|
import torch.nn as nn |
|
import torch |
|
|
|
import pflow.models.components.vits_modules as modules |
|
import pflow.models.components.commons as commons |
|
|
|
class PosteriorEncoder(nn.Module): |
|
|
|
def __init__(self, |
|
in_channels, |
|
out_channels, |
|
hidden_channels, |
|
kernel_size, |
|
dilation_rate, |
|
n_layers, |
|
gin_channels=0): |
|
super().__init__() |
|
self.in_channels = in_channels |
|
self.out_channels = out_channels |
|
self.hidden_channels = hidden_channels |
|
self.kernel_size = kernel_size |
|
self.dilation_rate = dilation_rate |
|
self.n_layers = n_layers |
|
self.gin_channels = gin_channels |
|
|
|
self.pre = nn.Conv1d(in_channels, hidden_channels, 1) |
|
self.enc = modules.WN(hidden_channels, |
|
kernel_size, |
|
dilation_rate, |
|
n_layers, |
|
gin_channels=gin_channels) |
|
self.proj = nn.Conv1d(hidden_channels, out_channels, 1) |
|
|
|
def forward(self, x, x_lengths, g=None): |
|
x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), |
|
1).to(x.dtype) |
|
x = self.pre(x) * x_mask |
|
x = self.enc(x, x_mask, g=g) |
|
stats = self.proj(x) * x_mask |
|
|
|
|
|
|
|
return stats, x_mask |
|
|