627 lines
22 KiB
Python
627 lines
22 KiB
Python
import math
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import torch
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from torch import nn
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from torch.nn import Conv1d, Conv2d
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from torch.nn import functional as F
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from torch.nn.utils import remove_weight_norm, spectral_norm, weight_norm
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from . import commons, modules
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from .commons import get_padding, init_weights
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from .transforms import piecewise_rational_quadratic_transform
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LRELU_SLOPE = 0.1
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class LayerNorm(nn.Module):
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def __init__(self, channels, eps=1e-5):
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super().__init__()
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self.channels = channels
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self.eps = eps
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self.gamma = nn.Parameter(torch.ones(channels))
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self.beta = nn.Parameter(torch.zeros(channels))
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def forward(self, x):
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x = x.transpose(1, -1)
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x = F.layer_norm(x, (self.channels,), self.gamma, self.beta, self.eps)
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return x.transpose(1, -1)
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class ConvReluNorm(nn.Module):
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def __init__(
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self,
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in_channels,
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hidden_channels,
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out_channels,
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kernel_size,
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n_layers,
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p_dropout,
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):
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super().__init__()
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self.in_channels = in_channels
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self.hidden_channels = hidden_channels
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self.out_channels = out_channels
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self.kernel_size = kernel_size
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self.n_layers = n_layers
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self.p_dropout = p_dropout
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assert n_layers > 1, "Number of layers should be larger than 0."
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self.conv_layers = nn.ModuleList()
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self.norm_layers = nn.ModuleList()
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self.conv_layers.append(
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nn.Conv1d(
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in_channels, hidden_channels, kernel_size, padding=kernel_size // 2
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)
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)
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self.norm_layers.append(LayerNorm(hidden_channels))
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self.relu_drop = nn.Sequential(nn.ReLU(), nn.Dropout(p_dropout))
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for _ in range(n_layers - 1):
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self.conv_layers.append(
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nn.Conv1d(
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hidden_channels,
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hidden_channels,
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kernel_size,
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padding=kernel_size // 2,
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)
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)
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self.norm_layers.append(LayerNorm(hidden_channels))
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self.proj = nn.Conv1d(hidden_channels, out_channels, 1)
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self.proj.weight.data.zero_()
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self.proj.bias.data.zero_()
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def forward(self, x, x_mask):
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x_org = x
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for i in range(self.n_layers):
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x = self.conv_layers[i](x * x_mask)
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x = self.norm_layers[i](x)
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x = self.relu_drop(x)
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x = x_org + self.proj(x)
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return x * x_mask
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class DDSConv(nn.Module):
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"""
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Dialted and Depth-Separable Convolution
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"""
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def __init__(self, channels, kernel_size, n_layers, p_dropout=0.0):
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super().__init__()
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self.channels = channels
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self.kernel_size = kernel_size
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self.n_layers = n_layers
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self.p_dropout = p_dropout
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self.drop = nn.Dropout(p_dropout)
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self.convs_sep = nn.ModuleList()
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self.convs_1x1 = nn.ModuleList()
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self.norms_1 = nn.ModuleList()
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self.norms_2 = nn.ModuleList()
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for i in range(n_layers):
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dilation = kernel_size**i
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padding = (kernel_size * dilation - dilation) // 2
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self.convs_sep.append(
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nn.Conv1d(
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channels,
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channels,
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kernel_size,
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groups=channels,
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dilation=dilation,
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padding=padding,
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)
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)
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self.convs_1x1.append(nn.Conv1d(channels, channels, 1))
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self.norms_1.append(LayerNorm(channels))
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self.norms_2.append(LayerNorm(channels))
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def forward(self, x, x_mask, g=None):
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if g is not None:
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x = x + g
