File size: 19,658 Bytes
f348660 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 | import torch
def activation_func(activation_fn):
'''
Select activation function
Arg(s):
activation_fn : str
name of activation function
'''
if 'linear' in activation_fn:
return None
elif 'leaky_relu' in activation_fn:
return torch.nn.LeakyReLU(negative_slope=0.20, inplace=True)
elif 'relu' in activation_fn:
return torch.nn.ReLU()
elif 'elu' in activation_fn:
return torch.nn.ELU()
elif 'sigmoid' in activation_fn:
return torch.nn.Sigmoid()
else:
raise ValueError('Unsupported activation function: {}'.format(activation_fn))
'''
Network layers
'''
class Conv2d(torch.nn.Module):
'''
2D convolution class
Arg(s):
in_channels : int
number of input channels
out_channels : int
number of output channels
kernel_size : int
size of kernel
stride : int
stride of convolution
weight_initializer : str
kaiming_normal, kaiming_uniform, xavier_normal, xavier_uniform
activation_func : func
activation function after convolution
use_batch_norm : bool
if set, then applied batch normalization
'''
def __init__(self,
in_channels,
out_channels,
kernel_size=3,
stride=1,
weight_initializer='kaiming_uniform',
activation_func=torch.nn.LeakyReLU(negative_slope=0.10, inplace=True),
use_batch_norm=False):
super(Conv2d, self).__init__()
self.use_batch_norm = use_batch_norm
padding = kernel_size // 2
self.conv = torch.nn.Conv2d(
in_channels,
out_channels,
kernel_size=kernel_size,
stride=stride,
padding=padding,
bias=False)
# Select the type of weight initialization, by default kaiming_uniform
if weight_initializer == 'kaiming_normal':
torch.nn.init.kaiming_normal_(self.conv.weight)
elif weight_initializer == 'xavier_normal':
torch.nn.init.xavier_normal_(self.conv.weight)
elif weight_initializer == 'xavier_uniform':
torch.nn.init.xavier_uniform_(self.conv.weight)
self.activation_func = activation_func
if self.use_batch_norm:
self.batch_norm = torch.nn.BatchNorm2d(out_channels)
def forward(self, x):
conv = self.conv(x)
conv = self.batch_norm(conv) if self.use_batch_norm else conv
if self.activation_func is not None:
return self.activation_func(conv)
else:
return conv
class TransposeConv2d(torch.nn.Module):
'''
Transpose convolution class
Arg(s):
in_channels : int
number of input channels
out_channels : int
number of output channels
kernel_size : int
size of kernel (k x k)
weight_initializer : str
kaiming_normal, kaiming_uniform, xavier_normal, xavier_uniform
activation_func : func
activation function after convolution
use_batch_norm : bool
if set, then applied batch normalization
'''
def __init__(self,
in_channels,
out_channels,
kernel_size=3,
weight_initializer='kaiming_uniform',
activation_func=torch.nn.LeakyReLU(negative_slope=0.10, inplace=True),
use_batch_norm=False):
super(TransposeConv2d, self).__init__()
self.use_batch_norm = use_batch_norm
padding = kernel_size // 2
self.deconv = torch.nn.ConvTranspose2d(
in_channels,
out_channels,
kernel_size=kernel_size,
stride=2,
padding=padding,
output_padding=1,
bias=False)
# Select the type of weight initialization, by default kaiming_uniform
if weight_initializer == 'kaiming_normal':
torch.nn.init.kaiming_normal_(self.conv.weight)
elif weight_initializer == 'xavier_normal':
torch.nn.init.xavier_normal_(self.conv.weight)
elif weight_initializer == 'xavier_uniform':
torch.nn.init.xavier_uniform_(self.conv.weight)
self.activation_func = activation_func
if self.use_batch_norm:
self.batch_norm = torch.nn.BatchNorm2d(out_channels)
def forward(self, x):
deconv = self.deconv(x)
deconv = self.batch_norm(deconv) if self.use_batch_norm else deconv
