38 lines
1.3 KiB
Python
38 lines
1.3 KiB
Python
from torch import nn
|
|
|
|
from .normalization import LayerNorm
|
|
|
|
|
|
class GatedConvBlock(nn.Module):
|
|
"""Gated convolutional block as in https://arxiv.org/pdf/1612.08083.pdf
|
|
Args:
|
|
in_out_channels (int): number of input/output channels.
|
|
kernel_size (int): convolution kernel size.
|
|
dropout_p (float): dropout rate.
|
|
"""
|
|
|
|
def __init__(self, in_out_channels, kernel_size, dropout_p, num_layers):
|
|
super().__init__()
|
|
# class arguments
|
|
self.dropout_p = dropout_p
|
|
self.num_layers = num_layers
|
|
# define layers
|
|
self.conv_layers = nn.ModuleList()
|
|
self.norm_layers = nn.ModuleList()
|
|
self.layers = nn.ModuleList()
|
|
for _ in range(num_layers):
|
|
self.conv_layers += [nn.Conv1d(in_out_channels, 2 * in_out_channels, kernel_size, padding=kernel_size // 2)]
|
|
self.norm_layers += [LayerNorm(2 * in_out_channels)]
|
|
|
|
def forward(self, x, x_mask):
|
|
o = x
|
|
res = x
|
|
for idx in range(self.num_layers):
|
|
o = nn.functional.dropout(o, p=self.dropout_p, training=self.training)
|
|
o = self.conv_layers[idx](o * x_mask)
|
|
o = self.norm_layers[idx](o)
|
|
o = nn.functional.glu(o, dim=1)
|
|
o = res + o
|
|
res = o
|
|
return o
|