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for i in range(self.n_layers):
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y = self.convs_sep[i](x * x_mask)
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y = self.norms_1[i](y)
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y = F.gelu(y)
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y = self.convs_1x1[i](y)
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y = self.norms_2[i](y)
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y = F.gelu(y)
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y = self.drop(y)
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x = x + y
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return x * x_mask
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class WN(torch.nn.Module):
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def __init__(
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self,
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hidden_channels,
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kernel_size,
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dilation_rate,
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n_layers,
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gin_channels=0,
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p_dropout=0,
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):
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super(WN, self).__init__()
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assert kernel_size % 2 == 1
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self.hidden_channels = hidden_channels
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self.kernel_size = (kernel_size,)
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self.dilation_rate = dilation_rate
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self.n_layers = n_layers
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self.gin_channels = gin_channels
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self.p_dropout = p_dropout
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self.in_layers = torch.nn.ModuleList()
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self.res_skip_layers = torch.nn.ModuleList()
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self.drop = nn.Dropout(p_dropout)
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if gin_channels != 0:
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cond_layer = torch.nn.Conv1d(
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gin_channels, 2 * hidden_channels * n_layers, 1
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)
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self.cond_layer = torch.nn.utils.weight_norm(cond_layer, name="weight")
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for i in range(n_layers):
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dilation = dilation_rate**i
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padding = int((kernel_size * dilation - dilation) / 2)
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in_layer = torch.nn.Conv1d(
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hidden_channels,
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2 * hidden_channels,
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kernel_size,
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dilation=dilation,
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padding=padding,
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)
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in_layer = torch.nn.utils.weight_norm(in_layer, name="weight")
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self.in_layers.append(in_layer)
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# last one is not necessary
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if i < n_layers - 1:
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res_skip_channels = 2 * hidden_channels
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else:
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res_skip_channels = hidden_channels
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res_skip_layer = torch.nn.Conv1d(hidden_channels, res_skip_channels, 1)
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res_skip_layer = torch.nn.utils.weight_norm(res_skip_layer, name="weight")
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self.res_skip_layers.append(res_skip_layer)
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def forward(self, x, x_mask, g=None, **kwargs):
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output = torch.zeros_like(x)
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n_channels_tensor = torch.IntTensor([self.hidden_channels])
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if g is not None:
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g = self.cond_layer(g)
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for i in range(self.n_layers):
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x_in = self.in_layers[i](x)
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if g is not None:
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cond_offset = i * 2 * self.hidden_channels
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g_l = g[:, cond_offset : cond_offset + 2 * self.hidden_channels, :]
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else:
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g_l = torch.zeros_like(x_in)
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acts = commons.fused_add_tanh_sigmoid_multiply(x_in, g_l, n_channels_tensor)
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acts = self.drop(acts)
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res_skip_acts = self.res_skip_layers[i](acts)
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if i < self.n_layers - 1:
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res_acts = res_skip_acts[:, : self.hidden_channels, :]
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x = (x + res_acts) * x_mask
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output = output + res_skip_acts[:, self.hidden_channels :, :]
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else:
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output = output + res_skip_acts
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return output * x_mask
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def remove_weight_norm(self):
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if self.gin_channels != 0:
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torch.nn.utils.remove_weight_norm(self.cond_layer)
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for l in self.in_layers:
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torch.nn.utils.remove_weight_norm(l)
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for l in self.res_skip_layers:
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torch.nn.utils.remove_weight_norm(l)
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class DilatedCausalConv1d(nn.Module):
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def __init__(self, in_channels, out_channels, kernel_size=3, stride=1, groups=1, dilation=1, bias=True):
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super(DilatedCausalConv1d, self).__init__()
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self.kernel_size = kernel_size
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self.dilation = dilation
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self.stride = stride
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self.conv = weight_norm(nn.Conv1d(in_channels, out_channels, kernel_size, stride=stride, groups=groups, dilation=dilation, bias=bias))
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init_weights(self.conv)
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def forward(self, x):
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x = torch.flip(x, [2])
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x = F.pad(x, [0, (self.kernel_size - 1) * self.dilation], mode="constant", value=0.)
