"""The SpeechBrain implementation of ContextNet by
https://arxiv.org/pdf/2005.03191.pdf
Authors
* Jianyuan Zhong 2020
"""
import torch
from torch.nn import Dropout
from speechbrain.nnet.CNN import DepthwiseSeparableConv1d, Conv1d
from speechbrain.nnet.linear import Linear
from speechbrain.nnet.pooling import AdaptivePool
from speechbrain.nnet.containers import Sequential
from speechbrain.nnet.normalization import BatchNorm1d
from speechbrain.nnet.activations import Swish
[docs]class ContextNet(Sequential):
"""This class implements the ContextNet.
Reference paper: https://arxiv.org/pdf/2005.03191.pdf
Arguments
----------
out_channels : int
Number of output channels of this model (default 640).
conv_channels : Optional (list[int])
Number of output channels for each of the contextnet block. If not provided, it will be initialized as the default setting of above mentioned paper.
kernel_size : int
Kernel size of convolution layers (default 3).
strides: Optional (list[int])
Striding factor for each context block. This stride is applied at the last convolution layer at each context block. If not provided, it will be initialize as the default setting of above paper.
num_blocks : int
Number of context block (default 21).
num_layers : int
Number of depthwise convolution layers for each context block (default 5).
inner_dim : int
Inner dimension of bottle-neck network of the SE Module (default 12).
alpha : float
The factor to scale the output channel of the network (default 1).
beta : float
Beta to scale the Swish activation (default 1).
dropout : float
Dropout (default 0.15).
activation : torch class
Activation function for each context block (default Swish).
se_activation : torch class
Activation function for SE Module (default torch.nn.Sigmoid).
norm : torch class
Normalization to regularize the model (default BatchNorm1d).
residuals : Optional (list[bool])
Whether to apply residual connection at each context block (default None).
Example
-------
>>> inp = torch.randn([8, 48, 40])
>>> block = ContextNet(input_shape=inp.shape, num_blocks=14)
>>> out = block(inp)
>>> out.shape
torch.Size([8, 6, 640])
"""
def __init__(
self,
input_shape,
out_channels=640,
conv_channels=None,
kernel_size=3,
strides=None,
num_blocks=21,
num_layers=5,
inner_dim=12,
alpha=1,
beta=1,
dropout=0.15,
activation=Swish,
se_activation=torch.nn.Sigmoid,
norm=BatchNorm1d,
residuals=None,
):
super().__init__(input_shape=input_shape)
if conv_channels is None:
conv_channels = [*[256] * 10, *[512] * 11]
if strides is None:
strides = [1] * num_blocks
strides[2] = 2
strides[6] = 2
strides[13] = 2
if residuals is None:
residuals = [True] * num_blocks
self.append(
DepthwiseSeparableConv1d,
conv_channels[0],
kernel_size,
layer_name="conv_start",
)
self.append(norm, layer_name="norm_start")
if isinstance(activation, Swish):
self.append(activation(beta), layer_name="act_start")
else:
self.append(activation(), layer_name="act_start")
for i in range(num_blocks):
channels = int(conv_channels[i] * alpha)
self.append(
ContextNetBlock,
out_channels=channels,
kernel_size=kernel_size,
num_layers=num_layers,
inner_dim=inner_dim,
stride=strides[i],
beta=beta,
dropout=dropout,
activation=activation,
se_activation=se_activation,
norm=norm,
residual=residuals[i],
layer_name=f"block_{i}",
)
self.append(
DepthwiseSeparableConv1d,
out_channels,
kernel_size,
layer_name="conv_end",
)
self.append(norm, layer_name="norm_end")
if isinstance(activation, Swish):
self.append(activation(beta), layer_name="act_end")
else:
self.append(activation(), layer_name="act_end")
[docs]class SEmodule(torch.nn.Module):
"""This class implements the Squeeze-and-Excitation module.
Arguments
---------
inner_dim : int
Inner dimension of bottle-neck network of the SE Module (default 12).
activation : torch class
Activation function for SE Module (default torch.nn.Sigmoid).
norm : torch class
Normalization to regularize the model (default BatchNorm1d).
