280 lines
9.4 KiB
Python
280 lines
9.4 KiB
Python
# These codes are copied from modelscope revision c58451baead80d83281f063d12fb377fad415257
|
|
# Copyright (c) Alibaba, Inc. and its affiliates.
|
|
|
|
import torch
|
|
import torch.nn as nn
|
|
import torch.nn.init as init
|
|
from torch import Tensor
|
|
|
|
EPS = 1e-8
|
|
|
|
|
|
class GlobalLayerNorm(nn.Module):
|
|
"""Calculate Global Layer Normalization.
|
|
|
|
Args:
|
|
dim : (int or list or torch.Size)
|
|
Input shape from an expected input of size.
|
|
eps : float
|
|
A value added to the denominator for numerical stability.
|
|
elementwise_affine : bool
|
|
A boolean value that when set to True,
|
|
this module has learnable per-element affine parameters
|
|
initialized to ones (for weights) and zeros (for biases).
|
|
|
|
Example:
|
|
-------
|
|
>>> x = torch.randn(5, 10, 20)
|
|
>>> GLN = GlobalLayerNorm(10, 3)
|
|
>>> x_norm = GLN(x)
|
|
"""
|
|
|
|
def __init__(self, dim, shape, eps=1e-8, elementwise_affine=True):
|
|
super(GlobalLayerNorm, self).__init__()
|
|
self.dim = dim
|
|
self.eps = eps
|
|
self.elementwise_affine = elementwise_affine
|
|
|
|
if self.elementwise_affine:
|
|
if shape == 3:
|
|
self.weight = nn.Parameter(torch.ones(self.dim, 1))
|
|
self.bias = nn.Parameter(torch.zeros(self.dim, 1))
|
|
if shape == 4:
|
|
self.weight = nn.Parameter(torch.ones(self.dim, 1, 1))
|
|
self.bias = nn.Parameter(torch.zeros(self.dim, 1, 1))
|
|
else:
|
|
self.register_parameter('weight', None)
|
|
self.register_parameter('bias', None)
|
|
|
|
def forward(self, x):
|
|
"""Returns the normalized tensor.
|
|
|
|
Args:
|
|
x : torch.Tensor
|
|
Tensor of size [N, C, K, S] or [N, C, L].
|
|
"""
|
|
# x = N x C x K x S or N x C x L
|
|
# N x 1 x 1
|
|
# cln: mean,var N x 1 x K x S
|
|
# gln: mean,var N x 1 x 1
|
|
if x.dim() == 3:
|
|
mean = torch.mean(x, (1, 2), keepdim=True)
|
|
var = torch.mean((x - mean)**2, (1, 2), keepdim=True)
|
|
if self.elementwise_affine:
|
|
# yapf: disable
|
|
x = (self.weight * (x - mean) / torch.sqrt(var + self.eps)
|
|
+ self.bias)
|
|
# yapf: enable
|
|
else:
|
|
x = (x - mean) / torch.sqrt(var + self.eps)
|
|
|
|
if x.dim() == 4:
|
|
mean = torch.mean(x, (1, 2, 3), keepdim=True)
|
|
var = torch.mean((x - mean)**2, (1, 2, 3), keepdim=True)
|
|
if self.elementwise_affine:
|
|
# yapf: disable
|
|
x = (self.weight * (x - mean) / torch.sqrt(var + self.eps)
|
|
+ self.bias)
|
|
# yapf: enable
|
|
else:
|
|
x = (x - mean) / torch.sqrt(var + self.eps)
|
|
return x
|
|
|
|
|
|
class CumulativeLayerNorm(nn.LayerNorm):
|
|
"""Calculate Cumulative Layer Normalization.
|
|
|
|
Args:
|
|
dim : int
|
|
Dimension that you want to normalize.
|
|
elementwise_affine : True
|
|
Learnable per-element affine parameters.