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size = x.shape[2] // self.stride
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x = self.conv(x)[:, :, :size]
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x = torch.flip(x, [2])
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return x
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def remove_weight_norm(self):
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remove_weight_norm(self.conv)
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class CausalConvTranspose1d(nn.Module):
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"""
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padding = 0, dilation = 1のとき
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Lout = (Lin - 1) * stride + kernel_rate * stride + output_padding
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Lout = Lin * stride + (kernel_rate - 1) * stride + output_padding
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output_paddingいらないね
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"""
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def __init__(self, in_channels, out_channels, kernel_rate=3, stride=1, groups=1):
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super(CausalConvTranspose1d, self).__init__()
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kernel_size = kernel_rate * stride
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self.trim_size = (kernel_rate - 1) * stride
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self.conv = weight_norm(nn.ConvTranspose1d(in_channels, out_channels, kernel_size, stride=stride, groups=groups))
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def forward(self, x):
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x = self.conv(x)
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return x[:, :, :-self.trim_size]
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def remove_weight_norm(self):
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remove_weight_norm(self.conv)
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class LoRALinear1d(nn.Module):
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def __init__(self, in_channels, out_channels, info_channels, r):
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super().__init__()
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self.in_channels = in_channels
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self.out_channels = out_channels
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self.info_channels = info_channels
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self.r = r
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self.main_fc = weight_norm(nn.Conv1d(in_channels, out_channels, 1))
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self.adapter_in = nn.Conv1d(info_channels, in_channels * r, 1)
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self.adapter_out = nn.Conv1d(info_channels, out_channels * r, 1)
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nn.init.normal_(self.adapter_in.weight.data, 0, 0.01)
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nn.init.constant_(self.adapter_out.weight.data, 1e-6)
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init_weights(self.main_fc)
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self.adapter_in = weight_norm(self.adapter_in)
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self.adapter_out = weight_norm(self.adapter_out)
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def forward(self, x, g):
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a_in = self.adapter_in(g).view(-1, self.in_channels, self.r)
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a_out = self.adapter_out(g).view(-1, self.r, self.out_channels)
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x = self.main_fc(x) + torch.einsum("brl,brc->bcl", torch.einsum("bcl,bcr->brl", x, a_in), a_out)
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return x
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def remove_weight_norm(self):
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remove_weight_norm(self.main_fc)
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remove_weight_norm(self.adapter_in)
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remove_weight_norm(self.adapter_out)
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class LoRALinear2d(nn.Module):
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def __init__(self, in_channels, out_channels, info_channels, r):
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super().__init__()
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self.in_channels = in_channels
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self.out_channels = out_channels
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self.info_channels = info_channels
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self.r = r
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self.main_fc = weight_norm(nn.Conv2d(in_channels, out_channels, (1, 1), (1, 1)))
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self.adapter_in = nn.Conv1d(info_channels, in_channels * r, 1)
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self.adapter_out = nn.Conv1d(info_channels, out_channels * r, 1)
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nn.init.normal_(self.adapter_in.weight.data, 0, 0.01)
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nn.init.constant_(self.adapter_out.weight.data, 1e-6)
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self.adapter_in = weight_norm(self.adapter_in)
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self.adapter_out = weight_norm(self.adapter_out)
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def forward(self, x, g):
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a_in = self.adapter_in(g).view(-1, self.in_channels, self.r)
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a_out = self.adapter_out(g).view(-1, self.r, self.out_channels)
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x = self.main_fc(x) + torch.einsum("brhw,brc->bchw", torch.einsum("bchw,bcr->brhw", x, a_in), a_out)
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return x
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def remove_weight_norm(self):
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remove_weight_norm(self.main_fc)
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remove_weight_norm(self.adapter_in)
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remove_weight_norm(self.adapter_out)
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class WaveConv1D(torch.nn.Module):
