Spaces:
Running
Running
File size: 15,468 Bytes
e8160b2 | 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 | import torch
import torch.nn as nn
import torch.nn.functional as F
from .complexmodule import ComplexConv1D, ComplexConv2d, ComplexBatchNorm, ComplexTranspose1D, cPReLU, ComplexConvTranspose2d
class ComplexLinearLayer(nn.Module):
"""
A 1x1 Convolution Layer, which can be used to efficiently increase/decrease Feature Maps or, in the context of
the U-Net architecture, generate a linear projection of the feature maps learned in earlier layers ("channel-wise
pooling").
"""
def __init__(self, in_chan, out_chan):
super(ComplexLinearLayer, self).__init__()
self.conv = ComplexConv2d(in_channels=in_chan,
out_channels=out_chan,
kernel_size=(1, 1),
stride=(1, 1))
self.bn = ComplexBatchNorm(out_chan)
self.act = cPReLU()
def forward(self, x):
return self.act(self.bn(self.conv(x)))
class ComplexDConvBlock(nn.Module):
def __init__(self, in_chan, out_chan, kernel_size=3, stride=1, dilation=2):
super(ComplexDConvBlock, self).__init__()
dconv_pad = (dilation * (kernel_size - 1)) // 2
self.conv1 = ComplexConv2d(in_channels=in_chan,
out_channels=out_chan,
kernel_size=(kernel_size, kernel_size),
stride=(stride, stride),
padding=(dconv_pad, dconv_pad),
dilation=dilation, complex_axis=1)
self.act = cPReLU()
self.bn1 = ComplexBatchNorm(out_chan)
self.drop1 = nn.Dropout(0.2)
def forward(self, x, dropout=False):
x = self.act(self.bn1(self.conv1(x)))
if dropout:
x = self.drop1(x)
return x
class ComplexConv1x1Block(nn.Module):
""" Inspired by TasNet Temporal Block - not a 1x1 block, TODO rename across whole project """
def __init__(self, in_chan, out_chan, kernel_size, dilation):
super(ComplexConv1x1Block, self).__init__()
# Start with linear projection
self.conv1x1 = ComplexLinearLayer(in_chan, out_chan)
dconv_pad = (dilation * (kernel_size - 1)) // 2 # dont divide by 2 for a causal system
# Follow up by depthwise, dilated conv
self.dconv = ComplexConv2d(in_channels=out_chan, # before it was out everywhere
out_channels=out_chan,
kernel_size=(kernel_size, kernel_size),
groups=in_chan,
padding=(dconv_pad, dconv_pad),
dilation=dilation)
self.prelu = cPReLU()
self.bn = ComplexBatchNorm(out_chan)
# 1x1 across channel conv (pointwise conv) in=out, out=in
self.pconv = ComplexConv2d(out_chan, in_chan, (1, 1))
def forward(self, x):
# Generate new features by using separable, dilated conv
y = self.conv1x1(x)
y = self.dconv(y)
y = self.bn(self.prelu(y))
# Map the new features to the same count of feature maps as the input was
y = self.pconv(y)
# Next part is done in tasnet paper but it might not be useful if one doesnt use a very deep module consisting
# of these Blocks
# # Add new features to the previous features, increasing the influence of important features and vice versa
x = x + y
return x
class ComplexConvBlock(nn.Module):
"""
"""
def __init__(self, in_channels, out_channels, kernel_size=(3, 3), stride=(1, 1), padding=(0, 0)):
super(ComplexConvBlock, self).__init__()
self.conv1 = ComplexConv2d(in_channels=in_channels, out_channels=out_channels,
kernel_size=kernel_size, stride=stride, complex_axis=1, padding=padding)
self.conv2 = ComplexConv2d(in_channels=out_channels, out_channels=out_channels,
kernel_size=kernel_size, stride=stride, complex_axis=1, padding=padding)
self.bn1 = ComplexBatchNorm(out_channels)
self.bn2 = ComplexBatchNorm(out_channels)
self.act1 = cPReLU()
self.act2 = cPReLU()
self.drop1 = nn.Dropout(0.2)
def forward(self, x, pool_size=(2, 2), pool_type=None, dropout=False):
'Conv -> BN -> Relu * 2 for Unet'
x = self.act1(self.bn1(self.conv1(x)))
x = self.act2(self.bn2(self.conv2(x)))
if pool_type == 'max':
x = F.max_pool2d(x, kernel_size=pool_size)
elif pool_type == 'avg':
x = F.avg_pool2d(x, kernel_size=pool_size)
if dropout:
x = self.drop1(x)
return x
class ComplexUpsampleUnet(nn.Module):
def __init__(self, in_size, out_size, kz=3,
mode='conv', dilation=1, padding=(0, 0), output_padding=(0, 0), complex_axis=1):
super().__init__()
self.complex_axis = complex_axis
if mode == 'conv':
self.up = ComplexConvTranspose2d(in_size, in_size//2, kernel_size=(kz, kz), stride=(2, 2),
