| import math | |
| from typing import Optional, 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 | |
| from torch.nn.utils.parametrizations import weight_norm | |
| import commons | |
| from commons import get_padding, init_weights | |
| import numpy as np | |
| DEFAULT_MIN_BIN_WIDTH = 1e-3 | |
| DEFAULT_MIN_BIN_HEIGHT = 1e-3 | |
| DEFAULT_MIN_DERIVATIVE = 1e-3 | |
| def piecewise_rational_quadratic_transform( | |
| inputs, | |
| unnormalized_widths, | |
| unnormalized_heights, | |
| unnormalized_derivatives, | |
| inverse=False, | |
| tails=None, | |
| tail_bound=1.0, | |
| min_bin_width=DEFAULT_MIN_BIN_WIDTH, | |
| min_bin_height=DEFAULT_MIN_BIN_HEIGHT, | |
| min_derivative=DEFAULT_MIN_DERIVATIVE, | |
| ): | |
| if tails is None: | |
| spline_fn = rational_quadratic_spline | |
| spline_kwargs = {} | |
| else: | |
| spline_fn = unconstrained_rational_quadratic_spline | |
| spline_kwargs = {"tails": tails, "tail_bound": tail_bound} | |
| outputs, logabsdet = spline_fn( | |
| inputs=inputs, | |
| unnormalized_widths=unnormalized_widths, | |
| unnormalized_heights=unnormalized_heights, | |
| unnormalized_derivatives=unnormalized_derivatives, | |
| inverse=inverse, | |
| min_bin_width=min_bin_width, | |
| min_bin_height=min_bin_height, | |
| min_derivative=min_derivative, | |
| **spline_kwargs | |
| ) | |
| return outputs, logabsdet | |
| def searchsorted(bin_locations, inputs, eps=1e-6): | |
| bin_locations[..., -1] += eps | |
| return torch.sum(inputs[..., None] >= bin_locations, dim=-1) - 1 | |
| def unconstrained_rational_quadratic_spline( | |
| inputs, | |
| unnormalized_widths, | |
| unnormalized_heights, | |
| unnormalized_derivatives, | |
| inverse=False, | |
| tails="linear", | |
| tail_bound=1.0, | |
| min_bin_width=DEFAULT_MIN_BIN_WIDTH, | |
| min_bin_height=DEFAULT_MIN_BIN_HEIGHT, | |
| min_derivative=DEFAULT_MIN_DERIVATIVE, | |
| ): | |
| inside_interval_mask = (inputs >= -tail_bound) & (inputs <= tail_bound) | |
| outside_interval_mask = ~inside_interval_mask | |
| outputs = torch.zeros_like(inputs) | |
| logabsdet = torch.zeros_like(inputs) | |
| if tails == "linear": | |
| unnormalized_derivatives = F.pad(unnormalized_derivatives, pad=(1, 1)) | |
| constant = np.log(np.exp(1 - min_derivative) - 1) | |
| unnormalized_derivatives[..., 0] = constant | |
| unnormalized_derivatives[..., -1] = constant | |
| outputs[outside_interval_mask] = inputs[outside_interval_mask] | |
| logabsdet[outside_interval_mask] = 0 | |
| else: | |
| raise RuntimeError("{} Đuôi Không Được Thực Hiện.".format(tails)) | |
| ( | |
| outputs[inside_interval_mask], | |
| logabsdet[inside_interval_mask], | |
| ) = rational_quadratic_spline( | |
| inputs=inputs[inside_interval_mask], | |
| unnormalized_widths=unnormalized_widths[inside_interval_mask, :], | |
| unnormalized_heights=unnormalized_heights[inside_interval_mask, :], | |
| unnormalized_derivatives=unnormalized_derivatives[inside_interval_mask, :], | |
| inverse=inverse, | |
| left=-tail_bound, | |
| right=tail_bound, | |
| bottom=-tail_bound, | |
| top=tail_bound, | |
| min_bin_width=min_bin_width, | |
| min_bin_height=min_bin_height, | |
| min_derivative=min_derivative, | |
| ) | |
| return outputs, logabsdet | |
| def rational_quadratic_spline( | |
| inputs, | |
| unnormalized_widths, | |
| unnormalized_heights, | |
| unnormalized_derivatives, | |
| inverse=False, | |
| left=0.0, | |
| right=1.0, | |
| bottom=0.0, | |
| top=1.0, | |
| min_bin_width=DEFAULT_MIN_BIN_WIDTH, | |
