File size: 9,740 Bytes
51e2f90 |
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 |
from typing import List, Tuple
import torch
import torch.nn as nn
from models.scnet_unofficial.utils import create_intervals
class Downsample(nn.Module):
"""
Downsample class implements a module for downsampling input tensors using 2D convolution.
Args:
- input_dim (int): Dimensionality of the input channels.
- output_dim (int): Dimensionality of the output channels.
- stride (int): Stride value for the convolution operation.
Shapes:
- Input: (B, C_in, F, T) where
B is batch size,
C_in is the number of input channels,
F is the frequency dimension,
T is the time dimension.
- Output: (B, C_out, F // stride, T) where
B is batch size,
C_out is the number of output channels,
F // stride is the downsampled frequency dimension.
"""
def __init__(
self,
input_dim: int,
output_dim: int,
stride: int,
):
"""
Initializes Downsample with input dimension, output dimension, and stride.
"""
super().__init__()
self.conv = nn.Conv2d(input_dim, output_dim, 1, (stride, 1))
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""
Performs forward pass through the Downsample module.
Args:
- x (torch.Tensor): Input tensor of shape (B, C_in, F, T).
Returns:
- torch.Tensor: Downsampled tensor of shape (B, C_out, F // stride, T).
"""
return self.conv(x)
class ConvolutionModule(nn.Module):
"""
ConvolutionModule class implements a module with a sequence of convolutional layers similar to Conformer.
Args:
- input_dim (int): Dimensionality of the input features.
- hidden_dim (int): Dimensionality of the hidden features.
- kernel_sizes (List[int]): List of kernel sizes for the convolutional layers.
- bias (bool, optional): If True, adds a learnable bias to the output. Default is False.
Shapes:
- Input: (B, T, D) where
B is batch size,
T is sequence length,
D is input dimensionality.
- Output: (B, T, D) where
B is batch size,
T is sequence length,
D is input dimensionality.
"""
def __init__(
self,
input_dim: int,
hidden_dim: int,
kernel_sizes: List[int],
bias: bool = False,
) -> None:
"""
Initializes ConvolutionModule with input dimension, hidden dimension, kernel sizes, and bias.
"""
super().__init__()
self.sequential = nn.Sequential(
nn.GroupNorm(num_groups=1, num_channels=input_dim),
nn.Conv1d(
input_dim,
2 * hidden_dim,
kernel_sizes[0],
stride=1,
padding=(kernel_sizes[0] - 1) // 2,
bias=bias,
),
nn.GLU(dim=1),
nn.Conv1d(
hidden_dim,
hidden_dim,
kernel_sizes[1],
stride=1,
padding=(kernel_sizes[1] - 1) // 2,
groups=hidden_dim,
bias=bias,
),
nn.GroupNorm(num_groups=1, num_channels=hidden_dim),
nn.SiLU(),
nn.Conv1d(
hidden_dim,
input_dim,
kernel_sizes[2],
stride=1,
padding=(kernel_sizes[2] - 1) // 2,
bias=bias,
),
)
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""
Performs forward pass through the ConvolutionModule.
Args:
- x (torch.Tensor): Input tensor of shape (B, T, D).
Returns:
- torch.Tensor: Output tensor of shape (B, T, D).
"""
x = x.transpose(1, 2)
x = x + self.sequential(x)
x = x.transpose(1, 2)
return x
class SDLayer(nn.Module):
"""
SDLayer class implements a subband decomposition layer with downsampling and convolutional modules.
Args:
- subband_interval (Tuple[float, float]): Tuple representing the frequency interval for subband decomposition.
- input_dim (int): Dimensionality of the input channels.
- output_dim (int): Dimensionality of the output channels after downsampling.
- downsample_stride (int): Stride value for the downsampling operation.
- n_conv_modules (int): Number of convolutional modules.
- kernel_sizes (List[int]): List of kernel sizes for the convolutional layers.
- bias (bool, optional): If True, adds a learnable bias to the convolutional layers. Default is True.
Shapes:
- Input: (B, Fi, T, Ci) where
B is batch size,
Fi is the number of input subbands,
T is sequence length, and
Ci is the number of input channels.