|
|
|
|
Example:
|
|
-------
|
|
>>> x = torch.randn(5, 10, 20)
|
|
>>> CLN = CumulativeLayerNorm(10)
|
|
>>> x_norm = CLN(x)
|
|
"""
|
|
|
|
def __init__(self, dim, elementwise_affine=True):
|
|
super(CumulativeLayerNorm, self).__init__(
|
|
dim, elementwise_affine=elementwise_affine, eps=1e-8)
|
|
|
|
def forward(self, x):
|
|
"""Returns the normalized tensor.
|
|
|
|
Args:
|
|
x : torch.Tensor
|
|
Tensor size [N, C, K, S] or [N, C, L]
|
|
"""
|
|
# x: N x C x K x S or N x C x L
|
|
# N x K x S x C
|
|
if x.dim() == 4:
|
|
x = x.permute(0, 2, 3, 1).contiguous()
|
|
# N x K x S x C == only channel norm
|
|
x = super().forward(x)
|
|
# N x C x K x S
|
|
x = x.permute(0, 3, 1, 2).contiguous()
|
|
if x.dim() == 3:
|
|
x = torch.transpose(x, 1, 2)
|
|
# N x L x C == only channel norm
|
|
x = super().forward(x)
|
|
# N x C x L
|
|
x = torch.transpose(x, 1, 2)
|
|
return x
|
|
|
|
|
|
class Transpose(nn.Module):
|
|
""" Wrapper class of torch.transpose() for Sequential module. """
|
|
|
|
def __init__(self, shape: tuple):
|
|
super(Transpose, self).__init__()
|
|
self.shape = shape
|
|
|
|
def forward(self, x: Tensor) -> Tensor:
|
|
return x.transpose(*self.shape)
|
|
|
|
|
|
class DepthwiseConv1d(nn.Module):
|
|
"""When groups == in_channels and out_channels == K * in_channels, where K is a positive integer,
|
|
this operation is termed in literature as depthwise convolution.
|
|
|
|
Args:
|
|
in_channels (int): Number of channels in the input
|
|
out_channels (int): Number of channels produced by the convolution
|
|
kernel_size (int or tuple): Size of the convolving kernel
|
|
stride (int, optional): Stride of the convolution. Default: 1
|
|
padding (int or tuple, optional): Zero-padding added to both sides of the input. Default: 0
|
|
bias (bool, optional): If True, adds a learnable bias to the output. Default: True
|
|
Inputs: inputs
|
|
- **inputs** (batch, in_channels, time): Tensor containing input vector
|
|
Returns: outputs
|
|
- **outputs** (batch, out_channels, time): Tensor produces by depthwise 1-D convolution.
|
|
"""
|
|
|
|
def __init__(
|
|
self,
|
|
in_channels: int,
|
|
out_channels: int,
|
|
kernel_size: int,
|
|
stride: int = 1,
|
|
padding: int = 0,
|
|
bias: bool = False,
|
|
) -> None:
|
|
super(DepthwiseConv1d, self).__init__()
|
|
assert out_channels % in_channels == 0, 'out_channels should be constant multiple of in_channels'
|
|
self.conv = nn.Conv1d(
|
|
in_channels=in_channels,
|
|
out_channels=out_channels,
|
|
kernel_size=kernel_size,
|
|
groups=in_channels,
|
|
stride=stride,
|
|
padding=padding,
|
|
bias=bias,
|
|
)
|
|
|
|
def forward(self, inputs: Tensor) -> Tensor:
|
|
return self.conv(inputs)
|
|
|
|
|
|
class ConvModule(nn.Module):
|
|
"""
|
|
Conformer convolution module starts with a pointwise convolution and a gated linear unit (GLU).
|
|
This is followed by a single 1-D depthwise convolution layer. Batchnorm is deployed just after the convolution
|
|
to aid training deep models.
|
|
|
|
Args:
|
|
in_channels (int): Number of channels in the input
|
|
kernel_size (int or tuple, optional): Size of the convolving kernel Default: 17
|
|
dropout_p (float, optional): probability of dropout
|
|
Inputs: inputs
|
|
inputs (batch, time, dim): Tensor contains input sequences
|
|
Outputs: outputs
|
|
outputs (batch, time, dim): Tensor produces by conformer convolution module.