| min_bin_height=DEFAULT_MIN_BIN_HEIGHT, | |
| min_derivative=DEFAULT_MIN_DERIVATIVE, | |
| ): | |
| if torch.min(inputs) < left or torch.max(inputs) > right: | |
| raise ValueError("Đầu Vào Của Một Phép Biến Đổi Không Nằm Trong Miền Của Nó") | |
| num_bins = unnormalized_widths.shape[-1] | |
| if min_bin_width * num_bins > 1.0: | |
| raise ValueError("Chiều Rộng Ngăn Tối Thiểu Quá Lớn So Với Số Ngăn") | |
| if min_bin_height * num_bins > 1.0: | |
| raise ValueError("Chiều Cao Ngăn Tối Thiểu Quá Lớn So Với Số lLượng Ngăn") | |
| widths = F.softmax(unnormalized_widths, dim=-1) | |
| widths = min_bin_width + (1 - min_bin_width * num_bins) * widths | |
| cumwidths = torch.cumsum(widths, dim=-1) | |
| cumwidths = F.pad(cumwidths, pad=(1, 0), mode="constant", value=0.0) | |
| cumwidths = (right - left) * cumwidths + left | |
| cumwidths[..., 0] = left | |
| cumwidths[..., -1] = right | |
| widths = cumwidths[..., 1:] - cumwidths[..., :-1] | |
| derivatives = min_derivative + F.softplus(unnormalized_derivatives) | |
| heights = F.softmax(unnormalized_heights, dim=-1) | |
| heights = min_bin_height + (1 - min_bin_height * num_bins) * heights | |
| cumheights = torch.cumsum(heights, dim=-1) | |
| cumheights = F.pad(cumheights, pad=(1, 0), mode="constant", value=0.0) | |
| cumheights = (top - bottom) * cumheights + bottom | |
| cumheights[..., 0] = bottom | |
| cumheights[..., -1] = top | |
| heights = cumheights[..., 1:] - cumheights[..., :-1] | |
| if inverse: | |
| bin_idx = searchsorted(cumheights, inputs)[..., None] | |
| else: | |
| bin_idx = searchsorted(cumwidths, inputs)[..., None] | |
| input_cumwidths = cumwidths.gather(-1, bin_idx)[..., 0] | |
| input_bin_widths = widths.gather(-1, bin_idx)[..., 0] | |
| input_cumheights = cumheights.gather(-1, bin_idx)[..., 0] | |
| delta = heights / widths | |
| input_delta = delta.gather(-1, bin_idx)[..., 0] | |
| input_derivatives = derivatives.gather(-1, bin_idx)[..., 0] | |
| input_derivatives_plus_one = derivatives[..., 1:].gather(-1, bin_idx)[..., 0] | |
| input_heights = heights.gather(-1, bin_idx)[..., 0] | |
| if inverse: | |
| a = (inputs - input_cumheights) * ( | |
| input_derivatives + input_derivatives_plus_one - 2 * input_delta | |
| ) + input_heights * (input_delta - input_derivatives) | |
| b = input_heights * input_derivatives - (inputs - input_cumheights) * ( | |
| input_derivatives + input_derivatives_plus_one - 2 * input_delta | |
| ) | |
| c = -input_delta * (inputs - input_cumheights) | |
| discriminant = b.pow(2) - 4 * a * c | |
| assert (discriminant >= 0).all() | |
| root = (2 * c) / (-b - torch.sqrt(discriminant)) | |
| outputs = root * input_bin_widths + input_cumwidths | |
| theta_one_minus_theta = root * (1 - root) | |
| denominator = input_delta + ( | |
| (input_derivatives + input_derivatives_plus_one - 2 * input_delta) | |
| * theta_one_minus_theta | |
| ) | |
| derivative_numerator = input_delta.pow(2) * ( | |
| input_derivatives_plus_one * root.pow(2) | |
| + 2 * input_delta * theta_one_minus_theta | |
| + input_derivatives * (1 - root).pow(2) | |
| ) | |
| logabsdet = torch.log(derivative_numerator) - 2 * torch.log(denominator) | |
| return outputs, -logabsdet | |
| else: | |
| theta = (inputs - input_cumwidths) / input_bin_widths | |
| theta_one_minus_theta = theta * (1 - theta) | |
| numerator = input_heights * ( | |
| input_delta * theta.pow(2) + input_derivatives * theta_one_minus_theta | |
| ) | |
| denominator = input_delta + ( | |
| (input_derivatives + input_derivatives_plus_one - 2 * input_delta) | |
