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import os
import sys
import torch
from typing import Optional
from torch.nn.utils import remove_weight_norm
from torch.nn.utils.parametrizations import weight_norm
now_dir = os.getcwd()
sys.path.append(now_dir)
from .modules import WaveNet
from .commons import get_padding, init_weights
LRELU_SLOPE = 0.1
def create_conv1d_layer(channels, kernel_size, dilation):
return weight_norm(torch.nn.Conv1d(channels, channels, kernel_size, 1, dilation=dilation, padding=get_padding(kernel_size, dilation)))
def apply_mask(tensor, mask):
return tensor * mask if mask is not None else tensor
class ResBlockBase(torch.nn.Module):
def __init__(self, channels, kernel_size, dilations):
super(ResBlockBase, self).__init__()
self.convs1 = torch.nn.ModuleList([create_conv1d_layer(channels, kernel_size, d) for d in dilations])
self.convs1.apply(init_weights)
self.convs2 = torch.nn.ModuleList([create_conv1d_layer(channels, kernel_size, 1) for _ in dilations])
self.convs2.apply(init_weights)
def forward(self, x, x_mask=None):
for c1, c2 in zip(self.convs1, self.convs2):
xt = torch.nn.functional.leaky_relu(x, LRELU_SLOPE)
xt = apply_mask(xt, x_mask)
xt = torch.nn.functional.leaky_relu(c1(xt), LRELU_SLOPE)
xt = apply_mask(xt, x_mask)
xt = c2(xt)
x = xt + x
return apply_mask(x, x_mask)
def remove_weight_norm(self):
for conv in self.convs1 + self.convs2:
remove_weight_norm(conv)
class ResBlock1(ResBlockBase):
def __init__(self, channels, kernel_size=3, dilation=(1, 3, 5)):
super(ResBlock1, self).__init__(channels, kernel_size, dilation)
class ResBlock2(ResBlockBase):
def __init__(self, channels, kernel_size=3, dilation=(1, 3)):
super(ResBlock2, self).__init__(channels, kernel_size, dilation)
class Log(torch.nn.Module):
def forward(self, x, x_mask, reverse=False, **kwargs):
if not reverse:
y = torch.log(torch.clamp_min(x, 1e-5)) * x_mask
logdet = torch.sum(-y, [1, 2])
return y, logdet
else:
x = torch.exp(x) * x_mask
return x
class Flip(torch.nn.Module):
def forward(self, x, *args, reverse=False, **kwargs):
x = torch.flip(x, [1])
if not reverse:
logdet = torch.zeros(x.size(0)).to(dtype=x.dtype, device=x.device)
return x, logdet
else: return x
class ElementwiseAffine(torch.nn.Module):
def __init__(self, channels):
super().__init__()
self.channels = channels
self.m = torch.nn.Parameter(torch.zeros(channels, 1))
self.logs = torch.nn.Parameter(torch.zeros(channels, 1))
def forward(self, x, x_mask, reverse=False, **kwargs):
if not reverse:
y = self.m + torch.exp(self.logs) * x
y = y * x_mask
logdet = torch.sum(self.logs * x_mask, [1, 2])
return y, logdet
else:
x = (x - self.m) * torch.exp(-self.logs) * x_mask
return x
class ResidualCouplingBlock(torch.nn.Module):
def __init__(self, channels, hidden_channels, kernel_size, dilation_rate, n_layers, n_flows=4, gin_channels=0):
super(ResidualCouplingBlock, self).__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 = torch.nn.ModuleList()
for i in range(n_flows):
self.flows.append(ResidualCouplingLayer(channels, hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=gin_channels, mean_only=True))
self.flows.append(Flip())
def forward(self, x: torch.Tensor, x_mask: torch.Tensor, g: Optional[torch.Tensor] = 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.forward(x, x_mask, g=g, reverse=reverse)
return x
def remove_weight_norm(self):
for i in range(self.n_flows):
self.flows[i * 2].remove_weight_norm()
def __prepare_scriptable__(self):
for i in range(self.n_flows):
for hook in self.flows[i * 2]._forward_pre_hooks.values():
if (hook.__module__ == "torch.nn.utils.parametrizations.weight_norm" and hook.__class__.__name__ == "WeightNorm"): torch.nn.utils.remove_weight_norm(self.flows[i * 2])
return self
class ResidualCouplingLayer(torch.nn.Module):
def __init__(self, channels, hidden_channels, kernel_size, dilation_rate, n_layers, p_dropout=0, gin_channels=0, mean_only=False):
assert channels % 2 == 0, "Channels/2"
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.half_channels = channels // 2
self.mean_only = mean_only
self.pre = torch.nn.Conv1d(self.half_channels, hidden_channels, 1)
self.enc = WaveNet(hidden_channels, kernel_size, dilation_rate, n_layers, p_dropout=p_dropout, gin_channels=gin_channels)
self.post = torch.nn.Conv1d(hidden_channels, self.half_channels * (2 - mean_only), 1)
self.post.weight.data.zero_()
self.post.bias.data.zero_()
def forward(self, x, x_mask, g=None, reverse=False):
x0, x1 = torch.split(x, [self.half_channels] * 2, 1)
h = self.pre(x0) * x_mask
h = self.enc(h, x_mask, g=g)
stats = self.post(h) * x_mask
if not self.mean_only: m, logs = torch.split(stats, [self.half_channels] * 2, 1)
else:
m = stats
logs = torch.zeros_like(m)
if not reverse:
x1 = m + x1 * torch.exp(logs) * x_mask
x = torch.cat([x0, x1], 1)
logdet = torch.sum(logs, [1, 2])
return x, logdet
else:
x1 = (x1 - m) * torch.exp(-logs) * x_mask
x = torch.cat([x0, x1], 1)
return x
def remove_weight_norm(self):
self.enc.remove_weight_norm() |