File size: 5,817 Bytes
e4d8df5 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 |
import os
import sys
import torch
from torch.nn.utils import remove_weight_norm
from torch.nn.utils.parametrizations import weight_norm
sys.path.append(os.getcwd())
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):
x = c2(apply_mask(torch.nn.functional.leaky_relu(c1(apply_mask(torch.nn.functional.leaky_relu(x, LRELU_SLOPE), x_mask)), LRELU_SLOPE), x_mask)) + x
return apply_mask(x, x_mask)
def remove_weight_norm(self):
for conv in self.convs1 + self.convs2:
remove_weight_norm(conv)
class ResBlock(ResBlockBase):
def __init__(self, channels, kernel_size=3, dilation=(1, 3, 5)):
super(ResBlock, 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
return y, torch.sum(-y, [1, 2])
else: return torch.exp(x) * x_mask
class Flip(torch.nn.Module):
def forward(self, x, *args, reverse=False, **kwargs):
x = torch.flip(x, [1])
if not reverse: return x, torch.zeros(x.size(0)).to(dtype=x.dtype, device=x.device)
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: return ((self.m + torch.exp(self.logs) * x) * x_mask), torch.sum(self.logs * x_mask, [1, 2])
else: return (x - self.m) * torch.exp(-self.logs) * x_mask
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 _ 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, 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.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)
stats = self.post(self.enc((self.pre(x0) * x_mask), x_mask, g=g)) * 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: return torch.cat([x0, (m + x1 * torch.exp(logs) * x_mask)], 1), torch.sum(logs, [1, 2])
else: return torch.cat([x0, ((x1 - m) * torch.exp(-logs) * x_mask)], 1)
def remove_weight_norm(self):
self.enc.remove_weight_norm() |