dilation=dilation, padding=padding, output_padding=output_padding, complex_axis=self.complex_axis)
elif mode == 'bilinear':
self.up = nn.Sequential(
nn.Upsample(mode='bilinear', scale_factor=2),
nn.Conv2d(in_size, out_size, kernel_size=1)
)
self.conv1 = ComplexConvBlock(in_size, out_size, padding=(1, 1))
self.conv_noskip = ComplexConvBlock(in_size // 2, out_size, padding=(1, 1))
def _center_crop(self, x1, x2):
diffX = x2.size()[2] - x1.size()[2]
diffY = x2.size()[3] - x1.size()[3]
# print('sizes', x1.size(), x2.size(), diffX // 2, diffX - diffX//2, diffY // 2, diffY - diffY//2)
x1 = F.pad(x1, [diffX // 2, diffX - diffX // 2,
diffY // 2, diffY - diffY // 2])
return x1, x2
def forward(self, x, residual):
# x: complex tensor (B, 2*C, H, W) or equivalent
up = self.up(x)
if residual is None:
out = self.conv_noskip(up, pool_type=None)
else:
up_real, up_imag = torch.chunk(up, 2, dim=self.complex_axis)
res_real, res_imag = torch.chunk(residual, 2, dim=self.complex_axis)
# Padding
# up_real, res_real = self._center_crop(up_real, res_real)
# up_imag, res_imag = self._center_crop(up_imag, res_imag)
real_total = torch.cat([up_real, res_real], self.complex_axis)
imag_total = torch.cat([up_imag, res_imag], self.complex_axis)
out = torch.cat([real_total, imag_total], self.complex_axis)
# out = torch.cat([out, residual], self.complex_axis)
out = self.conv1(out, pool_type=None)
return out
class ComplexUpConstantUNet(nn.Module):
"""
Upsampling without residual/lateral connections.
Doubles dimensions of T and F but keeps the number of channels constant.
"""
def __init__(self, nb_chan, kz=3, dilation=(1, 1), padding=(0, 0), output_padding=(0, 0), complex_axis=1):
"""
:param nb_chan: Int, number of channels of the input and output.
"""
super(ComplexUpConstantUNet, self).__init__()
self.nb_chan = nb_chan
self.complex_axis = complex_axis
# Transposed convolution for upsampling
self.up = ComplexConvTranspose2d(
nb_chan, nb_chan, kernel_size=(kz, kz), stride=(2, 2),
dilation=dilation, padding=padding, output_padding=output_padding, complex_axis=self.complex_axis
)
# Optional convolution to refine channels (keeps nb_chan)
self.out_conv = ComplexConvBlock(nb_chan * 2, nb_chan, padding=padding)
def _center_crop(self, x1, x2):
diffX = x2.size()[2] - x1.size()[2]
diffY = x2.size()[3] - x1.size()[3]
# print('sizes', x1.size(), x2.size(), diffX // 2, diffX - diffX//2, diffY // 2, diffY - diffY//2)
x1 = F.pad(x1, [diffX // 2, diffX - diffX // 2,
diffY // 2, diffY - diffY // 2])
return x1, x2
def forward(self, x, residual):
up = self.up(x) # doubles time/freq dimensions
if residual is None:
return up
else:
up_real, up_imag = torch.chunk(up, 2, dim=self.complex_axis)
res_real, res_imag = torch.chunk(residual, 2, dim=self.complex_axis)
# Padding
# up_real, res_real = self._center_crop(up_real, res_real)
# up_imag, res_imag = self._center_crop(up_imag, res_imag)
real_total = torch.cat([up_real, res_real], self.complex_axis)
imag_total = torch.cat([up_imag, res_imag], self.complex_axis)
out = torch.cat([real_total, imag_total], self.complex_axis)
# Pass through conv block to refine
# out = torch.cat([up, residual], self.complex_axis)
out = self.out_conv(out, pool_type=None)
return out
class ComplexUConvBlock(nn.Module):
def __init__(self, in_chan, out_chan, kernel_size=3, stride=1):
super(ComplexUConvBlock, self).__init__()
self.conv1 = ComplexConv2d(in_channels=in_chan,
out_channels=out_chan,
kernel_size=(kernel_size, kernel_size),
stride=(stride, stride),
padding=(1, 1),
complex_axis=1)
self.act = cPReLU()
self.bn1 = ComplexBatchNorm(out_chan)
self.drop1 = nn.Dropout(0.2)
def forward(self, x, dropout=False):
x = self.act(self.bn1(self.conv1(x)))
if dropout:
x = self.drop1(x)
return x
class CVConvNeXtBlock(nn.Module):
"""ConvNeXt Block adapted from https://github.com/facebookresearch/ConvNeXt to 1D audio signal.
Args:
dim (int): Number of input channels.
intermediate_dim (int): Dimensionality of the intermediate layer.
layer_scale_init_value (float, optional): Initial value for the layer scale. None means no scaling.
Defaults to None.
adanorm_num_embeddings (int, optional): Number of embeddings for AdaLayerNorm.
