Spaces:
Running
on
L40S
Running
on
L40S
File size: 14,018 Bytes
2252f3d |
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 |
'''
Copyright (C) 2019 NVIDIA Corporation. Ting-Chun Wang, Ming-Yu Liu, Jun-Yan Zhu.
BSD License. All rights reserved.
Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:
* Redistributions of source code must retain the above copyright notice, this
list of conditions and the following disclaimer.
* Redistributions in binary form must reproduce the above copyright notice,
this list of conditions and the following disclaimer in the documentation
and/or other materials provided with the distribution.
THE AUTHOR DISCLAIMS ALL WARRANTIES WITH REGARD TO THIS SOFTWARE, INCLUDING ALL
IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR ANY PARTICULAR PURPOSE.
IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY SPECIAL, INDIRECT OR CONSEQUENTIAL
DAMAGES OR ANY DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS ACTION, ARISING
OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE OF THIS SOFTWARE.
'''
import torch
import torch.nn as nn
import functools
import numpy as np
import pytorch_lightning as pl
###############################################################################
# Functions
###############################################################################
def weights_init(m):
classname = m.__class__.__name__
if classname.find('Conv') != -1:
m.weight.data.normal_(0.0, 0.02)
elif classname.find('BatchNorm2d') != -1:
m.weight.data.normal_(1.0, 0.02)
m.bias.data.fill_(0)
def get_norm_layer(norm_type='instance'):
if norm_type == 'batch':
norm_layer = functools.partial(nn.BatchNorm2d, affine=True)
elif norm_type == 'instance':
norm_layer = functools.partial(nn.InstanceNorm2d, affine=False)
else:
raise NotImplementedError('normalization layer [%s] is not found' %
norm_type)
return norm_layer
def define_G(input_nc,
output_nc,
ngf,
netG,
n_downsample_global=3,
n_blocks_global=9,
n_local_enhancers=1,
n_blocks_local=3,
norm='instance',
gpu_ids=[],
last_op=nn.Tanh()):
norm_layer = get_norm_layer(norm_type=norm)
if netG == 'global':
netG = GlobalGenerator(input_nc,
output_nc,
ngf,
n_downsample_global,
n_blocks_global,
norm_layer,
last_op=last_op)
elif netG == 'local':
netG = LocalEnhancer(input_nc, output_nc, ngf, n_downsample_global,
n_blocks_global, n_local_enhancers,
n_blocks_local, norm_layer)
elif netG == 'encoder':
netG = Encoder(input_nc, output_nc, ngf, n_downsample_global,
norm_layer)
else:
raise ('generator not implemented!')
# print(netG)
if len(gpu_ids) > 0:
assert (torch.cuda.is_available())
netG.cuda(gpu_ids[0])
netG.apply(weights_init)
return netG
def print_network(net):
if isinstance(net, list):
net = net[0]
num_params = 0
for param in net.parameters():
num_params += param.numel()
print(net)
print('Total number of parameters: %d' % num_params)
##############################################################################
# Generator
##############################################################################
class LocalEnhancer(pl.LightningModule):
def __init__(self,
input_nc,
output_nc,
ngf=32,
n_downsample_global=3,
n_blocks_global=9,
n_local_enhancers=1,
n_blocks_local=3,
norm_layer=nn.BatchNorm2d,
padding_type='reflect'):
super(LocalEnhancer, self).__init__()
self.n_local_enhancers = n_local_enhancers
###### global generator model #####
ngf_global = ngf * (2**n_local_enhancers)
model_global = GlobalGenerator(input_nc, output_nc, ngf_global,
n_downsample_global, n_blocks_global,
norm_layer).model
model_global = [model_global[i] for i in range(len(model_global) - 3)
] # get rid of final convolution layers
self.model = nn.Sequential(*model_global)
###### local enhancer layers #####
for n in range(1, n_local_enhancers + 1):
# downsample
ngf_global = ngf * (2**(n_local_enhancers - n))
model_downsample = [
nn.ReflectionPad2d(3),
nn.Conv2d(input_nc, ngf_global, kernel_size=7, padding=0),
norm_layer(ngf_global),
nn.ReLU(True),
nn.Conv2d(ngf_global,
ngf_global * 2,
kernel_size=3,
stride=2,
padding=1),
norm_layer(ngf_global * 2),
nn.ReLU(True)
]
# residual blocks
model_upsample = []
for i in range(n_blocks_local):
model_upsample += [
ResnetBlock(ngf_global * 2,
padding_type=padding_type,
norm_layer=norm_layer)
]
# upsample
model_upsample += [
nn.ConvTranspose2d(ngf_global * 2,
ngf_global,
kernel_size=3,
stride=2,
padding=1,
output_padding=1),
norm_layer(ngf_global),
nn.ReLU(True)
]
# final convolution
if n == n_local_enhancers:
model_upsample += [
nn.ReflectionPad2d(3),
nn.Conv2d(ngf, output_nc, kernel_size=7, padding=0),
nn.Tanh()
]
setattr(self, 'model' + str(n) + '_1',
nn.Sequential(*model_downsample))
setattr(self, 'model' + str(n) + '_2',
