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# -*- coding: utf-8 -*- | |
"""Residual block module in WaveNet. | |
This code is modified from https://github.com/r9y9/wavenet_vocoder. | |
""" | |
import math | |
import torch | |
import torch.nn.functional as F | |
class Conv1d(torch.nn.Conv1d): | |
"""Conv1d module with customized initialization.""" | |
def __init__(self, *args, **kwargs): | |
"""Initialize Conv1d module.""" | |
super(Conv1d, self).__init__(*args, **kwargs) | |
def reset_parameters(self): | |
"""Reset parameters.""" | |
torch.nn.init.kaiming_normal_(self.weight, nonlinearity="relu") | |
if self.bias is not None: | |
torch.nn.init.constant_(self.bias, 0.0) | |
class Conv1d1x1(Conv1d): | |
"""1x1 Conv1d with customized initialization.""" | |
def __init__(self, in_channels, out_channels, bias): | |
"""Initialize 1x1 Conv1d module.""" | |
super(Conv1d1x1, self).__init__(in_channels, out_channels, | |
kernel_size=1, padding=0, | |
dilation=1, bias=bias) | |
class ResidualBlock(torch.nn.Module): | |
"""Residual block module in WaveNet.""" | |
def __init__(self, | |
kernel_size=3, | |
residual_channels=64, | |
gate_channels=128, | |
skip_channels=64, | |
aux_channels=80, | |
dropout=0.0, | |
dilation=1, | |
bias=True, | |
use_causal_conv=False | |
): | |
"""Initialize ResidualBlock module. | |
Args: | |
kernel_size (int): Kernel size of dilation convolution layer. | |
residual_channels (int): Number of channels for residual connection. | |
skip_channels (int): Number of channels for skip connection. | |
aux_channels (int): Local conditioning channels i.e. auxiliary input dimension. | |
dropout (float): Dropout probability. | |
dilation (int): Dilation factor. | |
bias (bool): Whether to add bias parameter in convolution layers. | |
use_causal_conv (bool): Whether to use use_causal_conv or non-use_causal_conv convolution. | |
""" | |
super(ResidualBlock, self).__init__() | |
self.dropout = dropout | |
# no future time stamps available | |
if use_causal_conv: | |
padding = (kernel_size - 1) * dilation | |
else: | |
assert (kernel_size - 1) % 2 == 0, "Not support even number kernel size." | |
padding = (kernel_size - 1) // 2 * dilation | |
self.use_causal_conv = use_causal_conv | |
# dilation conv | |
self.conv = Conv1d(residual_channels, gate_channels, kernel_size, | |
padding=padding, dilation=dilation, bias=bias) | |
# local conditioning | |
if aux_channels > 0: | |
self.conv1x1_aux = Conv1d1x1(aux_channels, gate_channels, bias=False) | |
else: | |
self.conv1x1_aux = None | |
# conv output is split into two groups | |
gate_out_channels = gate_channels // 2 | |
self.conv1x1_out = Conv1d1x1(gate_out_channels, residual_channels, bias=bias) | |
self.conv1x1_skip = Conv1d1x1(gate_out_channels, skip_channels, bias=bias) | |
def forward(self, x, c): | |
"""Calculate forward propagation. | |
Args: | |
x (Tensor): Input tensor (B, residual_channels, T). | |
c (Tensor): Local conditioning auxiliary tensor (B, aux_channels, T). | |
Returns: | |
Tensor: Output tensor for residual connection (B, residual_channels, T). | |
Tensor: Output tensor for skip connection (B, skip_channels, T). | |
""" | |
residual = x | |
x = F.dropout(x, p=self.dropout, training=self.training) | |
x = self.conv(x) | |
# remove future time steps if use_causal_conv conv | |
x = x[:, :, :residual.size(-1)] if self.use_causal_conv else x | |
# split into two part for gated activation | |
splitdim = 1 | |
xa, xb = x.split(x.size(splitdim) // 2, dim=splitdim) | |
# local conditioning | |
if c is not None: | |
assert self.conv1x1_aux is not None | |
c = self.conv1x1_aux(c) | |
ca, cb = c.split(c.size(splitdim) // 2, dim=splitdim) | |
xa, xb = xa + ca, xb + cb | |
x = torch.tanh(xa) * torch.sigmoid(xb) | |
# for skip connection | |
s = self.conv1x1_skip(x) | |
# for residual connection | |
x = (self.conv1x1_out(x) + residual) * math.sqrt(0.5) | |
return x, s | |