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def __init__(self, in_channels, out_channels, gin_channels, kernel_sizes, strides, dilations, extend_ratio, r, use_spectral_norm=False):
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super(WaveConv1D, self).__init__()
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norm_f = weight_norm if use_spectral_norm == False else spectral_norm
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inner_channels = int(in_channels * extend_ratio)
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self.convs = []
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# self.norms = []
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self.convs.append(LoRALinear1d(in_channels, inner_channels, gin_channels, r))
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for i, (k, s, d) in enumerate(zip(kernel_sizes, strides, dilations), start=1):
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self.convs.append(norm_f(Conv1d(inner_channels, inner_channels, k, s, dilation=d, groups=inner_channels, padding=get_padding(k, d))))
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if i < len(kernel_sizes):
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self.convs.append(norm_f(Conv1d(inner_channels, inner_channels, 1, 1)))
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else:
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self.convs.append(norm_f(Conv1d(inner_channels, out_channels, 1, 1)))
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self.convs = nn.ModuleList(self.convs)
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def forward(self, x, g, x_mask=None):
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for i, l in enumerate(self.convs):
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if i % 2:
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x_ = l(x)
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else:
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x_ = l(x, g)
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x = F.leaky_relu(x_, modules.LRELU_SLOPE)
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if x_mask is not None:
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x *= x_mask
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return x
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def remove_weight_norm(self):
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for i, c in enumerate(self.convs):
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if i % 2:
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remove_weight_norm(c)
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else:
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c.remove_weight_norm()
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class MBConv2d(torch.nn.Module):
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"""
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Causal MBConv2D
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"""
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def __init__(self, in_channels, out_channels, gin_channels, kernel_size, stride, extend_ratio, r, use_spectral_norm=False):
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super(MBConv2d, self).__init__()
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norm_f = weight_norm if use_spectral_norm == False else spectral_norm
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inner_channels = int(in_channels * extend_ratio)
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self.kernel_size = kernel_size
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self.pre_pointwise = LoRALinear2d(in_channels, inner_channels, gin_channels, r=r)
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self.depthwise = norm_f(Conv2d(inner_channels, inner_channels, kernel_size, stride, groups=inner_channels))
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self.post_pointwise = LoRALinear2d(inner_channels, out_channels, gin_channels, r=r)
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def forward(self, x, g):
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x = self.pre_pointwise(x, g)
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x = F.pad(x, [0, 0, self.kernel_size[0] - 1, 0], mode="constant")
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x = self.depthwise(x)
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x = self.post_pointwise(x, g)
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return x
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class ConvNext2d(torch.nn.Module):
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"""
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Causal ConvNext Block
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stride = 1 only
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"""
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def __init__(self, in_channels, out_channels, gin_channels, kernel_size, stride, extend_ratio, r, use_spectral_norm=False):
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super(ConvNext2d, self).__init__()
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norm_f = weight_norm if use_spectral_norm == False else spectral_norm
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inner_channels = int(in_channels * extend_ratio)
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self.kernel_size = kernel_size
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self.dwconv = norm_f(Conv2d(in_channels, in_channels, kernel_size, stride, groups=in_channels))
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self.pwconv1 = LoRALinear2d(in_channels, inner_channels, gin_channels, r=r)
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self.pwconv2 = LoRALinear2d(inner_channels, out_channels, gin_channels, r=r)
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self.act = nn.GELU()
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self.norm = LayerNorm(in_channels)
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def forward(self, x, g):
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x = F.pad(x, [0, 0, self.kernel_size[0] - 1, 0], mode="constant")
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x = self.dwconv(x)
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x = self.norm(x)
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x = self.pwconv1(x, g)
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x = F.leaky_relu(x, modules.LRELU_SLOPE)
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x = self.pwconv2(x, g)
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x = F.leaky_relu(x, modules.LRELU_SLOPE)
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return x
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def remove_weight_norm(self):