nn.Sequential(*model_upsample))
self.downsample = nn.AvgPool2d(3,
stride=2,
padding=[1, 1],
count_include_pad=False)
def forward(self, input):
# create input pyramid
input_downsampled = [input]
for i in range(self.n_local_enhancers):
input_downsampled.append(self.downsample(input_downsampled[-1]))
# output at coarest level
output_prev = self.model(input_downsampled[-1])
# build up one layer at a time
for n_local_enhancers in range(1, self.n_local_enhancers + 1):
model_downsample = getattr(self,
'model' + str(n_local_enhancers) + '_1')
model_upsample = getattr(self,
'model' + str(n_local_enhancers) + '_2')
input_i = input_downsampled[self.n_local_enhancers -
n_local_enhancers]
output_prev = model_upsample(
model_downsample(input_i) + output_prev)
return output_prev
class GlobalGenerator(pl.LightningModule):
def __init__(self,
input_nc,
output_nc,
ngf=64,
n_downsampling=3,
n_blocks=9,
norm_layer=nn.BatchNorm2d,
padding_type='reflect',
last_op=nn.Tanh()):
assert (n_blocks >= 0)
super(GlobalGenerator, self).__init__()
activation = nn.ReLU(True)
model = [
nn.ReflectionPad2d(3),
nn.Conv2d(input_nc, ngf, kernel_size=7, padding=0),
norm_layer(ngf), activation
]
# downsample
for i in range(n_downsampling):
mult = 2**i
model += [
nn.Conv2d(ngf * mult,
ngf * mult * 2,
kernel_size=3,
stride=2,
padding=1),
norm_layer(ngf * mult * 2), activation
]
# resnet blocks
mult = 2**n_downsampling
for i in range(n_blocks):
model += [
ResnetBlock(ngf * mult,
padding_type=padding_type,
activation=activation,
norm_layer=norm_layer)
]
# upsample
for i in range(n_downsampling):
mult = 2**(n_downsampling - i)
model += [
nn.ConvTranspose2d(ngf * mult,
int(ngf * mult / 2),
kernel_size=3,
stride=2,
padding=1,
output_padding=1),
norm_layer(int(ngf * mult / 2)), activation
]
model += [
nn.ReflectionPad2d(3),
nn.Conv2d(ngf, output_nc, kernel_size=7, padding=0)
]
if last_op is not None:
model += [last_op]
self.model = nn.Sequential(*model)
def forward(self, input):
return self.model(input)
# Define a resnet block
class ResnetBlock(pl.LightningModule):
def __init__(self,
dim,
padding_type,
norm_layer,
activation=nn.ReLU(True),
use_dropout=False):
super(ResnetBlock, self).__init__()
self.conv_block = self.build_conv_block(dim, padding_type, norm_layer,
activation, use_dropout)
def build_conv_block(self, dim, padding_type, norm_layer, activation,
use_dropout):
conv_block = []
p = 0
if padding_type == 'reflect':
conv_block += [nn.ReflectionPad2d(1)]
elif padding_type == 'replicate':
conv_block += [nn.ReplicationPad2d(1)]
elif padding_type == 'zero':
p = 1
else:
raise NotImplementedError('padding [%s] is not implemented' %
padding_type)
conv_block += [
nn.Conv2d(dim, dim, kernel_size=3, padding=p),
norm_layer(dim), activation
]
if use_dropout:
conv_block += [nn.Dropout(0.5)]
p = 0
if padding_type == 'reflect':
conv_block += [nn.ReflectionPad2d(1)]
elif padding_type == 'replicate':
conv_block += [nn.ReplicationPad2d(1)]
elif padding_type == 'zero':
p = 1
else:
raise NotImplementedError('padding [%s] is not implemented' %
padding_type)
conv_block += [
nn.Conv2d(dim, dim, kernel_size=3, padding=p),
norm_layer(dim)
]
return nn.Sequential(*conv_block)
def forward(self, x):
out = x + self.conv_block(x)
return out
class Encoder(pl.LightningModule):
def __init__(self,
input_nc,
output_nc,
ngf=32,
n_downsampling=4,
norm_layer=nn.BatchNorm2d):
super(Encoder, self).__init__()
self.output_nc = output_nc
model = [
nn.ReflectionPad2d(3),
nn.Conv2d(input_nc, ngf, kernel_size=7, padding=0),
norm_layer(ngf),
nn.ReLU(True)
]
# downsample
for i in range(n_downsampling):
mult = 2**i
model += [
nn.Conv2d(ngf * mult,
ngf * mult * 2,
kernel_size=3,
stride=2,
padding=1),
norm_layer(ngf * mult * 2),
nn.ReLU(True)
]
# upsample
for i in range(n_downsampling):
mult = 2**(n_downsampling - i)
model += [
nn.ConvTranspose2d(ngf * mult,
int(ngf * mult / 2),
kernel_size=3,
stride=2,
padding=1,
output_padding=1),
norm_layer(int(ngf * mult / 2)),
nn.ReLU(True)
]
model += [
nn.ReflectionPad2d(3),
nn.Conv2d(ngf, output_nc, kernel_size=7, padding=0),
nn.Tanh()
]
self.model = nn.Sequential(*model)
def forward(self, input, inst):
outputs = self.model(input)
# instance-wise average pooling
outputs_mean = outputs.clone()
inst_list = np.unique(inst.cpu().numpy().astype(int))
for i in inst_list:
for b in range(input.size()[0]):
indices = (inst[b:b + 1] == int(i)).nonzero() # n x 4
for j in range(self.output_nc):
output_ins = outputs[indices[:, 0] + b, indices[:, 1] + j,
indices[:, 2], indices[:, 3]]
mean_feat = torch.mean(output_ins).expand_as(output_ins)
outputs_mean[indices[:, 0] + b, indices[:, 1] + j,
indices[:, 2], indices[:, 3]] = mean_feat
return outputs_mean
|