File size: 23,580 Bytes
b41dbe3 | 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 | """
EchMo Model Implementation
End-to-End Radar Echo Segmentation Model
"""
import os
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.init as init
import scipy.io as scio
# ==================== Utility Functions ====================
def complex_std(x, epsilon=1e-5):
mu_R = x.real.mean(dim=3, keepdim=True).detach()
mu_I = x.imag.mean(dim=3, keepdim=True).detach()
x_R = x.real - mu_R
x_I = x.imag - mu_I
sigma_R = torch.sqrt(torch.var(x.real, dim=3, keepdim=True, unbiased=False) + epsilon)
sigma_I = torch.sqrt(torch.var(x.imag, dim=3, keepdim=True, unbiased=False) + epsilon)
x_R /= sigma_R
x_I /= sigma_I
return x_R + 1j * x_I
def complex_double2float(x):
x_r = x.real.float()
x_i = x.imag.float()
return x_r + 1j * x_i
def filter_coef_loader(path, coef_type=None):
coef = scio.loadmat(os.path.join(path, 's_tx.mat'))['s_tx']
coef = torch.tensor(coef)
if coef_type == 'ori_mf':
coef = complex_double2float(torch.conj(coef))
zero = torch.complex(torch.zeros([1, 1], dtype=torch.float32), torch.zeros([1, 1], dtype=torch.float32))
coef = torch.cat((zero, coef), dim=1)
return coef
elif coef_type == 'tay_mf':
win = scio.loadmat(os.path.join(path, 'w_taylor.mat'))['w_taylor']
elif coef_type == 'che_mf':
win = scio.loadmat(os.path.join(path, 'w_chebyshev.mat'))['w_chebyshev']
elif coef_type == 'ham_mf':
win = scio.loadmat(os.path.join(path, 'w_hamming.mat'))['w_hamming']
elif coef_type == 'han_mf':
win = scio.loadmat(os.path.join(path, 'w_hanning.mat'))['w_hanning']
elif coef_type == 'black_mf':
win = scio.loadmat(os.path.join(path, 'w_blackman.mat'))['w_blackman']
elif coef_type == 'cos_mf':
win = scio.loadmat(os.path.join(path, 'w_cosine.mat'))['w_cosine']
else:
win = scio.loadmat(os.path.join(path, 'w_taylor.mat'))['w_taylor']
win = torch.permute(torch.tensor(win), [1, 0])
coef = complex_double2float(torch.conj(coef * win))
zero = torch.complex(torch.zeros([1, 1], dtype=torch.float32), torch.zeros([1, 1], dtype=torch.float32))
coef = torch.cat((zero, coef), dim=1)
return coef
# ==================== Model Components ====================
class ComplexBatchNorm2d(nn.Module):
def __init__(self, num_features):
super().__init__()
self.bn_real = nn.BatchNorm2d(num_features)
self.bn_imag = nn.BatchNorm2d(num_features)
def forward(self, x):
return torch.complex(self.bn_real(x.real), self.bn_imag(x.imag))
class ComplexReLU(nn.Module):
def __init__(self, activation='leaky_relu'):
super(ComplexReLU, self).__init__()
self.activation = activation
def forward(self, input):
if self.activation == 'relu':
return nn.ReLU()(input.real) + 1j * nn.ReLU()(input.imag)
elif self.activation == 'leaky_relu':
return F.leaky_relu(input.real) + 1j * F.leaky_relu(input.imag)
elif self.activation == 'celu':
return F.celu(input.real) + 1j * F.celu(input.imag)
else:
return input
class DoubleConvBlock(nn.Module):
def __init__(self, in_ch, out_ch, k_size, pad, dil):
super().__init__()