| * theta_one_minus_theta | |
| ) | |
| outputs = input_cumheights + numerator / denominator | |
| derivative_numerator = input_delta.pow(2) * ( | |
| input_derivatives_plus_one * theta.pow(2) | |
| + 2 * input_delta * theta_one_minus_theta | |
| + input_derivatives * (1 - theta).pow(2) | |
| ) | |
| logabsdet = torch.log(derivative_numerator) - 2 * torch.log(denominator) | |
| return outputs, logabsdet | |
| LRELU_SLOPE = 0.1 | |
| class LayerNorm(nn.Module): | |
| def __init__(self, channels, eps=1e-5): | |
| super(LayerNorm, self).__init__() | |
| self.channels = channels | |
| self.eps = eps | |
| self.gamma = nn.Parameter(torch.ones(channels)) | |
| self.beta = nn.Parameter(torch.zeros(channels)) | |
| def forward(self, x): | |
| x = x.transpose(1, -1) | |
| x = F.layer_norm(x, (self.channels,), self.gamma, self.beta, self.eps) | |
| return x.transpose(1, -1) | |
| class ConvReluNorm(nn.Module): | |
| def __init__( | |
| self, | |
| in_channels, | |
| hidden_channels, | |
| out_channels, | |
| kernel_size, | |
| n_layers, | |
| p_dropout, | |
| ): | |
| super(ConvReluNorm, self).__init__() | |
| self.in_channels = in_channels | |
| self.hidden_channels = hidden_channels | |
| self.out_channels = out_channels | |
| self.kernel_size = kernel_size | |
| self.n_layers = n_layers | |
| self.p_dropout = float(p_dropout) | |
| assert n_layers > 1, "Số Lớp Phải Lớn Hơn 0." | |
| self.conv_layers = nn.ModuleList() | |
| self.norm_layers = nn.ModuleList() | |
| self.conv_layers.append( | |
| nn.Conv1d( | |
| in_channels, hidden_channels, kernel_size, padding=kernel_size // 2 | |
| ) | |
| ) | |
| self.norm_layers.append(LayerNorm(hidden_channels)) | |
| self.relu_drop = nn.Sequential(nn.ReLU(), nn.Dropout(float(p_dropout))) | |
| for _ in range(n_layers - 1): | |
| self.conv_layers.append( | |
| nn.Conv1d( | |
| hidden_channels, | |
| hidden_channels, | |
| kernel_size, | |
| padding=kernel_size // 2, | |
| ) | |
| ) | |
| self.norm_layers.append(LayerNorm(hidden_channels)) | |
| self.proj = nn.Conv1d(hidden_channels, out_channels, 1) | |
| self.proj.weight.data.zero_() | |
| self.proj.bias.data.zero_() | |
| def forward(self, x, x_mask): | |
| x_org = x | |
| for i in range(self.n_layers): | |
| x = self.conv_layers[i](x * x_mask) | |
| x = self.norm_layers[i](x) | |
| x = self.relu_drop(x) | |
| x = x_org + self.proj(x) | |
| return x * x_mask | |
| class DDSConv(nn.Module): | |
| def __init__(self, channels, kernel_size, n_layers, p_dropout=0.0): | |
| super(DDSConv, self).__init__() | |
| self.channels = channels | |
| self.kernel_size = kernel_size | |
| self.n_layers = n_layers | |
| self.p_dropout = float(p_dropout) | |
| self.drop = nn.Dropout(float(p_dropout)) | |
| self.convs_sep = nn.ModuleList() | |
| self.convs_1x1 = nn.ModuleList() | |
| self.norms_1 = nn.ModuleList() | |
| self.norms_2 = nn.ModuleList() | |
| for i in range(n_layers): | |
| dilation = kernel_size**i | |
| padding = (kernel_size * dilation - dilation) // 2 | |
| self.convs_sep.append( | |
| nn.Conv1d( | |
| channels, | |
| channels, | |
| kernel_size, | |
| groups=channels, | |
| dilation=dilation, | |
| padding=padding, | |
| ) | |
| ) | |
| self.convs_1x1.append(nn.Conv1d(channels, channels, 1)) | |
| self.norms_1.append(LayerNorm(channels)) | |
| self.norms_2.append(LayerNorm(channels)) | |
| def forward(self, x, x_mask, g: Optional[torch.Tensor] = None): | |
| if g is not None: | |
| x = x + g | |
| for i in range(self.n_layers): | |
