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- import torch
- from torch import nn
- from modules.commons.conv import ConditionalConvBlocks
- from modules.commons.wavenet import WN
- class FlipLayer(nn.Module):
- def forward(self, x, *args, **kwargs):
- x = torch.flip(x, [1])
- return x
- class CouplingLayer(nn.Module):
- def __init__(self, c_in, hidden_size, kernel_size, n_layers, p_dropout=0, c_in_g=0, nn_type='wn'):
- super().__init__()
- self.channels = c_in
- self.hidden_size = hidden_size
- self.kernel_size = kernel_size
- self.n_layers = n_layers
- self.c_half = c_in // 2
- self.pre = nn.Conv1d(self.c_half, hidden_size, 1)
- if nn_type == 'wn':
- self.enc = WN(hidden_size, kernel_size, 1, n_layers, p_dropout=p_dropout,
- c_cond=c_in_g)
- elif nn_type == 'conv':
- self.enc = ConditionalConvBlocks(
- hidden_size, c_in_g, hidden_size, None, kernel_size,
- layers_in_block=1, is_BTC=False, num_layers=n_layers)
- self.post = nn.Conv1d(hidden_size, self.c_half, 1)
- def forward(self, x, nonpadding, cond=None, reverse=False):
- x0, x1 = x[:, :self.c_half], x[:, self.c_half:]
- x_ = self.pre(x0) * nonpadding
- x_ = self.enc(x_, nonpadding=nonpadding, cond=cond)
- m = self.post(x_)
- x1 = m + x1 if not reverse else x1 - m
- x = torch.cat([x0, x1], 1)
- return x * nonpadding
- class ResFlow(nn.Module):
- def __init__(self,
- c_in,
- hidden_size,
- kernel_size,
- n_flow_layers,
- n_flow_steps=4,
- c_cond=0,
- nn_type='wn'):
- super().__init__()
- self.flows = nn.ModuleList()
- for i in range(n_flow_steps):
- self.flows.append(
- CouplingLayer(c_in, hidden_size, kernel_size, n_flow_layers, c_in_g=c_cond, nn_type=nn_type))
- self.flows.append(FlipLayer())
- def forward(self, x, nonpadding, cond=None, reverse=False):
- for flow in (self.flows if not reverse else reversed(self.flows)):
- x = flow(x, nonpadding, cond=cond, reverse=reverse)
- return x
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