if self.activation_func is not None:
return self.activation_func(deconv)
else:
return deconv
class UpConv2d(torch.nn.Module):
'''
Up-convolution (upsample + convolution) block class
Arg(s):
in_channels : int
number of input channels
out_channels : int
number of output channels
shape : list[int]
two element tuple of ints (height, width)
kernel_size : int
size of kernel (k x k)
weight_initializer : str
kaiming_normal, kaiming_uniform, xavier_normal, xavier_uniform
activation_func : func
activation function after convolution
use_batch_norm : bool
if set, then applied batch normalization
'''
def __init__(self,
in_channels,
out_channels,
kernel_size=3,
weight_initializer='kaiming_uniform',
activation_func=torch.nn.LeakyReLU(negative_slope=0.10, inplace=True),
use_batch_norm=False):
super(UpConv2d, self).__init__()
self.conv = Conv2d(
in_channels,
out_channels,
kernel_size=kernel_size,
stride=1,
weight_initializer=weight_initializer,
activation_func=activation_func,
use_batch_norm=use_batch_norm)
def forward(self, x, shape):
upsample = torch.nn.functional.interpolate(x, size=shape)
conv = self.conv(upsample)
return conv
class FullyConnected(torch.nn.Module):
'''
Fully connected layer
Arg(s):
in_channels : int
number of input neurons
out_channels : int
number of output neurons
dropout_rate : float
probability to use dropout
'''
def __init__(self,
in_features,
out_features,
weight_initializer='kaiming_uniform',
activation_func=torch.nn.LeakyReLU(negative_slope=0.10, inplace=True),
dropout_rate=0.00):
super(FullyConnected, self).__init__()
self.fully_connected = torch.nn.Linear(in_features, out_features)
if weight_initializer == 'kaiming_normal':
torch.nn.init.kaiming_normal_(self.fully_connected.weight)
elif weight_initializer == 'xavier_normal':
torch.nn.init.xavier_normal_(self.fully_connected.weight)
elif weight_initializer == 'xavier_uniform':
torch.nn.init.xavier_uniform_(self.fully_connected.weight)
self.activation_func = activation_func
if dropout_rate > 0.00 and dropout_rate <= 1.00:
self.dropout = torch.nn.Dropout(p=dropout_rate)
else:
self.dropout = None
def forward(self, x):
fully_connected = self.fully_connected(x)
if self.activation_func is not None:
fully_connected = self.activation_func(fully_connected)
if self.dropout is not None:
return self.dropout(fully_connected)
else:
return fully_connected
'''
Network encoder blocks
'''
class ResNetBlock(torch.nn.Module):
'''
Basic ResNet block class
Arg(s):
in_channels : int
number of input channels
out_channels : int
number of output channels
stride : int
stride of convolution
weight_initializer : str
kaiming_normal, kaiming_uniform, xavier_normal, xavier_uniform
activation_func : func
activation function after convolution
use_batch_norm : bool
if set, then applied batch normalization
'''
def __init__(self,
in_channels,
out_channels,
stride=1,
weight_initializer='kaiming_uniform',
activation_func=torch.nn.LeakyReLU(negative_slope=0.10, inplace=True),
use_batch_norm=False):
super(ResNetBlock, self).__init__()
self.activation_func = activation_func
self.conv1 = Conv2d(
in_channels,
out_channels,
kernel_size=3,
stride=stride,
weight_initializer=weight_initializer,
activation_func=activation_func,
use_batch_norm=use_batch_norm)
self.conv2 = Conv2d(
out_channels,
out_channels,
kernel_size=3,
stride=1,
weight_initializer=weight_initializer,
activation_func=activation_func,
use_batch_norm=use_batch_norm)
self.projection = Conv2d(
in_channels,
out_channels,
kernel_size=1,
stride=stride,
weight_initializer=weight_initializer,
activation_func=None,
use_batch_norm=False)
