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from torch import nn | |
from so_vits_svc_fork.modules import modules as modules | |
class ResidualCouplingBlock(nn.Module): | |
def __init__( | |
self, | |
channels, | |
hidden_channels, | |
kernel_size, | |
dilation_rate, | |
n_layers, | |
n_flows=4, | |
gin_channels=0, | |
): | |
super().__init__() | |
self.channels = channels | |
self.hidden_channels = hidden_channels | |
self.kernel_size = kernel_size | |
self.dilation_rate = dilation_rate | |
self.n_layers = n_layers | |
self.n_flows = n_flows | |
self.gin_channels = gin_channels | |
self.flows = nn.ModuleList() | |
for i in range(n_flows): | |
self.flows.append( | |
modules.ResidualCouplingLayer( | |
channels, | |
hidden_channels, | |
kernel_size, | |
dilation_rate, | |
n_layers, | |
gin_channels=gin_channels, | |
mean_only=True, | |
) | |
) | |
self.flows.append(modules.Flip()) | |
def forward(self, x, x_mask, g=None, reverse=False): | |
if not reverse: | |
for flow in self.flows: | |
x, _ = flow(x, x_mask, g=g, reverse=reverse) | |
else: | |
for flow in reversed(self.flows): | |
x = flow(x, x_mask, g=g, reverse=reverse) | |
return x | |