self.block = nn.Sequential(
nn.Conv2d(in_ch, out_ch, kernel_size=k_size, padding=pad, dilation=dil),
nn.BatchNorm2d(out_ch),
nn.LeakyReLU(inplace=True),
nn.Conv2d(out_ch, out_ch, kernel_size=k_size, padding=pad, dilation=dil),
nn.BatchNorm2d(out_ch),
nn.LeakyReLU(inplace=True)
)
def forward(self, x):
return self.block(x)
class Double3DConvBlock(nn.Module):
def __init__(self, in_ch, out_ch, k_size, pad, dil):
super().__init__()
self.block = nn.Sequential(
nn.Conv3d(in_ch, out_ch, kernel_size=k_size, padding=pad, dilation=dil),
nn.BatchNorm3d(out_ch),
nn.LeakyReLU(inplace=True),
nn.Conv3d(out_ch, out_ch, kernel_size=k_size, padding=pad, dilation=dil),
nn.BatchNorm3d(out_ch),
nn.LeakyReLU(inplace=True)
)
def forward(self, x):
return self.block(x)
class ConvBlock(nn.Module):
def __init__(self, in_ch, out_ch, k_size, pad, dil):
super().__init__()
self.block = nn.Sequential(
nn.Conv2d(in_ch, out_ch, kernel_size=k_size, padding=pad, dilation=dil),
nn.BatchNorm2d(out_ch),
nn.LeakyReLU(inplace=True)
)
def forward(self, x):
return self.block(x)
class PeakConv2D(nn.Module):
def __init__(self, in_ch, out_ch, bias, refer_band, guard_band_dop, guard_band, signal_type):
super(PeakConv2D, self).__init__()
self.signal_type = signal_type
if self.signal_type in ('range_doppler', 'angle_doppler'):
self.guard_band = (guard_band_dop, guard_band_dop)
self.padding = (self.guard_band[1] + refer_band,
self.guard_band[1] + refer_band,
self.guard_band[0] + refer_band,
self.guard_band[0] + refer_band)
else:
self.guard_band = (guard_band, guard_band)
self.padding = (self.guard_band[1] + refer_band,
self.guard_band[1] + refer_band,
self.guard_band[0] + refer_band,
self.guard_band[0] + refer_band)
self.refer_band = refer_band
self.kernel_size = (self.guard_band[0] + self.guard_band[1] + self.refer_band * 2), 4
self.stride = self.kernel_size
self.zero_padding = nn.ZeroPad2d(self.padding)
self.bias = bias
self.pc_strd = 1
self.peak_conv = nn.Conv2d(in_ch, out_ch,
kernel_size=self.kernel_size,
padding=0,
stride=self.stride,
bias=self.bias)
def forward(self, x):
h, w = x.shape[2], x.shape[3]
rb = self.refer_band
gb = self.guard_band
k_pk = self.kernel_size
N = k_pk[0] * k_pk[1]
dtype = x.data.type()
b = x.size(0)
x = self.zero_padding(x)
p_r = self._get_p_r(b, h, w, N, dtype)
x_r = self._sample_x(x, p_r, N)
p_c = self._get_p_c(b, h, w, N, dtype)
x_c = self._sample_x(x, p_c, N)
x_prf = x_c - x_r
x_prf = self._reshape_x_prf(k_pk[0], k_pk[1], x_prf)
out = self.peak_conv(x_prf)
return out
def _get_p_c(self, b, h, w, N, dtype):
p_c_x, p_c_y = torch.meshgrid(
torch.arange(self.padding[2], h * self.pc_strd + self.padding[2], self.pc_strd),
torch.arange(self.padding[0], w * self.pc_strd + self.padding[0], self.pc_strd), indexing='ij')
p_c_x = torch.flatten(p_c_x).view(1, 1, h, w).repeat(1, N, 1, 1)
p_c_y = torch.flatten(p_c_y).view(1, 1, h, w).repeat(1, N, 1, 1)
p_c = torch.cat([p_c_x, p_c_y], 1).type(dtype)
p_c = p_c.repeat(b, 1, 1, 1)
return p_c
def _gen_prf_grid(self, rb, gb, N, dtype):