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remove_weight_norm(self.dwconv)
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class SqueezeExcitation1D(torch.nn.Module):
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def __init__(self, input_channels, squeeze_channels, gin_channels, use_spectral_norm=False):
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norm_f = weight_norm if use_spectral_norm == False else spectral_norm
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super(SqueezeExcitation1D, self).__init__()
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self.fc1 = LoRALinear1d(input_channels, squeeze_channels, gin_channels, 2)
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self.fc2 = LoRALinear1d(squeeze_channels, input_channels, gin_channels, 2)
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def _scale(self, x, x_mask, g):
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x_length = torch.sum(x_mask, dim=2, keepdim=True)
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x_length = torch.maximum(x_length, torch.ones_like(x_length))
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scale = torch.sum(x * x_mask, dim=2, keepdim=True) / x_length
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scale = self.fc1(scale, g)
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scale = F.leaky_relu(scale, modules.LRELU_SLOPE)
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scale = self.fc2(scale, g)
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return torch.sigmoid(scale)
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def forward(self, x, x_mask, g):
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scale = self._scale(x, x_mask, g)
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return scale * x
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def remove_weight_norm(self):
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self.fc1.remove_weight_norm()
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self.fc2.remove_weight_norm()
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class ResBlock1(torch.nn.Module):
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def __init__(self, in_channels, out_channels, gin_channels, kernel_sizes, strides, dilations, extend_ratio, r):
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super(ResBlock1, self).__init__()
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norm_f = weight_norm
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inner_channels = int(in_channels * extend_ratio)
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self.dconvs = nn.ModuleList()
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self.pconvs = nn.ModuleList()
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# self.ses = nn.ModuleList()
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self.norms = nn.ModuleList()
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self.init_conv = LoRALinear1d(in_channels, inner_channels, gin_channels, r)
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for i, (k, s, d) in enumerate(zip(kernel_sizes, strides, dilations)):
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self.norms.append(LayerNorm(inner_channels))
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self.dconvs.append(DilatedCausalConv1d(inner_channels, inner_channels, k, stride=s, dilation=d, groups=inner_channels))
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if i < len(kernel_sizes) - 1:
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self.pconvs.append(LoRALinear1d(inner_channels, inner_channels, gin_channels, r))
|
|
self.out_conv = LoRALinear1d(inner_channels, out_channels, gin_channels, r)
|
|
init_weights(self.init_conv)
|
|
init_weights(self.out_conv)
|
|
|
|
def forward(self, x, x_mask, g):
|
|
x *= x_mask
|
|
x = self.init_conv(x, g)
|
|
for i in range(len(self.dconvs)):
|
|
x *= x_mask
|
|
x = self.norms[i](x)
|
|
x_ = self.dconvs[i](x)
|
|
x_ = F.leaky_relu(x_, modules.LRELU_SLOPE)
|
|
if i < len(self.dconvs) - 1:
|
|
x = x + self.pconvs[i](x_, g)
|
|
x = self.out_conv(x_, g)
|
|
return x
|
|
|
|
def remove_weight_norm(self):
|
|
for c in self.dconvs:
|
|
c.remove_weight_norm()
|
|
for c in self.pconvs:
|
|
c.remove_weight_norm()
|
|
self.init_conv.remove_weight_norm()
|
|
self.out_conv.remove_weight_norm()
|
|
|
|
|
|
class Log(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(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(nn.Module):
|
|
def __init__(self, channels):
|
|
super().__init__()
|
|
self.channels = channels
|
|
self.m = nn.Parameter(torch.zeros(channels, 1))
|
|
self.logs = 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 ResidualCouplingLayer(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 should be divisible by 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 = nn.Conv1d(self.half_channels, hidden_channels, 1)
|
|
self.enc = WN(
|
|
hidden_channels,
|
|
kernel_size,
|
|
dilation_rate,
|
|
n_layers,
|
|
p_dropout=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 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()
|
|
|
|
|
|
class ConvFlow(nn.Module):
|
|
def __init__(
|
|
self,
|
|
in_channels,
|
|
filter_channels,
|
|
kernel_size,
|
|
n_layers,
|
|
num_bins=10,
|
|
tail_bound=5.0,
|
|
):
|
|
super().__init__()
|
|
self.in_channels = in_channels
|
|
self.filter_channels = filter_channels
|
|
self.kernel_size = kernel_size
|
|
self.n_layers = n_layers
|
|
self.num_bins = num_bins
|
|
self.tail_bound = tail_bound
|
|
self.half_channels = in_channels // 2
|
|
|
|
self.pre = nn.Conv1d(self.half_channels, filter_channels, 1)
|
|
self.convs = DDSConv(filter_channels, kernel_size, n_layers, p_dropout=0.0)
|
|
self.proj = nn.Conv1d(
|
|
filter_channels, self.half_channels * (num_bins * 3 - 1), 1
|
|
)
|
|
self.proj.weight.data.zero_()
|
|
self.proj.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)
|
|
h = self.convs(h, x_mask, g=g)
|
|
h = self.proj(h) * x_mask
|
|
|
|
b, c, t = x0.shape
|
|
h = h.reshape(b, c, -1, t).permute(0, 1, 3, 2) # [b, cx?, t] -> [b, c, t, ?]
|
|
|
|
unnormalized_widths = h[..., : self.num_bins] / math.sqrt(self.filter_channels)
|
|
unnormalized_heights = h[..., self.num_bins : 2 * self.num_bins] / math.sqrt(
|
|
self.filter_channels
|
|
)
|
|
unnormalized_derivatives = h[..., 2 * self.num_bins :]
|
|
|
|
x1, logabsdet = piecewise_rational_quadratic_transform(
|
|
x1,
|
|
unnormalized_widths,
|
|
unnormalized_heights,
|
|
unnormalized_derivatives,
|
|
inverse=reverse,
|
|
tails="linear",
|
|
tail_bound=self.tail_bound,
|
|
)
|
|
|
|
x = torch.cat([x0, x1], 1) * x_mask
|
|
logdet = torch.sum(logabsdet * x_mask, [1, 2])
|
|
if not reverse:
|
|
return x, logdet
|
|
else:
|
|
return x
|