|
|
"""
|
|
|
|
def __init__(
|
|
self,
|
|
in_channels: int,
|
|
kernel_size: int = 17,
|
|
expansion_factor: int = 2,
|
|
dropout_p: float = 0.1,
|
|
) -> None:
|
|
super(ConvModule, self).__init__()
|
|
assert (
|
|
kernel_size - 1
|
|
) % 2 == 0, "kernel_size should be a odd number for 'SAME' padding"
|
|
assert expansion_factor == 2, 'Currently, Only Supports expansion_factor 2'
|
|
|
|
self.sequential = nn.Sequential(
|
|
Transpose(shape=(1, 2)),
|
|
DepthwiseConv1d(
|
|
in_channels,
|
|
in_channels,
|
|
kernel_size,
|
|
stride=1,
|
|
padding=(kernel_size - 1) // 2),
|
|
)
|
|
|
|
def forward(self, inputs: Tensor) -> Tensor:
|
|
return inputs + self.sequential(inputs).transpose(1, 2)
|
|
|
|
|
|
class DilatedDenseNet(nn.Module):
|
|
|
|
def __init__(self, depth=4, lorder=20, in_channels=64):
|
|
super(DilatedDenseNet, self).__init__()
|
|
self.depth = depth
|
|
self.in_channels = in_channels
|
|
self.pad = nn.ConstantPad2d((1, 1, 1, 0), value=0.)
|
|
self.twidth = lorder * 2 - 1
|
|
self.kernel_size = (self.twidth, 1)
|
|
for i in range(self.depth):
|
|
dil = 2**i
|
|
pad_length = lorder + (dil - 1) * (lorder - 1) - 1
|
|
setattr(self, 'pad{}'.format(i + 1),
|
|
nn.ConstantPad2d((0, 0, pad_length, pad_length), value=0.))
|
|
setattr(
|
|
self, 'conv{}'.format(i + 1),
|
|
nn.Conv2d(
|
|
self.in_channels * (i + 1),
|
|
self.in_channels,
|
|
kernel_size=self.kernel_size,
|
|
dilation=(dil, 1),
|
|
groups=self.in_channels,
|
|
bias=False))
|
|
setattr(self, 'norm{}'.format(i + 1),
|
|
nn.InstanceNorm2d(in_channels, affine=True))
|
|
setattr(self, 'prelu{}'.format(i + 1), nn.PReLU(self.in_channels))
|
|
|
|
def forward(self, x):
|
|
x = torch.unsqueeze(x, 1)
|
|
x_per = x.permute(0, 3, 2, 1)
|
|
skip = x_per
|
|
for i in range(self.depth):
|
|
out = getattr(self, 'pad{}'.format(i + 1))(skip)
|
|
out = getattr(self, 'conv{}'.format(i + 1))(out)
|
|
out = getattr(self, 'norm{}'.format(i + 1))(out)
|
|
out = getattr(self, 'prelu{}'.format(i + 1))(out)
|
|
skip = torch.cat([out, skip], dim=1)
|
|
out1 = out.permute(0, 3, 2, 1)
|
|
return out1.squeeze(1)
|
|
|
|
|
|
class FFConvMDilated(nn.Module):
|
|
|
|
def __init__(self, dim_in, dim_out, norm_klass=nn.LayerNorm, dropout=0.1):
|
|
super().__init__()
|
|
self.mdl = nn.Sequential(
|
|
norm_klass(dim_in), nn.Linear(dim_in, dim_out), nn.SiLU(),
|
|
DilatedDenseNet(depth=2, lorder=17, in_channels=dim_out),
|
|
nn.Dropout(dropout))
|
|
|
|
def forward(
|
|
self,
|
|
x,
|
|
):
|
|
output = self.mdl(x)
|
|
return output
|