h_t = -(rb + gb[0])
h_d = rb + gb[0]
w_l = -(rb + gb[1])
w_r = rb + gb[1]
w_prf = (rb + gb[1]) * 2 + 1
h_prf = (rb + gb[0]) * 2 + 1
prf_x_idx, prf_y_idx = torch.meshgrid(
torch.arange(h_t, h_d + 1),
torch.arange(w_l, w_r + 1), indexing='ij')
prf_xt = prf_x_idx[0:rb]
prf_xr = prf_x_idx[rb:(h_prf - 2 * rb + 1), (w_prf - rb):w_prf]
prf_xd = prf_x_idx[(h_prf - rb):h_prf]
prf_xl = prf_x_idx[rb:(h_prf - 2 * rb + 1), 0:rb]
prf_x = torch.cat([torch.flatten(prf_xt),
torch.flatten(prf_xr),
torch.flatten(prf_xd),
torch.flatten(prf_xl)], 0)
prf_yt = prf_y_idx[0:rb]
prf_yr = prf_y_idx[rb:(h_prf - 2 * rb + 1), (w_prf - rb):w_prf]
prf_yd = prf_y_idx[(h_prf - rb):h_prf]
prf_yl = prf_y_idx[rb:(h_prf - 2 * rb + 1), 0:rb]
prf_y = torch.cat([torch.flatten(prf_yt),
torch.flatten(prf_yr),
torch.flatten(prf_yd),
torch.flatten(prf_yl)], 0)
prf = torch.cat([prf_x, prf_y], 0)
prf = prf.view(1, 2 * N, 1, 1).type(dtype)
return prf
def _get_p_r(self, b, h, w, N, dtype):
rb = self.refer_band
gb = self.guard_band
prf = self._gen_prf_grid(rb, gb, N, dtype)
p_c = self._get_p_c(b, h, w, N, dtype)
p_r = p_c + prf
return p_r
def _sample_x(self, x_pad, p, N):
p = p.contiguous().permute(0, 2, 3, 1).floor()
b, h, w, _ = p.size()
h_pad = x_pad.size(2)
w_pad = x_pad.size(3)
c = x_pad.size(1)
x_pad = x_pad.contiguous().view(b, c, -1)
index = p[..., :N] * w_pad + p[..., N:]
index = index.contiguous().unsqueeze(dim=1).expand(-1, c, -1, -1, -1).contiguous().view(b, c, -1)
index = index.long().to(x_pad.device)
x_r = x_pad.gather(dim=-1, index=index).contiguous().view(b, c, h, w, N)
return x_r
@staticmethod
def _reshape_x_prf(k_h, k_w, x_prf):
b, c, h, w, N = x_prf.size()
x_prf = torch.cat([x_prf[..., s:s + k_w].contiguous().view(b, c, h, w * k_w) for s in range(0, N, k_w)], dim=-1)
x_prf = x_prf.contiguous().view(b, c, h * k_h, w * k_w)
return x_prf
class DoublePeakConv2D(nn.Module):
def __init__(self, in_ch, out_ch, bias, rb, gb_dop, gb, sig):
super(DoublePeakConv2D, self).__init__()
self.bias = bias
self.refer_band = rb
self.guard_band_dop = gb_dop
self.guard_band = gb
self.signal_type = sig
self.pk_conv1 = PeakConv2D(in_ch, out_ch, bias=self.bias,
refer_band=self.refer_band,
guard_band_dop=self.guard_band_dop,
guard_band=self.guard_band,
signal_type=self.signal_type)
self.bn1 = nn.BatchNorm2d(out_ch)
self.act1 = nn.LeakyReLU(inplace=True)
self.pk_conv2 = PeakConv2D(out_ch, out_ch, bias=self.bias,
refer_band=self.refer_band,
guard_band_dop=self.guard_band_dop,
guard_band=self.guard_band,
signal_type=self.signal_type)
self.bn2 = nn.BatchNorm2d(out_ch)
self.act2 = nn.LeakyReLU(inplace=True)
def forward(self, x):
x = self.pk_conv1(x)
x = self.bn1(x)
x = self.act1(x)
x = self.pk_conv2(x)
x = self.bn2(x)
x = self.act2(x)
return x
class ASPPBlock(nn.Module):
def __init__(self, in_ch, out_ch, shape):
super().__init__()
self.shape = shape
self.global_avg_pool = nn.AvgPool2d(self.shape)
self.conv1_1x1 = nn.Conv2d(in_ch, out_ch, kernel_size=1, padding=0, dilation=1)
