File size: 12,251 Bytes
c968fc3 |
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 |
# Copyright (c) 2023 Amphion.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import torch
import torch.nn as nn
import torch.nn.functional as F
from modules.distributions.distributions import DiagonalGaussianDistribution
def nonlinearity(x):
# swish
return x * torch.sigmoid(x)
def Normalize(in_channels):
return torch.nn.GroupNorm(
num_groups=32, num_channels=in_channels, eps=1e-6, affine=True
)
class Upsample2d(nn.Module):
def __init__(self, in_channels, with_conv):
super().__init__()
self.with_conv = with_conv
if self.with_conv:
self.conv = torch.nn.Conv2d(
in_channels, in_channels, kernel_size=3, stride=1, padding=1
)
def forward(self, x):
x = torch.nn.functional.interpolate(x, scale_factor=2.0, mode="nearest")
if self.with_conv:
x = self.conv(x)
return x
class Upsample1d(Upsample2d):
def __init__(self, in_channels, with_conv):
super().__init__(in_channels, with_conv)
if self.with_conv:
self.conv = torch.nn.Conv1d(
in_channels, in_channels, kernel_size=3, stride=1, padding=1
)
class Downsample2d(nn.Module):
def __init__(self, in_channels, with_conv):
super().__init__()
self.with_conv = with_conv
if self.with_conv:
# no asymmetric padding in torch conv, must do it ourselves
self.conv = torch.nn.Conv2d(
in_channels, in_channels, kernel_size=3, stride=2, padding=0
)
self.pad = (0, 1, 0, 1)
else:
self.avg_pool = nn.AvgPool2d(kernel_size=2, stride=2)
def forward(self, x):
if self.with_conv: # bp: check self.avgpool and self.pad
x = torch.nn.functional.pad(x, self.pad, mode="constant", value=0)
x = self.conv(x)
else:
x = self.avg_pool(x)
return x
class Downsample1d(Downsample2d):
def __init__(self, in_channels, with_conv):
super().__init__(in_channels, with_conv)
if self.with_conv:
# no asymmetric padding in torch conv, must do it ourselves
# TODO: can we replace it just with conv2d with padding 1?
self.conv = torch.nn.Conv1d(
in_channels, in_channels, kernel_size=3, stride=2, padding=0
)
self.pad = (1, 1)
else:
self.avg_pool = nn.AvgPool1d(kernel_size=2, stride=2)
class ResnetBlock(nn.Module):
def __init__(self, *, in_channels, out_channels=None, conv_shortcut=False, dropout):
super().__init__()
self.in_channels = in_channels
out_channels = in_channels if out_channels is None else out_channels
self.out_channels = out_channels
self.use_conv_shortcut = conv_shortcut
self.norm1 = Normalize(in_channels)
self.conv1 = torch.nn.Conv2d(
in_channels, out_channels, kernel_size=3, stride=1, padding=1
)
self.norm2 = Normalize(out_channels)
self.dropout = torch.nn.Dropout(dropout)
self.conv2 = torch.nn.Conv2d(
out_channels, out_channels, kernel_size=3, stride=1, padding=1
)
if self.in_channels != self.out_channels:
if self.use_conv_shortcut:
self.conv_shortcut = torch.nn.Conv2d(
in_channels, out_channels, kernel_size=3, stride=1, padding=1
)
else:
self.nin_shortcut = torch.nn.Conv2d(
in_channels, out_channels, kernel_size=1, stride=1, padding=0
)
def forward(self, x):
h = x
h = self.norm1(h)
h = nonlinearity(h)
h = self.conv1(h)
h = self.norm2(h)
h = nonlinearity(h)
h = self.dropout(h)
h = self.conv2(h)
if self.in_channels != self.out_channels:
if self.use_conv_shortcut:
x = self.conv_shortcut(x)
else:
x = self.nin_shortcut(x)
return x + h
class ResnetBlock1d(ResnetBlock):
def __init__(
self,
*,
in_channels,
out_channels=None,
conv_shortcut=False,
dropout,
temb_channels=512
):
super().__init__(
in_channels=in_channels,
out_channels=out_channels,
conv_shortcut=conv_shortcut,
dropout=dropout,
)
self.conv1 = torch.nn.Conv1d(
in_channels, out_channels, kernel_size=3, stride=1, padding=1
)
self.conv2 = torch.nn.Conv1d(
out_channels, out_channels, kernel_size=3, stride=1, padding=1
)
if self.in_channels != self.out_channels:
if self.use_conv_shortcut:
self.conv_shortcut = torch.nn.Conv1d(
in_channels, out_channels, kernel_size=3, stride=1, padding=1
)
else:
self.nin_shortcut = torch.nn.Conv1d(
in_channels, out_channels, kernel_size=1, stride=1, padding=0
)
class Encoder2d(nn.Module):
def __init__(
self,
*,
ch,
ch_mult=(1, 2, 4, 8),
num_res_blocks,
dropout=0.0,
resamp_with_conv=True,
in_channels,
z_channels,
double_z=True,
**ignore_kwargs
):
super().__init__()
self.ch = ch
self.num_resolutions = len(ch_mult)
self.num_res_blocks = num_res_blocks
self.in_channels = in_channels
# downsampling
self.conv_in = torch.nn.Conv2d(
in_channels, self.ch, kernel_size=3, stride=1, padding=1
)
in_ch_mult = (1,) + tuple(ch_mult)
self.down = nn.ModuleList()
for i_level in range(self.num_resolutions):
block = nn.ModuleList()
block_in = ch * in_ch_mult[i_level]
block_out = ch * ch_mult[i_level]
for i_block in range(self.num_res_blocks):
block.append(
ResnetBlock(
in_channels=block_in, out_channels=block_out, dropout=dropout
)
)
block_in = block_out
down = nn.Module()
down.block = block
if i_level != self.num_resolutions - 1:
down.downsample = Downsample2d(block_in, resamp_with_conv)
self.down.append(down)
# middle
self.mid = nn.Module()
self.mid.block_1 = ResnetBlock(
in_channels=block_in, out_channels=block_in, dropout=dropout
)
self.mid.block_2 = ResnetBlock(
in_channels=block_in, out_channels=block_in, dropout=dropout
)
# end
self.norm_out = Normalize(block_in)
self.conv_out = torch.nn.Conv2d(
block_in,
2 * z_channels if double_z else z_channels,
kernel_size=3,
stride=1,
padding=1,
)
def forward(self, x):
# downsampling
hs = [self.conv_in(x)]
for i_level in range(self.num_resolutions):
for i_block in range(self.num_res_blocks):
h = self.down[i_level].block[i_block](hs[-1])
hs.append(h)
if i_level != self.num_resolutions - 1:
hs.append(self.down[i_level].downsample(hs[-1]))
# middle
h = hs[-1]
h = self.mid.block_1(h)
h = self.mid.block_2(h)
# end
h = self.norm_out(h)
h = nonlinearity(h)
h = self.conv_out(h)
return h
# TODO: Encoder1d
class Encoder1d(Encoder2d): ...
