group_size=16, epsilon=1e-8):
super().__init__()
self.group_size = group_size
self.epsilon = epsilon
def forward(self, x):
if self.group_size <= 1:
return x
group_size = min(self.group_size, x.shape[0]) # [NCHW]
y = x.view(group_size, -1, x.shape[1], x.shape[2], x.shape[3]) # [GMCHW]
y = y - torch.mean(y, dim=0, keepdim=True) # [GMCHW]
y = torch.mean(y ** 2, dim=0) # [MCHW]
y = torch.sqrt(y + self.epsilon) # [MCHW]
y = torch.mean(y, dim=[1, 2, 3], keepdim=True) # [M111]
y = y.repeat(group_size, 1, x.shape[2], x.shape[3]) # [N1HW]
return torch.cat([x, y], dim=1)
class DownsamplingLayer(nn.Module):
"""Implements the downsampling layer.
Basically, this layer can be used to downsample feature maps with average
pooling.
"""
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.avg_pool2d(x,
kernel_size=self.scale_factor,
stride=self.scale_factor,
padding=0)
class ConvBlock(nn.Module):
"""Implements the convolutional block.
Basically, this block executes minibatch standard deviation layer (if
needed), convolutional layer, activation layer, and downsampling layer (
if needed) in sequence.
"""
def __init__(self,
in_channels,
out_channels,
kernel_size=3,
stride=1,
padding=1,
add_bias=True,
downsample=False,
fused_scale=False,
use_wscale=True,
wscale_gain=_WSCALE_GAIN,
activation_type='lrelu',
minibatch_std_group_size=0):
"""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)
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)
downsample: Whether to downsample the result after convolution.
(default: False)
fused_scale: Whether to fused `conv2d` and `downsample` together,
resulting in `conv2d` with strides. (default: False)
use_wscale: Whether to use weight scaling. (default: True)
wscale_gain: Gain factor for weight scaling. (default: _WSCALE_GAIN)
activation_type: Type of activation. Support `linear` and `lrelu`.
(default: `lrelu`)
minibatch_std_group_size: Group size for the minibatch standard
deviation layer. 0 means disable. (default: 0)
Raises:
NotImplementedError: If the `activation_type` is not supported.
"""
super().__init__()
if minibatch_std_group_size > 1:
in_channels = in_channels + 1
self.mbstd = MiniBatchSTDLayer(group_size=minibatch_std_group_size)
else:
self.mbstd = nn.Identity()
if downsample and not fused_scale:
self.downsample = DownsamplingLayer()
else:
self.downsample = nn.Identity()
if downsample and fused_scale:
self.use_stride = True
self.stride = 2
self.padding = 1
else:
self.use_stride = 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))
self.wscale = wscale
else:
self.weight = nn.Parameter(torch.randn(*weight_shape) * wscale)
self.wscale = 1.0
if add_bias:
self.bias = nn.Parameter(torch.zeros(out_channels))
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):
x = self.mbstd(x)
weight = self.weight * self.wscale
if self.use_stride:
weight = F.pad(weight, (1, 1, 1, 1, 0, 0, 0, 0), 'constant', 0.0)
weight = (weight[:, :, 1:, 1:] + weight[:, :, :-1, 1:] +
weight[:, :, 1:, :-1] + weight[:, :, :-1, :-1]) * 0.25
x = F.conv2d(x,
weight=weight,
bias=self.bias,
stride=self.stride,
padding=self.padding)
x = self.activate(x)
x = self.downsample(x)
return x
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,
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)
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))
self.wscale = wscale
else:
self.weight = nn.Parameter(torch.randn(*weight_shape) * wscale)
self.wscale = 1.0
if add_bias:
self.bias = nn.Parameter(torch.zeros(out_channels))
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)
x = F.linear(x, weight=self.weight * self.wscale, bias=self.bias)
x = self.activate(x)
return x
|