self.single_conv_block1_1x1 = ConvBlock(in_ch, out_ch, k_size=1, pad=0, dil=1)
self.single_conv_block1_3x3 = ConvBlock(in_ch, out_ch, k_size=3, pad=6, dil=6)
self.single_conv_block2_3x3 = ConvBlock(in_ch, out_ch, k_size=3, pad=12, dil=12)
self.single_conv_block3_3x3 = ConvBlock(in_ch, out_ch, k_size=3, pad=18, dil=18)
def forward(self, x):
x1 = F.interpolate(self.global_avg_pool(x), size=self.shape, align_corners=False, mode='bilinear')
x1 = self.conv1_1x1(x1)
x2 = self.single_conv_block1_1x1(x)
x3 = self.single_conv_block1_3x3(x)
x4 = self.single_conv_block2_3x3(x)
x5 = self.single_conv_block3_3x3(x)
x_cat = torch.cat((x2, x3, x4, x5, x1), 1)
return x_cat
class EncodingBranch(nn.Module):
def __init__(self, signal_type, n_frames, in_ch=1):
super().__init__()
self.signal_type = signal_type
self.n_frames = n_frames
self.in_ch = in_ch
self.double_3dconv_block1 = Double3DConvBlock(in_ch=self.in_ch, out_ch=32,
k_size=(1+n_frames//2, 3, 3),
pad=(0, 1, 1), dil=1)
self.doppler_max_pool = nn.MaxPool2d(2, stride=(1, 2))
self.max_pool = nn.MaxPool2d(2, stride=2)
self.double_pkc_block = DoublePeakConv2D(in_ch=32, out_ch=64,
bias=True, rb=1, gb_dop=1, gb=1, sig=self.signal_type)
self.single_conv_block1_1x1 = ConvBlock(in_ch=64, out_ch=128, k_size=1, pad=0, dil=1)
def forward(self, x):
x1 = self.double_3dconv_block1(x)
x1 = torch.squeeze(x1, 2)
x1_pad = F.pad(x1, (0, 0, 0, 0), "constant", 0)
x1_down = self.max_pool(x1_pad)
x2 = self.double_pkc_block(x1_down)
x2_pad = F.pad(x2, (0, 0, 0, 1), "constant", 0)
x2_down = self.doppler_max_pool(x2_pad)
x3 = self.single_conv_block1_1x1(x2_down)
return x2_down, x3
class PKCIn4ANY(nn.Module):
def __init__(self, n_classes, n_frames, in_ch, w, h):
super().__init__()
self.n_classes = n_classes
self.n_frames = n_frames
self.in_ch = in_ch
self.rd_shape = (int(w//2), int(h//4))
self.rd_encoding_branch = EncodingBranch('range_doppler', self.n_frames, self.in_ch)
self.rd_aspp_block = ASPPBlock(in_ch=64, out_ch=128, shape=self.rd_shape)
self.rd_single_conv_block1_1x1 = ConvBlock(in_ch=640, out_ch=128, k_size=1, pad=0, dil=1)
self.rd_upconv1 = nn.ConvTranspose2d(256, 128, (2, 2), stride=(2, 2))
self.rd_double_conv_block1 = DoubleConvBlock(in_ch=128, out_ch=128, k_size=3, pad=1, dil=1)
self.rd_upconv2 = nn.ConvTranspose2d(128, 128, (1, 2), stride=(1, 2))
self.rd_double_conv_block2 = DoubleConvBlock(in_ch=128, out_ch=128, k_size=3, pad=1, dil=1)
self.rd_final = nn.Conv2d(in_channels=128, out_channels=n_classes, kernel_size=1)
def forward(self, x_rd):
rd_features, rd_latent = self.rd_encoding_branch(x_rd)
x2_rd = self.rd_aspp_block(rd_features)
x2_rd = self.rd_single_conv_block1_1x1(x2_rd)
x4_rd = torch.cat((x2_rd, rd_latent), 1)
x4_rd = self.rd_upconv1(x4_rd)
x4_rd = self.rd_double_conv_block1(x4_rd)
x4_rd = self.rd_upconv2(x4_rd)
x4_rd = self.rd_double_conv_block2(x4_rd)
x4_rd = self.rd_final(x4_rd)
return x4_rd
def _init_translator_weights(m):
if isinstance(m, nn.Linear):
torch.nn.init.xavier_uniform_(m.weight)
nn.init.constant_(m.bias, 0.)