- Output: (B, Fi+1, T, Ci+1) where
B is batch size,
Fi+1 is the number of output subbands,
T is sequence length,
Ci+1 is the number of output channels.
"""
def __init__(
self,
subband_interval: Tuple[float, float],
input_dim: int,
output_dim: int,
downsample_stride: int,
n_conv_modules: int,
kernel_sizes: List[int],
bias: bool = True,
):
"""
Initializes SDLayer with subband interval, input dimension,
output dimension, downsample stride, number of convolutional modules, kernel sizes, and bias.
"""
super().__init__()
self.subband_interval = subband_interval
self.downsample = Downsample(input_dim, output_dim, downsample_stride)
self.activation = nn.GELU()
conv_modules = [
ConvolutionModule(
input_dim=output_dim,
hidden_dim=output_dim // 4,
kernel_sizes=kernel_sizes,
bias=bias,
)
for _ in range(n_conv_modules)
]
self.conv_modules = nn.Sequential(*conv_modules)
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""
Performs forward pass through the SDLayer.
Args:
- x (torch.Tensor): Input tensor of shape (B, Fi, T, Ci).
Returns:
- torch.Tensor: Output tensor of shape (B, Fi+1, T, Ci+1).
"""
B, F, T, C = x.shape
x = x[:, int(self.subband_interval[0] * F) : int(self.subband_interval[1] * F)]
x = x.permute(0, 3, 1, 2)
x = self.downsample(x)
x = self.activation(x)
x = x.permute(0, 2, 3, 1)
B, F, T, C = x.shape
x = x.reshape((B * F), T, C)
x = self.conv_modules(x)
x = x.reshape(B, F, T, C)
return x
class SDBlock(nn.Module):
"""
SDBlock class implements a block with subband decomposition layers and global convolution.
Args:
- input_dim (int): Dimensionality of the input channels.
- output_dim (int): Dimensionality of the output channels.
- bandsplit_ratios (List[float]): List of ratios for splitting the frequency bands.
- downsample_strides (List[int]): List of stride values for downsampling in each subband layer.
- n_conv_modules (List[int]): List specifying the number of convolutional modules in each subband layer.
- kernel_sizes (List[int], optional): List of kernel sizes for the convolutional layers. Default is None.
Shapes:
- Input: (B, Fi, T, Ci) where
B is batch size,
Fi is the number of input subbands,
T is sequence length,
Ci is the number of input channels.
- Output: (B, Fi+1, T, Ci+1) where
B is batch size,
Fi+1 is the number of output subbands,
T is sequence length,
Ci+1 is the number of output channels.
"""
def __init__(
self,
input_dim: int,
output_dim: int,
bandsplit_ratios: List[float],
downsample_strides: List[int],
n_conv_modules: List[int],
kernel_sizes: List[int] = None,
):
"""
Initializes SDBlock with input dimension, output dimension, band split ratios, downsample strides, number of convolutional modules, and kernel sizes.
"""
super().__init__()
if kernel_sizes is None:
kernel_sizes = [3, 3, 1]
assert sum(bandsplit_ratios) == 1, "The split ratios must sum up to 1."
subband_intervals = create_intervals(bandsplit_ratios)
self.sd_layers = nn.ModuleList(
SDLayer(
input_dim=input_dim,
output_dim=output_dim,
subband_interval=sbi,
downsample_stride=dss,
n_conv_modules=ncm,
kernel_sizes=kernel_sizes,
)
for sbi, dss, ncm in zip(
subband_intervals, downsample_strides, n_conv_modules
)
)
self.global_conv2d = nn.Conv2d(output_dim, output_dim, 1, 1)
def forward(self, x: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
"""
Performs forward pass through the SDBlock.
Args:
- x (torch.Tensor): Input tensor of shape (B, Fi, T, Ci).
Returns:
- Tuple[torch.Tensor, torch.Tensor]: Output tensor and skip connection tensor.
"""
x_skip = torch.concat([layer(x) for layer in self.sd_layers], dim=1)
x = self.global_conv2d(x_skip.permute(0, 3, 1, 2)).permute(0, 2, 3, 1)
return x, x_skip
|