Source code for speechbrain.lobes.models.ContextNet

"""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)