None means non-conditional LayerNorm. Defaults to None.
"""
def __init__(
self,
dim: int = 512,
intermediate_dim: int = 1536,
layer_scale_init_value: float = 0.125,
complex_axis: int = 1
):
super().__init__()
self.dwconv = ComplexConv1D(in_channels=dim, out_channels=dim, kernel_size=3, padding=1, complex_axis=1)
self.norm = nn.LayerNorm(dim, eps=1e-6)
self.pwconv1 = nn.Linear(dim, intermediate_dim) # pointwise/1x1 convs, implemented with linear layers
self.act = nn.GELU()
self.pwconv2 = nn.Linear(intermediate_dim, dim)
self.gamma = (
nn.Parameter(layer_scale_init_value * torch.ones(dim), requires_grad=True)
if layer_scale_init_value > 0
else None
)
self.complex_axis = complex_axis
def forward(self, x: torch.Tensor, laterial: torch.Tensor = None) -> torch.Tensor:
residual = x
x = self.dwconv(x)
real, imag = torch.chunk(x, 2, dim=self.complex_axis) # Split real and imaginary parts
real = real.transpose(1, 2) # (B, C, T) -> (B, T, C)
imag = imag.transpose(1, 2) # (B, C, T) -> (B, T, C)
real = self.norm(real) # Apply LayerNorm to real part
imag = self.norm(imag) # Apply LayerNorm to imaginary part
real = self.pwconv2(self.act(self.pwconv1(real))) # MLP on real part
imag = self.pwconv2(self.act(self.pwconv1(imag))) # MLP on imaginary part
if self.gamma is not None:
real = self.gamma * real
imag = self.gamma * imag
real = real.transpose(1, 2) # (B, T, C) -> (B, C, T)
imag = imag.transpose(1, 2) # (B, T, C) -> (B, C, T)
x = torch.cat([real, imag], dim=self.complex_axis) # Concatenate real and imaginary parts back together
x = residual + x
return x
class CVConvNeXtDBlock(nn.Module):
"""ConvNeXt Block adapted from https://github.com/facebookresearch/ConvNeXt to 1D audio signal.
Args:
dim (int): Number of input channels.
intermediate_dim (int): Dimensionality of the intermediate layer.
layer_scale_init_value (float, optional): Initial value for the layer scale. None means no scaling.
Defaults to None.
adanorm_num_embeddings (int, optional): Number of embeddings for AdaLayerNorm.
None means non-conditional LayerNorm. Defaults to None.
"""
def __init__(
self,
dim: int = 512,
intermediate_dim: int = 1536,
layer_scale_init_value: float = 0.125,
complex_axis: int = 1
):
super().__init__()
self.dwconv = ComplexTranspose1D(in_channels=dim, out_channels=dim, kernel_size=3, padding=1, complex_axis=1)
self.norm = nn.LayerNorm(dim, eps=1e-6)
self.pwconv1 = nn.Linear(dim, intermediate_dim) # pointwise/1x1 convs, implemented with linear layers
self.act = nn.GELU()
self.pwconv2 = nn.Linear(intermediate_dim, dim)
self.gamma = (
nn.Parameter(layer_scale_init_value * torch.ones(dim), requires_grad=True)
if layer_scale_init_value > 0
else None
)
self.complex_axis = complex_axis
self.out_conv = ComplexConv1D(in_channels=dim*2, out_channels=dim, kernel_size=3, padding=1, complex_axis=1)
def forward(self, x: torch.Tensor, laterial: torch.Tensor = None) -> torch.Tensor:
# residual = x
x = self.dwconv(x)
real, imag = torch.chunk(x, 2, dim=self.complex_axis) # Split real and imaginary parts
real = real.transpose(1, 2) # (B, C, T) -> (B, T, C)
imag = imag.transpose(1, 2) # (B, C, T) -> (B, T, C)
real = self.norm(real) # Apply LayerNorm to real part
imag = self.norm(imag) # Apply LayerNorm to imaginary part
real = self.pwconv2(self.act(self.pwconv1(real))) # MLP on real part
imag = self.pwconv2(self.act(self.pwconv1(imag))) # MLP on imaginary part
if self.gamma is not None:
real = self.gamma * real
imag = self.gamma * imag
real = real.transpose(1, 2) # (B, T, C) -> (B, C, T)
imag = imag.transpose(1, 2) # (B, T, C) -> (B, C, T)
x = torch.cat([real, imag], dim=self.complex_axis) # Concatenate real and imaginary parts back together
# x = residual + x
if laterial is not None:
up_real, up_imag = torch.chunk(x, 2, dim=self.complex_axis)
res_real, res_imag = torch.chunk(laterial, 2, dim=self.complex_axis)
real_total = torch.cat([up_real, res_real], self.complex_axis)
imag_total = torch.cat([up_imag, res_imag], self.complex_axis)
x = torch.cat([real_total, imag_total], self.complex_axis)
x = self.out_conv(x)
return x
|