Example
-------
>>> inp = torch.randn([8, 120, 40])
>>> net = SEmodule(input_shape=inp.shape, inner_dim=64)
>>> out = net(inp)
>>> out.shape
torch.Size([8, 120, 40])
"""
def __init__(
self,
input_shape,
inner_dim,
activation=torch.nn.Sigmoid,
norm=BatchNorm1d,
):
super().__init__()
self.inner_dim = inner_dim
self.norm = norm
self.activation = activation
bz, t, chn = input_shape
self.conv = Sequential(input_shape=input_shape)
self.conv.append(
DepthwiseSeparableConv1d, out_channels=chn, kernel_size=1, stride=1,
)
self.conv.append(self.norm)
self.conv.append(self.activation())
self.avg_pool = AdaptivePool(1)
self.bottleneck = Sequential(
Linear(input_size=input_shape[-1], n_neurons=self.inner_dim),
self.activation(),
Linear(input_size=self.inner_dim, n_neurons=chn),
self.activation(),
)
[docs] def forward(self, x):
""" Processes the input tensor x and returns an output tensor."""
bz, t, chn = x.shape
x = self.conv(x)
avg = self.avg_pool(x)
avg = self.bottleneck(avg)
context = avg.repeat(1, t, 1)
return x * context
[docs]class ContextNetBlock(torch.nn.Module):
"""This class implements a block in ContextNet.
Arguments
---------
out_channels : int
Number of output channels of this model (default 640).
kernel_size : int
Kernel size of convolution layers (default 3).
strides : int
Striding factor for this context block (default 1).
num_layersi : int
Number of depthwise convolution layers for this context block (default 5).
inner_dim : int
Inner dimension of bottle-neck network of the SE Module (default 12).
beta : float
Beta to scale the Swish activation (default 1).
dropout : float
Dropout (default 0.15).
activation : torch class
Activation function for this context block (default Swish).
se_activation : torch class
Activation function for SE Module (default torch.nn.Sigmoid).
norm : torch class
Normalization to regularize the model (default BatchNorm1d).
residuals : bool
Whether to apply residual connection at this context block (default None).
Example
-------
>>> inp = torch.randn([8, 120, 40])
>>> block = ContextNetBlock(256, 3, 5, 12, input_shape=inp.shape, stride=2)
>>> out = block(inp)
>>> out.shape
torch.Size([8, 60, 256])
"""
def __init__(
self,
out_channels,
kernel_size,
num_layers,
inner_dim,
input_shape,
stride=1,
beta=1,
dropout=0.15,
activation=Swish,
se_activation=torch.nn.Sigmoid,
norm=BatchNorm1d,
residual=True,
):
super().__init__()
self.residual = residual
self.Convs = Sequential(input_shape=input_shape)
for i in range(num_layers):
self.Convs.append(
DepthwiseSeparableConv1d,
out_channels,
kernel_size,
stride=stride if i == num_layers - 1 else 1,
)
self.Convs.append(norm)
self.SE = SEmodule(
input_shape=self.Convs.get_output_shape(),
inner_dim=inner_dim,
activation=se_activation,
norm=norm,
)
self.drop = Dropout(dropout)
self.reduced_cov = None
if residual:
self.reduced_cov = Sequential(input_shape=input_shape)
self.reduced_cov.append(
Conv1d, out_channels, kernel_size=3, stride=stride,
)
self.reduced_cov.append(norm)
if isinstance(activation, Swish):
self.activation = activation(beta)
else:
self.activation = activation()
self._reset_params()
[docs] def forward(self, x):
""" Processes the input tensor x and returns an output tensor."""
out = self.Convs(x)
out = self.SE(out)
if self.reduced_cov:
out = out + self.reduced_cov(x)
out = self.activation(out)
return self.drop(out)
def _reset_params(self):
for p in self.parameters():
if p.dim() > 1:
torch.nn.init.kaiming_normal_(p)