def forward(self, x):
# Perform 2 convolutions
conv1 = self.conv1(x)
conv2 = self.conv2(conv1)
# Perform projection if (1) shape does not match (2) channels do not match
in_shape = list(x.shape)
out_shape = list(conv2.shape)
if in_shape[2:4] != out_shape[2:4] or in_shape[1] != out_shape[1]:
X = self.projection(x)
else:
X = x
# f(x) + x
return self.activation_func(conv2 + X)
class ResNetBottleneckBlock(torch.nn.Module):
'''
ResNet bottleneck block class
Arg(s):
in_channels : int
number of input channels
out_channels : int
number of output channels
stride : int
stride of convolution
weight_initializer : str
kaiming_normal, kaiming_uniform, xavier_normal, xavier_uniform
activation_func : func
activation function after convolution
use_batch_norm : bool
if set, then applied batch normalization
'''
def __init__(self,
in_channels,
out_channels,
stride=1,
weight_initializer='kaiming_uniform',
activation_func=torch.nn.LeakyReLU(negative_slope=0.10, inplace=True),
use_batch_norm=False):
super(ResNetBottleneckBlock, self).__init__()
self.activation_func = activation_func
self.conv1 = Conv2d(
in_channels,
out_channels,
kernel_size=1,
stride=1,
weight_initializer=weight_initializer,
activation_func=activation_func,
use_batch_norm=use_batch_norm)
self.conv2 = Conv2d(
out_channels,
out_channels,
kernel_size=3,
stride=stride,
weight_initializer=weight_initializer,
activation_func=activation_func,
use_batch_norm=use_batch_norm)
self.conv3 = Conv2d(
out_channels,
4 * out_channels,
kernel_size=1,
stride=1,
weight_initializer=weight_initializer,
activation_func=activation_func,
use_batch_norm=use_batch_norm)
self.projection = Conv2d(
in_channels,
4 * out_channels,
kernel_size=1,
stride=stride,
weight_initializer=weight_initializer,
activation_func=None,
use_batch_norm=False)
def forward(self, x):
# Perform 2 convolutions
conv1 = self.conv1(x)
conv2 = self.conv2(conv1)
conv3 = self.conv3(conv2)
# Perform projection if (1) shape does not match (2) channels do not match
in_shape = list(x.shape)
out_shape = list(conv2.shape)
if in_shape[2:4] != out_shape[2:4] or in_shape[1] != out_shape[1]:
X = self.projection(x)
else:
X = x
# f(x) + x
return self.activation_func(conv3 + X)
class VGGNetBlock(torch.nn.Module):
'''
VGGNet block class
Arg(s):
in_channels : int
number of input channels
out_channels : int
number of output channels
n_conv : int
number of convolution layers
stride : int
stride of convolution
weight_initializer : str
kaiming_normal, kaiming_uniform, xavier_normal, xavier_uniform
activation_func : func
activation function after convolution
use_batch_norm : bool
if set, then applied batch normalization
'''
def __init__(self,
in_channels,
out_channels,
n_conv=1,
stride=1,
weight_initializer='kaiming_uniform',
activation_func=torch.nn.LeakyReLU(negative_slope=0.10, inplace=True),
use_batch_norm=False):
super(VGGNetBlock, self).__init__()
layers = []
for n in range(n_conv - 1):
conv = Conv2d(
in_channels,
out_channels,
kernel_size=3,
stride=1,
weight_initializer=weight_initializer,
activation_func=activation_func,
use_batch_norm=use_batch_norm)
layers.append(conv)
in_channels = out_channels
conv = Conv2d(
in_channels,
out_channels,
kernel_size=3,
stride=stride,
weight_initializer=weight_initializer,
activation_func=activation_func,
use_batch_norm=use_batch_norm)
layers.append(conv)
self.conv_block = torch.nn.Sequential(*layers)
def forward(self, x):
return self.conv_block(x)
'''
Network decoder blocks
'''
class DecoderBlock(torch.nn.Module):
'''
Decoder block with skip connection
Arg(s):
in_channels : int
number of input channels