class Decoder2d(nn.Module):
def __init__(
self,
*,
ch,
out_ch,
ch_mult=(1, 2, 4, 8),
num_res_blocks,
dropout=0.0,
resamp_with_conv=True,
in_channels,
z_channels,
give_pre_end=False,
**ignorekwargs
):
super().__init__()
self.ch = ch
self.num_resolutions = len(ch_mult)
self.num_res_blocks = num_res_blocks
self.in_channels = in_channels
self.give_pre_end = give_pre_end
# compute in_ch_mult, block_in and curr_res at lowest res
in_ch_mult = (1,) + tuple(ch_mult)
block_in = ch * ch_mult[self.num_resolutions - 1]
# self.z_shape = (1,z_channels,curr_res,curr_res)
# print("Working with z of shape {} = {} dimensions.".format(
# self.z_shape, np.prod(self.z_shape)))
# z to block_in
self.conv_in = torch.nn.Conv2d(
z_channels, block_in, kernel_size=3, stride=1, padding=1
)
# middle
self.mid = nn.Module()
self.mid.block_1 = ResnetBlock(
in_channels=block_in, out_channels=block_in, dropout=dropout
)
self.mid.block_2 = ResnetBlock(
in_channels=block_in, out_channels=block_in, dropout=dropout
)
# upsampling
self.up = nn.ModuleList()
for i_level in reversed(range(self.num_resolutions)):
block = nn.ModuleList()
attn = nn.ModuleList()
block_out = ch * ch_mult[i_level]
for i_block in range(self.num_res_blocks + 1):
block.append(
ResnetBlock(
in_channels=block_in, out_channels=block_out, dropout=dropout
)
)
block_in = block_out
up = nn.Module()
up.block = block
up.attn = attn
if i_level != 0:
up.upsample = Upsample2d(block_in, resamp_with_conv)
self.up.insert(0, up) # prepend to get consistent order
# end
self.norm_out = Normalize(block_in)
self.conv_out = torch.nn.Conv2d(
block_in, out_ch, kernel_size=3, stride=1, padding=1
)
def forward(self, z):
self.last_z_shape = z.shape
# z to block_in
h = self.conv_in(z)
# middle
h = self.mid.block_1(h)
h = self.mid.block_2(h)
# upsampling
for i_level in reversed(range(self.num_resolutions)):
for i_block in range(self.num_res_blocks + 1):
h = self.up[i_level].block[i_block](h)
if i_level != 0:
h = self.up[i_level].upsample(h)
# end
if self.give_pre_end:
return h
h = self.norm_out(h)
h = nonlinearity(h)
h = self.conv_out(h)
return h
# TODO: decoder1d
class Decoder1d(Decoder2d): ...
class AutoencoderKL(nn.Module):
def __init__(self, cfg):
super().__init__()
self.cfg = cfg
self.encoder = Encoder2d(
ch=cfg.ch,
ch_mult=cfg.ch_mult,
num_res_blocks=cfg.num_res_blocks,
in_channels=cfg.in_channels,
z_channels=cfg.z_channels,
double_z=cfg.double_z,
)
self.decoder = Decoder2d(
ch=cfg.ch,
ch_mult=cfg.ch_mult,
num_res_blocks=cfg.num_res_blocks,
out_ch=cfg.out_ch,
z_channels=cfg.z_channels,
in_channels=None,
)
assert self.cfg.double_z
self.quant_conv = torch.nn.Conv2d(2 * cfg.z_channels, 2 * cfg.z_channels, 1)
self.post_quant_conv = torch.nn.Conv2d(cfg.z_channels, cfg.z_channels, 1)
self.embed_dim = cfg.z_channels
def encode(self, x):
h = self.encoder(x)
moments = self.quant_conv(h)
posterior = DiagonalGaussianDistribution(moments)
return posterior
def decode(self, z):
z = self.post_quant_conv(z)
dec = self.decoder(z)
return dec
def forward(self, input, sample_posterior=True):
posterior = self.encode(input)
if sample_posterior:
z = posterior.sample()
else:
z = posterior.mode()
dec = self.decode(z)
return dec, posterior
def get_last_layer(self):
return self.decoder.conv_out.weight
|