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from typing import Optional, List, Tuple
import torch
from torch import nn
from torch.nn import Conv1d
from torch.nn import functional as F
from torch.nn.utils import remove_weight_norm, weight_norm
from .norms import WN
from .utils import (
get_padding,
call_weight_data_normal_if_Conv,
)
LRELU_SLOPE = 0.1
class ResBlock1(torch.nn.Module):
def __init__(
self,
channels: int,
kernel_size: int = 3,
dilation: List[int] = (1, 3, 5),
):
super(ResBlock1, self).__init__()
self.convs1 = nn.ModuleList()
for d in dilation:
self.convs1.append(
weight_norm(
Conv1d(
channels,
channels,
kernel_size,
1,
dilation=d,
padding=get_padding(kernel_size, d),
)
),
)
self.convs1.apply(call_weight_data_normal_if_Conv)
self.convs2 = nn.ModuleList()
for _ in dilation:
self.convs2.append(
weight_norm(
Conv1d(
channels,
channels,
kernel_size,
1,
dilation=1,
padding=get_padding(kernel_size, 1),
)
),
)
self.convs2.apply(call_weight_data_normal_if_Conv)
self.lrelu_slope = LRELU_SLOPE
def __call__(
self,
x: torch.Tensor,
x_mask: Optional[torch.Tensor] = None,
) -> torch.Tensor:
return super().__call__(x, x_mask=x_mask)
def forward(
self,
x: torch.Tensor,
x_mask: Optional[torch.Tensor] = None,
) -> torch.Tensor:
for c1, c2 in zip(self.convs1, self.convs2):
xt = F.leaky_relu(x, self.lrelu_slope)
if x_mask is not None:
xt = xt * x_mask
xt = c1(xt)
xt = F.leaky_relu(xt, self.lrelu_slope)
if x_mask is not None:
xt = xt * x_mask
xt = c2(xt)
x = xt + x
if x_mask is not None:
x = x * x_mask
return x
def remove_weight_norm(self):
for l in self.convs1:
remove_weight_norm(l)
for l in self.convs2:
remove_weight_norm(l)
def __prepare_scriptable__(self):
for l in self.convs1:
for hook in l._forward_pre_hooks.values():
if (
hook.__module__ == "torch.nn.utils.weight_norm"
and hook.__class__.__name__ == "WeightNorm"
):
torch.nn.utils.remove_weight_norm(l)
for l in self.convs2:
for hook in l._forward_pre_hooks.values():
if (
hook.__module__ == "torch.nn.utils.weight_norm"
and hook.__class__.__name__ == "WeightNorm"
):
torch.nn.utils.remove_weight_norm(l)
return self
class ResBlock2(torch.nn.Module):
"""
Actually this module is not used currently
because all configs specified "resblock": "1"
"""
def __init__(
self,
channels: int,
kernel_size=3,
dilation: List[int] = (1, 3),
):
super(ResBlock2, self).__init__()
self.convs = nn.ModuleList()
for d in dilation:
self.convs.append(
weight_norm(
Conv1d(
channels,
channels,
kernel_size,
1,
dilation=d,
padding=get_padding(kernel_size, d),
)
),
)
self.convs.apply(call_weight_data_normal_if_Conv)
self.lrelu_slope = LRELU_SLOPE
def __call__(
self,
x: torch.Tensor,
x_mask: Optional[torch.Tensor] = None,
) -> torch.Tensor:
return super().__call__(x, x_mask=x_mask)
def forward(
self,
x: torch.Tensor,
x_mask: Optional[torch.Tensor] = None,
) -> torch.Tensor:
for c in self.convs:
xt = F.leaky_relu(x, self.lrelu_slope)
if x_mask is not None:
xt = xt * x_mask
xt = c(xt)
x = xt + x
if x_mask is not None:
x = x * x_mask
return x
def remove_weight_norm(self):
for l in self.convs:
remove_weight_norm(l)
def __prepare_scriptable__(self):
for l in self.convs:
for hook in l._forward_pre_hooks.values():
if (
hook.__module__ == "torch.nn.utils.weight_norm"
and hook.__class__.__name__ == "WeightNorm"
):
torch.nn.utils.remove_weight_norm(l)
return self
class ResidualCouplingLayer(nn.Module):
def __init__(
self,
channels: int,
hidden_channels: int,
kernel_size: int,
dilation_rate: int,
n_layers: int,
p_dropout: int = 0,
gin_channels: int = 0,
mean_only: bool = False,
):
assert channels % 2 == 0, "channels should be divisible by 2"
super(ResidualCouplingLayer, 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.half_channels = channels // 2
self.mean_only = mean_only
self.pre = nn.Conv1d(self.half_channels, hidden_channels, 1)
self.enc = WN(
hidden_channels,
kernel_size,
dilation_rate,
n_layers,
p_dropout=float(p_dropout),
gin_channels=gin_channels,
)
self.post = nn.Conv1d(hidden_channels, self.half_channels * (2 - mean_only), 1)
self.post.weight.data.zero_()
self.post.bias.data.zero_()
def __call__(
self,
x: torch.Tensor,
x_mask: torch.Tensor,
g: Optional[torch.Tensor] = None,
reverse: bool = False,
) -> Tuple[torch.Tensor, torch.Tensor]:
return super().__call__(x, x_mask, g=g, reverse=reverse)
def forward(
self,
x: torch.Tensor,
x_mask: torch.Tensor,
g: Optional[torch.Tensor] = None,
reverse: bool = False,
) -> Tuple[torch.Tensor, torch.Tensor]:
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
x1 = (x1 - m) * torch.exp(-logs) * x_mask
x = torch.cat([x0, x1], 1)
return x, torch.zeros([1])
def remove_weight_norm(self):
self.enc.remove_weight_norm()
def __prepare_scriptable__(self):
for hook in self.enc._forward_pre_hooks.values():
if (
hook.__module__ == "torch.nn.utils.weight_norm"
and hook.__class__.__name__ == "WeightNorm"
):
torch.nn.utils.remove_weight_norm(self.enc)
return self
class ResidualCouplingBlock(nn.Module):
class Flip(nn.Module):
"""
torch.jit.script() Compiled functions
can't take variable number of arguments or
use keyword-only arguments with defaults
"""
def forward(
self,
x: torch.Tensor,
x_mask: torch.Tensor,
g: Optional[torch.Tensor] = None,
reverse: bool = False,
) -> Tuple[torch.Tensor, Optional[torch.Tensor]]:
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, torch.zeros([1], device=x.device)
def __init__(
self,
channels: int,
hidden_channels: int,
kernel_size: int,
dilation_rate: int,
n_layers: int,
n_flows: int = 4,
gin_channels: int = 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 = nn.ModuleList()
for _ 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(self.Flip())
def __call__(
self,
x: torch.Tensor,
x_mask: torch.Tensor,
g: Optional[torch.Tensor] = None,
reverse: bool = False,
) -> torch.Tensor:
return super().__call__(x, x_mask, g=g, reverse=reverse)
def forward(
self,
x: torch.Tensor,
x_mask: torch.Tensor,
g: Optional[torch.Tensor] = None,
reverse: bool = False,
) -> torch.Tensor:
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.weight_norm"
and hook.__class__.__name__ == "WeightNorm"
):
torch.nn.utils.remove_weight_norm(self.flows[i * 2])
return self
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