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import torch
from packaging.version import Version
from torch import nn
from torch.nn import functional as F
from TTS.tts.layers.generic.wavenet import WN
from ..generic.normalization import LayerNorm
class ResidualConv1dLayerNormBlock(nn.Module):
"""Conv1d with Layer Normalization and residual connection as in GlowTTS paper.
https://arxiv.org/pdf/1811.00002.pdf
::
x |-> conv1d -> layer_norm -> relu -> dropout -> + -> o
|---------------> conv1d_1x1 ------------------|
Args:
in_channels (int): number of input tensor channels.
hidden_channels (int): number of inner layer channels.
out_channels (int): number of output tensor channels.
kernel_size (int): kernel size of conv1d filter.
num_layers (int): number of blocks.
dropout_p (float): dropout rate for each block.
"""
def __init__(self, in_channels, hidden_channels, out_channels, kernel_size, num_layers, dropout_p):
super().__init__()
self.in_channels = in_channels
self.hidden_channels = hidden_channels
self.out_channels = out_channels
self.kernel_size = kernel_size
self.num_layers = num_layers
self.dropout_p = dropout_p
assert num_layers > 1, " [!] number of layers should be > 0."
assert kernel_size % 2 == 1, " [!] kernel size should be odd number."
self.conv_layers = nn.ModuleList()
self.norm_layers = nn.ModuleList()
for idx in range(num_layers):
self.conv_layers.append(
nn.Conv1d(
in_channels if idx == 0 else hidden_channels, hidden_channels, kernel_size, padding=kernel_size // 2
)
)
self.norm_layers.append(LayerNorm(hidden_channels))
self.proj = nn.Conv1d(hidden_channels, out_channels, 1)
self.proj.weight.data.zero_()
self.proj.bias.data.zero_()
def forward(self, x, x_mask):
"""
Shapes:
- x: :math:`[B, C, T]`
- x_mask: :math:`[B, 1, T]`
"""
x_res = x
for i in range(self.num_layers):
x = self.conv_layers[i](x * x_mask)
x = self.norm_layers[i](x * x_mask)
x = F.dropout(F.relu(x), self.dropout_p, training=self.training)
x = x_res + self.proj(x)
return x * x_mask
class InvConvNear(nn.Module):
"""Invertible Convolution with input splitting as in GlowTTS paper.
https://arxiv.org/pdf/1811.00002.pdf
Args:
channels (int): input and output channels.
num_splits (int): number of splits, also H and W of conv layer.
no_jacobian (bool): enable/disable jacobian computations.
Note:
Split the input into groups of size self.num_splits and
perform 1x1 convolution separately. Cast 1x1 conv operation
to 2d by reshaping the input for efficiency.
"""
def __init__(self, channels, num_splits=4, no_jacobian=False, **kwargs): # pylint: disable=unused-argument
super().__init__()
assert num_splits % 2 == 0
self.channels = channels
self.num_splits = num_splits
self.no_jacobian = no_jacobian
self.weight_inv = None
if Version(torch.__version__) < Version("1.9"):
w_init = torch.qr(torch.FloatTensor(self.num_splits, self.num_splits).normal_())[0]
else:
w_init = torch.linalg.qr(torch.FloatTensor(self.num_splits, self.num_splits).normal_(), "complete")[0]
if torch.det(w_init) < 0:
w_init[:, 0] = -1 * w_init[:, 0]
self.weight = nn.Parameter(w_init)
def forward(self, x, x_mask=None, reverse=False, **kwargs): # pylint: disable=unused-argument
"""
Shapes:
- x: :math:`[B, C, T]`
- x_mask: :math:`[B, 1, T]`
"""
b, c, t = x.size()
assert c % self.num_splits == 0
if x_mask is None:
x_mask = 1
x_len = torch.ones((b,), dtype=x.dtype, device=x.device) * t
else:
x_len = torch.sum(x_mask, [1, 2])
x = x.view(b, 2, c // self.num_splits, self.num_splits // 2, t)
x = x.permute(0, 1, 3, 2, 4).contiguous().view(b, self.num_splits, c // self.num_splits, t)
if reverse:
if self.weight_inv is not None:
weight = self.weight_inv
else:
weight = torch.inverse(self.weight.float()).to(dtype=self.weight.dtype)
logdet = None
else:
weight = self.weight
if self.no_jacobian:
logdet = 0
else:
logdet = torch.logdet(self.weight) * (c / self.num_splits) * x_len # [b]
weight = weight.view(self.num_splits, self.num_splits, 1, 1)
z = F.conv2d(x, weight)
z = z.view(b, 2, self.num_splits // 2, c // self.num_splits, t)
z = z.permute(0, 1, 3, 2, 4).contiguous().view(b, c, t) * x_mask
return z, logdet
def store_inverse(self):
weight_inv = torch.inverse(self.weight.float()).to(dtype=self.weight.dtype)
self.weight_inv = nn.Parameter(weight_inv, requires_grad=False)
class CouplingBlock(nn.Module):
"""Glow Affine Coupling block as in GlowTTS paper.
