Spaces:
Runtime error
Runtime error
File size: 12,764 Bytes
ce190ee |
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 |
"""Discriminator architecture for ClimateGAN's GAN components (a and t)
"""
import functools
import torch
import torch.nn as nn
from climategan.blocks import SpectralNorm
from climategan.tutils import init_weights
# from torch.optim import lr_scheduler
# mainly from https://github.com/sangwoomo/instagan/blob/master/models/networks.py
def create_discriminator(opts, device, no_init=False, verbose=0):
disc = OmniDiscriminator(opts)
if no_init:
return disc
for task, model in disc.items():
if isinstance(model, nn.ModuleDict):
for domain, domain_model in model.items():
init_weights(
domain_model,
init_type=opts.dis[task].init_type,
init_gain=opts.dis[task].init_gain,
verbose=verbose,
caller=f"create_discriminator {task} {domain}",
)
else:
init_weights(
model,
init_type=opts.dis[task].init_type,
init_gain=opts.dis[task].init_gain,
verbose=verbose,
caller=f"create_discriminator {task}",
)
return disc.to(device)
def define_D(
input_nc,
ndf,
n_layers=3,
norm="batch",
use_sigmoid=False,
get_intermediate_features=False,
num_D=1,
):
norm_layer = get_norm_layer(norm_type=norm)
net = MultiscaleDiscriminator(
input_nc,
ndf,
n_layers=n_layers,
norm_layer=norm_layer,
use_sigmoid=use_sigmoid,
get_intermediate_features=get_intermediate_features,
num_D=num_D,
)
return net
def get_norm_layer(norm_type="instance"):
if not norm_type:
print("norm_type is {}, defaulting to instance")
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, track_running_stats=False
)
elif norm_type == "none":
norm_layer = None
else:
raise NotImplementedError("normalization layer [%s] is not found" % norm_type)
return norm_layer
# Defines the PatchGAN discriminator with the specified arguments.
class NLayerDiscriminator(nn.Module):
def __init__(
self,
input_nc=3,
ndf=64,
n_layers=3,
norm_layer=nn.BatchNorm2d,
use_sigmoid=False,
get_intermediate_features=True,
):
super(NLayerDiscriminator, self).__init__()
if type(norm_layer) == functools.partial:
use_bias = norm_layer.func == nn.InstanceNorm2d
else:
use_bias = norm_layer == nn.InstanceNorm2d
self.get_intermediate_features = get_intermediate_features
kw = 4
padw = 1
sequence = [
[
# Use spectral normalization
SpectralNorm(
nn.Conv2d(input_nc, ndf, kernel_size=kw, stride=2, padding=padw)
),
nn.LeakyReLU(0.2, True),
]
]
nf_mult = 1
nf_mult_prev = 1
for n in range(1, n_layers):
nf_mult_prev = nf_mult
nf_mult = min(2 ** n, 8)
sequence += [
[
# Use spectral normalization
SpectralNorm( # TODO replace with Conv2dBlock
nn.Conv2d(
ndf * nf_mult_prev,
ndf * nf_mult,
kernel_size=kw,
stride=2,
padding=padw,
bias=use_bias,
)
),
norm_layer(ndf * nf_mult),
nn.LeakyReLU(0.2, True),
]
]
nf_mult_prev = nf_mult
nf_mult = min(2 ** n_layers, 8)
sequence += [
[
# Use spectral normalization
SpectralNorm(
nn.Conv2d(
ndf * nf_mult_prev,
ndf * nf_mult,
kernel_size=kw,
stride=1,
padding=padw,
bias=use_bias,
)
),
norm_layer(ndf * nf_mult),
nn.LeakyReLU(0.2, True),
]
]
# Use spectral normalization
sequence += [
[
SpectralNorm(
nn.Conv2d(ndf * nf_mult, 1, kernel_size=kw, stride=1, padding=padw)
)
]
]
if use_sigmoid:
sequence += [[nn.Sigmoid()]]
# We divide the layers into groups to extract intermediate layer outputs
for n in range(len(sequence)):
self.add_module("model" + str(n), nn.Sequential(*sequence[n]))
# self.model = nn.Sequential(*sequence)
def forward(self, input):
results = [input]
for submodel in self.children():
intermediate_output = submodel(results[-1])
results.append(intermediate_output)
get_intermediate_features = self.get_intermediate_features
if get_intermediate_features:
return results[1:]
else:
return results[-1]
# def forward(self, input):
# return self.model(input)
# Source: https://github.com/NVIDIA/pix2pixHD
class MultiscaleDiscriminator(nn.Module):
def __init__(
self,
input_nc=3,
ndf=64,
n_layers=3,
norm_layer=nn.BatchNorm2d,
use_sigmoid=False,
get_intermediate_features=True,
num_D=3,
):
super(MultiscaleDiscriminator, self).__init__()
# self, input_nc, ndf=64, n_layers=3, norm_layer=nn.BatchNorm2d,
# use_sigmoid=False, num_D=3, getIntermFeat=False
self.n_layers = n_layers