elif isinstance(m, nn.Conv2d):
torch.nn.init.xavier_uniform_(m.weight)
if m.bias is not None:
nn.init.constant_(m.bias, 0.)
elif isinstance(m, nn.BatchNorm2d):
nn.init.uniform_(m.weight, 0., 1.)
nn.init.constant_(m.bias, 0.)
class Leanable_Match_FilterV5(nn.Module):
def __init__(self, k_size, in_ch, out_ch, lmf_coef, activation='relu', bias=True):
super(Leanable_Match_FilterV5, self).__init__()
self.k_size = k_size
self.stride = 1
self.in_ch = in_ch
self.out_ch = out_ch
self.activation = activation
self.bias = bias
self.lmf_real = nn.Conv2d(self.in_ch, self.out_ch,
kernel_size=(1, k_size), stride=(1, 1),
bias=self.bias)
self.lmf_imag = nn.Conv2d(self.in_ch, self.out_ch,
kernel_size=(1, k_size), stride=(1, 1),
bias=self.bias)
init.kaiming_uniform_(self.lmf_real.weight)
init.kaiming_uniform_(self.lmf_imag.weight)
if self.bias:
init.zeros_(self.lmf_real.bias)
init.zeros_(self.lmf_imag.bias)
self.cause_padding = torch.nn.ZeroPad2d(padding=(0, self.k_size - 1, 0, 0))
self.cmplx_relu = ComplexReLU(self.activation)
def forward(self, x):
inputR = self.cause_padding(x.real)
inputI = self.cause_padding(x.imag)
x_RR = self.lmf_real(inputR)
x_II = self.lmf_imag(inputI)
x_RI = self.lmf_imag(inputR)
x_IR = self.lmf_real(inputI)
x = x_RR - x_II + 1j * (x_RI + x_IR)
x = self.cmplx_relu(x)
return x
class LMF_Chain(nn.Module):
def __init__(self, configs):
super(LMF_Chain, self).__init__()
self.lmf_coef = filter_coef_loader(os.path.join(configs.dataset_path, configs.coef_path), configs.lmf_coefs)
self.in_ch = 1
self.Chain = nn.Sequential()
for l in range(configs.lmf_layers):
self.Chain.add_module('lmfV5-{0}'.format(l),
Leanable_Match_FilterV5(k_size=self.lmf_coef.shape[1],
in_ch=self.in_ch,
out_ch=configs.lmf_channels,
lmf_coef=self.lmf_coef,
activation=configs.activation,
bias=configs.bias))
self.in_ch = configs.lmf_channels
def forward(self, x):
x = self.Chain(x)
return x
class Multi_Order_LDC(nn.Module):
def __init__(self, in_ch, out_ch, orders, activation='relu', bias=True):
super(Multi_Order_LDC, self).__init__()
self.in_ch = in_ch
self.out_ch = out_ch
self.orders = orders
self.activation = activation
self.bias = bias
self.ldc_list = nn.ModuleList()
self.pad_list = nn.ModuleList()
for n in range(self.orders):
ldc = nn.Conv2d(self.in_ch, self.out_ch, kernel_size=(n+2, 1), bias=self.bias)
pad = torch.nn.ZeroPad2d(padding=(0, 0, n+1, 0))
self.in_ch = self.out_ch
self.ldc_list.append(ldc)
self.pad_list.append(pad)
self.cmplx_relu = ComplexReLU(self.activation)
self._initialize_MLDC_weights()
def _initialize_MLDC_weights(self):
for n, conv in enumerate(self.ldc_list):
nn.init.kaiming_normal_(conv.weight)
if self.bias:
nn.init.zeros_(conv.bias)
def forward(self, x):
B, P, R = x.shape[0], x.shape[2], x.shape[3]
x = x.unsqueeze(dim=1).broadcast_to(B, self.orders, x.shape[1], P, R)
y_r = torch.zeros(B, self.orders, self.out_ch, P, R).to(x.device)
y_i = torch.zeros(B, self.orders, self.out_ch, P, R).to(x.device)
for n in range(self.orders):
x_n_real = self.ldc_list[n](self.pad_list[n](x[:,n,...].real))
x_n_imag = self.ldc_list[n](self.pad_list[n](x[:,n,...].imag))
y_r[:,n,...] = x_n_real
y_i[:,n,...] = x_n_imag