skip_channels : int
number of skip connection channels
out_channels : int
number of output channels
weight_initializer : str
kaiming_normal, kaiming_uniform, xavier_normal, xavier_uniform
activation_func : func
activation function after convolution
use_batch_norm : bool
if set, then applied batch normalization
deconv_type : str
deconvolution types: transpose, up
'''
def __init__(self,
in_channels,
skip_channels,
out_channels,
weight_initializer='kaiming_uniform',
activation_func=torch.nn.LeakyReLU(negative_slope=0.10, inplace=True),
use_batch_norm=False,
deconv_type='up'):
super(DecoderBlock, self).__init__()
self.skip_channels = skip_channels
self.deconv_type = deconv_type
if deconv_type == 'transpose':
self.deconv = TransposeConv2d(
in_channels,
out_channels,
kernel_size=3,
weight_initializer=weight_initializer,
activation_func=activation_func,
use_batch_norm=use_batch_norm)
elif deconv_type == 'up':
self.deconv = UpConv2d(
in_channels,
out_channels,
kernel_size=3,
weight_initializer=weight_initializer,
activation_func=activation_func,
use_batch_norm=use_batch_norm)
concat_channels = skip_channels + out_channels
self.conv = Conv2d(
concat_channels,
out_channels,
kernel_size=3,
stride=1,
weight_initializer=weight_initializer,
activation_func=activation_func,
use_batch_norm=use_batch_norm)
def forward(self, x, skip=None, shape=None):
'''
Forward input x through a decoder block and fuse with skip connection
Arg(s):
x : torch.Tensor[float32]
N x C x h x w input tensor
skip : torch.Tensor[float32]
N x F x h x w skip connection
shape : tuple[int]
height, width (H, W) tuple denoting output shape
Returns:
torch.Tensor[float32] : N x K x H x W output tensor
'''
if self.deconv_type == 'transpose':
deconv = self.deconv(x)
elif self.deconv_type == 'up':
if skip is not None:
shape = skip.shape[2:4]
elif shape is not None:
pass
else:
n_height, n_width = x.shape[2:4]
shape = (int(2 * n_height), int(2 * n_width))
deconv = self.deconv(x, shape=shape)
if self.skip_channels > 0:
concat = torch.cat([deconv, skip], dim=1)
else:
concat = deconv
return self.conv(concat)
'''
Utility function to pre-process sparse depth and input depth
'''
class OutlierRemoval(object):
'''
Class to perform outlier removal based on depth difference in local neighborhood
Arg(s):
kernel_size : int
local neighborhood to consider
threshold : float
depth difference threshold
'''
def __init__(self, kernel_size=7, threshold=1.5):
self.kernel_size = kernel_size
self.threshold = threshold
def remove_outliers(self, depth):
'''
Removes erroneous measurements from sparse depth
Arg(s):
depth : torch.Tensor[float32]
N x 1 x H x W tensor sparse depth
Returns:
torch.Tensor[float32] : N x 1 x H x W depth
'''
# Get valid locations
validity_map = torch.where(
depth > 0.0,
torch.ones_like(depth),
depth)
# Replace all zeros with large values
max_value = 10 * torch.max(depth)
depth_max_filled = torch.where(
validity_map <= 0,
torch.full_like(depth, fill_value=max_value),
depth)
# For each neighborhood find the smallest value
padding = self.kernel_size // 2
depth_max_filled = torch.nn.functional.pad(
input=depth_max_filled,
pad=(padding, padding, padding, padding),
mode='constant',
value=max_value)
min_values = -torch.nn.functional.max_pool2d(
input=-depth_max_filled,
kernel_size=self.kernel_size,
stride=1,
padding=0)
# If measurement differs a lot from minimum value then remove
validity_map_clean = torch.where(
min_values < depth - self.threshold,
torch.zeros_like(validity_map),
torch.ones_like(validity_map))
# Update depth map
depth_clean = depth * validity_map_clean
return depth_clean
|