self.ndf = ndf
self.norm_layer = norm_layer
self.use_sigmoid = use_sigmoid
self.get_intermediate_features = get_intermediate_features
self.num_D = num_D
for i in range(self.num_D):
netD = NLayerDiscriminator(
input_nc=input_nc,
ndf=self.ndf,
n_layers=self.n_layers,
norm_layer=self.norm_layer,
use_sigmoid=self.use_sigmoid,
get_intermediate_features=self.get_intermediate_features,
)
self.add_module("discriminator_%d" % i, netD)
self.downsample = nn.AvgPool2d(
3, stride=2, padding=[1, 1], count_include_pad=False
)
def forward(self, input):
result = []
get_intermediate_features = self.get_intermediate_features
for name, D in self.named_children():
if "discriminator" not in name:
continue
out = D(input)
if not get_intermediate_features:
out = [out]
result.append(out)
input = self.downsample(input)
return result
class OmniDiscriminator(nn.ModuleDict):
def __init__(self, opts):
super().__init__()
if "p" in opts.tasks:
if opts.dis.p.use_local_discriminator:
self["p"] = nn.ModuleDict(
{
"global": define_D(
input_nc=3,
ndf=opts.dis.p.ndf,
n_layers=opts.dis.p.n_layers,
norm=opts.dis.p.norm,
use_sigmoid=opts.dis.p.use_sigmoid,
get_intermediate_features=opts.dis.p.get_intermediate_features, # noqa: E501
num_D=opts.dis.p.num_D,
),
"local": define_D(
input_nc=3,
ndf=opts.dis.p.ndf,
n_layers=opts.dis.p.n_layers,
norm=opts.dis.p.norm,
use_sigmoid=opts.dis.p.use_sigmoid,
get_intermediate_features=opts.dis.p.get_intermediate_features, # noqa: E501
num_D=opts.dis.p.num_D,
),
}
)
else:
self["p"] = define_D(
input_nc=4, # image + mask
ndf=opts.dis.p.ndf,
n_layers=opts.dis.p.n_layers,
norm=opts.dis.p.norm,
use_sigmoid=opts.dis.p.use_sigmoid,
get_intermediate_features=opts.dis.p.get_intermediate_features,
num_D=opts.dis.p.num_D,
)
if "m" in opts.tasks:
if opts.gen.m.use_advent:
if opts.dis.m.architecture == "base":
if opts.dis.m.gan_type == "WGAN_norm":
self["m"] = nn.ModuleDict(
{
"Advent": get_fc_discriminator(
num_classes=2, use_norm=True
)
}
)
else:
self["m"] = nn.ModuleDict(
{
"Advent": get_fc_discriminator(
num_classes=2, use_norm=False
)
}
)
elif opts.dis.m.architecture == "OmniDiscriminator":
self["m"] = nn.ModuleDict(
{
"Advent": define_D(
input_nc=2,
ndf=opts.dis.m.ndf,
n_layers=opts.dis.m.n_layers,
norm=opts.dis.m.norm,
use_sigmoid=opts.dis.m.use_sigmoid,
get_intermediate_features=opts.dis.m.get_intermediate_features, # noqa: E501
num_D=opts.dis.m.num_D,
)
}
)
else:
raise Exception("This Discriminator is currently not supported!")
if "s" in opts.tasks:
if opts.gen.s.use_advent:
if opts.dis.s.gan_type == "WGAN_norm":
self["s"] = nn.ModuleDict(
{"Advent": get_fc_discriminator(num_classes=11, use_norm=True)}
)
else:
self["s"] = nn.ModuleDict(
{"Advent": get_fc_discriminator(num_classes=11, use_norm=False)}
)
def get_fc_discriminator(num_classes=2, ndf=64, use_norm=False):
if use_norm:
return torch.nn.Sequential(
SpectralNorm(
torch.nn.Conv2d(num_classes, ndf, kernel_size=4, stride=2, padding=1)
),
torch.nn.LeakyReLU(negative_slope=0.2, inplace=True),
SpectralNorm(
torch.nn.Conv2d(ndf, ndf * 2, kernel_size=4, stride=2, padding=1)
),
torch.nn.LeakyReLU(negative_slope=0.2, inplace=True),
SpectralNorm(
torch.nn.Conv2d(ndf * 2, ndf * 4, kernel_size=4, stride=2, padding=1)
),
torch.nn.LeakyReLU(negative_slope=0.2, inplace=True),
SpectralNorm(
torch.nn.Conv2d(ndf * 4, ndf * 8, kernel_size=4, stride=2, padding=1)
),
torch.nn.LeakyReLU(negative_slope=0.2, inplace=True),
SpectralNorm(
torch.nn.Conv2d(ndf * 8, 1, kernel_size=4, stride=2, padding=1)
),
)
else:
return torch.nn.Sequential(
torch.nn.Conv2d(num_classes, ndf, kernel_size=4, stride=2, padding=1),
torch.nn.LeakyReLU(negative_slope=0.2, inplace=True),
torch.nn.Conv2d(ndf, ndf * 2, kernel_size=4, stride=2, padding=1),
torch.nn.LeakyReLU(negative_slope=0.2, inplace=True),
torch.nn.Conv2d(ndf * 2, ndf * 4, kernel_size=4, stride=2, padding=1),
torch.nn.LeakyReLU(negative_slope=0.2, inplace=True),
torch.nn.Conv2d(ndf * 4, ndf * 8, kernel_size=4, stride=2, padding=1),
torch.nn.LeakyReLU(negative_slope=0.2, inplace=True),
torch.nn.Conv2d(ndf * 8, 1, kernel_size=4, stride=2, padding=1),
)
|