y_r = y_r.view(B, self.orders*self.out_ch, P, R)
y_i = y_i.view(B, self.orders*self.out_ch, P, R)
y = torch.complex(y_r, y_i)
y = self.cmplx_relu(y)
return y
class Group_Purturble_DF(nn.Module):
def __init__(self, in_ch, out_ch, k_size, groups, activation='relu', bias=True):
super(Group_Purturble_DF, self).__init__()
self.k_size = k_size
self.in_ch = in_ch
self.out_ch = out_ch
self.groups = groups
self.activation = activation
self.bias = bias
self.gdf_cos = nn.Conv2d(self.in_ch*self.groups, self.out_ch*self.groups,
kernel_size=(k_size, 1), stride=(k_size, 1),
padding=0, groups=self.groups, bias=self.bias)
self.gdf_sin = nn.Conv2d(self.in_ch*self.groups, self.out_ch*self.groups,
kernel_size=(k_size, 1), stride=(k_size, 1),
padding=0, groups=self.groups, bias=self.bias)
self._initialize_GDF_weights()
self.cmplx_relu = ComplexReLU(self.activation)
def _initialize_GDF_weights(self):
init.kaiming_uniform_(self.gdf_cos.weight)
init.kaiming_uniform_(self.gdf_sin.weight)
if self.bias:
init.zeros_(self.gdf_cos.bias)
init.zeros_(self.gdf_sin.bias)
def forward(self, x):
B, R = x.shape[0], x.shape[3]
x_real, x_imag = x.real.to(x.device), x.imag.to(x.device)
x_rr = self.gdf_cos(x_real)
x_ii = self.gdf_sin(x_imag)
x_ri = self.gdf_sin(x_real)
x_ir = self.gdf_cos(x_imag)
x = x_rr + x_ii - 1j * x_ri + 1j * x_ir
x = self.cmplx_relu(x)
x = x.view(B, self.groups, self.out_ch, 1, R).squeeze(dim=3)
return x
class EchMo(nn.Module):
def __init__(self, configs):
super(EchMo, self).__init__()
self.cfg = configs
self.task_name = configs.task_name
self.n_range = configs.n_range
self.n_pulse = configs.n_pulse
self.n_classes = configs.n_classes
self.n_frames = configs.n_frames
self.scale = configs.scale
self.activation = configs.activation
self.bias = configs.bias
self.lmf_chains = configs.lmf_chains
self.lmf_channels = configs.lmf_channels
self.lmf_layers = configs.lmf_layers
self.lmf_coefs = configs.lmf_coefs
self.ldc_orders = configs.ldc_orders
self.ldc_channels = configs.ldc_channels
self.df_groups = configs.ldc_orders
self.df_channels = configs.df_channels
self.LMFChain = nn.ModuleList([LMF_Chain(configs=self.cfg) for _ in range(self.lmf_chains)])
self.MLDC = Multi_Order_LDC(in_ch=self.lmf_channels*self.lmf_chains,
out_ch=self.ldc_channels,
orders=self.ldc_orders,
activation=self.activation,
bias=self.bias)
self.GDF = Group_Purturble_DF(in_ch=self.ldc_channels,
out_ch=self.df_channels,
k_size=self.n_pulse,
groups=self.df_groups,
activation=self.activation,
bias=self.bias)
self.RDTranslator = PKCIn4ANY(n_classes=self.n_classes,
n_frames=self.n_frames,
in_ch=self.df_groups,
w=self.df_channels,
h=self.n_range)
self.RDTranslator.apply(_init_translator_weights)
def forward(self, x):
if self.scale == 'instance':
x = complex_double2float(complex_std(x, epsilon=1e-8))
elif self.scale == 'global':
x = complex_double2float(x)
for i in range(self.lmf_chains):
if i == 0:
x_k = self.LMFChain[i](x)
else:
x_ki = self.LMFChain[i](x)
x_k = torch.concatenate([x_k, x_ki], dim=1)
x_h = self.MLDC(x_k)
x_d = self.GDF(x_h)
gdf_out = x_d
x_d = torch.abs(x_d).unsqueeze(dim=2)
y = self.RDTranslator(x_d)
return y, x_k, x_h, gdf_out
|