https://arxiv.org/pdf/1811.00002.pdf
::
x --> x0 -> conv1d -> wavenet -> conv1d --> t, s -> concat(s*x1 + t, x0) -> o
'-> x1 - - - - - - - - - - - - - - - - - - - - - - - - - ^
Args:
in_channels (int): number of input tensor channels.
hidden_channels (int): number of hidden channels.
kernel_size (int): WaveNet filter kernel size.
dilation_rate (int): rate to increase dilation by each layer in a decoder block.
num_layers (int): number of WaveNet layers.
c_in_channels (int): number of conditioning input channels.
dropout_p (int): wavenet dropout rate.
sigmoid_scale (bool): enable/disable sigmoid scaling for output scale.
Note:
It does not use the conditional inputs differently from WaveGlow.
"""
def __init__(
self,
in_channels,
hidden_channels,
kernel_size,
dilation_rate,
num_layers,
c_in_channels=0,
dropout_p=0,
sigmoid_scale=False,
):
super().__init__()
self.in_channels = in_channels
self.hidden_channels = hidden_channels
self.kernel_size = kernel_size
self.dilation_rate = dilation_rate
self.num_layers = num_layers
self.c_in_channels = c_in_channels
self.dropout_p = dropout_p
self.sigmoid_scale = sigmoid_scale
# input layer
start = torch.nn.Conv1d(in_channels // 2, hidden_channels, 1)
start = torch.nn.utils.parametrizations.weight_norm(start)
self.start = start
# output layer
# Initializing last layer to 0 makes the affine coupling layers
# do nothing at first. This helps with training stability
end = torch.nn.Conv1d(hidden_channels, in_channels, 1)
end.weight.data.zero_()
end.bias.data.zero_()
self.end = end
# coupling layers
self.wn = WN(hidden_channels, hidden_channels, kernel_size, dilation_rate, num_layers, c_in_channels, dropout_p)
def forward(self, x, x_mask=None, reverse=False, g=None, **kwargs): # pylint: disable=unused-argument
"""
Shapes:
- x: :math:`[B, C, T]`
- x_mask: :math:`[B, 1, T]`
- g: :math:`[B, C, 1]`
"""
if x_mask is None:
x_mask = 1
x_0, x_1 = x[:, : self.in_channels // 2], x[:, self.in_channels // 2 :]
x = self.start(x_0) * x_mask
x = self.wn(x, x_mask, g)
out = self.end(x)
z_0 = x_0
t = out[:, : self.in_channels // 2, :]
s = out[:, self.in_channels // 2 :, :]
if self.sigmoid_scale:
s = torch.log(1e-6 + torch.sigmoid(s + 2))
if reverse:
z_1 = (x_1 - t) * torch.exp(-s) * x_mask
logdet = None
else:
z_1 = (t + torch.exp(s) * x_1) * x_mask
logdet = torch.sum(s * x_mask, [1, 2])
z = torch.cat([z_0, z_1], 1)
return z, logdet
def store_inverse(self):
self.wn.remove_weight_norm()
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