| y = self.convs_sep[i](x * x_mask) | |
| y = self.norms_1[i](y) | |
| y = F.gelu(y) | |
| y = self.convs_1x1[i](y) | |
| y = self.norms_2[i](y) | |
| y = F.gelu(y) | |
| y = self.drop(y) | |
| x = x + y | |
| return x * x_mask | |
| class WN(torch.nn.Module): | |
| def __init__( | |
| self, | |
| hidden_channels, | |
| kernel_size, | |
| dilation_rate, | |
| n_layers, | |
| gin_channels=0, | |
| p_dropout=0, | |
| ): | |
| super(WN, self).__init__() | |
| assert kernel_size % 2 == 1 | |
| self.hidden_channels = hidden_channels | |
| self.kernel_size = (kernel_size,) | |
| self.dilation_rate = dilation_rate | |
| self.n_layers = n_layers | |
| self.gin_channels = gin_channels | |
| self.p_dropout = float(p_dropout) | |
| self.in_layers = torch.nn.ModuleList() | |
| self.res_skip_layers = torch.nn.ModuleList() | |
| self.drop = nn.Dropout(float(p_dropout)) | |
| if gin_channels != 0: | |
| cond_layer = torch.nn.Conv1d( | |
| gin_channels, 2 * hidden_channels * n_layers, 1 | |
| ) | |
| self.cond_layer = torch.nn.utils.parametrizations.weight_norm(cond_layer, name="weight") | |
| for i in range(n_layers): | |
| dilation = dilation_rate**i | |
| padding = int((kernel_size * dilation - dilation) / 2) | |
| in_layer = torch.nn.Conv1d( | |
| hidden_channels, | |
| 2 * hidden_channels, | |
| kernel_size, | |
| dilation=dilation, | |
| padding=padding, | |
| ) | |
| in_layer = torch.nn.utils.parametrizations.weight_norm(in_layer, name="weight") | |
| self.in_layers.append(in_layer) | |
| if i < n_layers - 1: | |
| res_skip_channels = 2 * hidden_channels | |
| else: | |
| res_skip_channels = hidden_channels | |
| res_skip_layer = torch.nn.Conv1d(hidden_channels, res_skip_channels, 1) | |
| res_skip_layer = torch.nn.utils.parametrizations.weight_norm(res_skip_layer, name="weight") | |
| self.res_skip_layers.append(res_skip_layer) | |
| def forward( | |
| self, x: torch.Tensor, x_mask: torch.Tensor, g: Optional[torch.Tensor] = None | |
| ): | |
| output = torch.zeros_like(x) | |
| n_channels_tensor = torch.IntTensor([self.hidden_channels]) | |
| if g is not None: | |
| g = self.cond_layer(g) | |
| for i, (in_layer, res_skip_layer) in enumerate( | |
| zip(self.in_layers, self.res_skip_layers) | |
| ): | |
| x_in = in_layer(x) | |
| if g is not None: | |
| cond_offset = i * 2 * self.hidden_channels | |
| g_l = g[:, cond_offset : cond_offset + 2 * self.hidden_channels, :] | |
| else: | |
| g_l = torch.zeros_like(x_in) | |
| acts = commons.fused_add_tanh_sigmoid_multiply(x_in, g_l, n_channels_tensor) | |
| acts = self.drop(acts) | |
| res_skip_acts = res_skip_layer(acts) | |
| if i < self.n_layers - 1: | |
| res_acts = res_skip_acts[:, : self.hidden_channels, :] | |
| x = (x + res_acts) * x_mask | |
| output = output + res_skip_acts[:, self.hidden_channels :, :] | |
| else: | |
| output = output + res_skip_acts | |
| return output * x_mask | |
| def remove_weight_norm(self): | |
| if self.gin_channels != 0: | |
| torch.nn.utils.remove_weight_norm(self.cond_layer) | |
| for l in self.in_layers: | |
| torch.nn.utils.remove_weight_norm(l) | |
| for l in self.res_skip_layers: | |
| torch.nn.utils.remove_weight_norm(l) | |
| def __prepare_scriptable__(self): | |
| if self.gin_channels != 0: | |
| for hook in self.cond_layer._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.cond_layer) | |
| for l in self.in_layers: | |
| for hook in l._forward_pre_hooks.values(): | |
| if ( | |
| hook.__module__ == "torch.nn.utils.parametrizations.weight_norm" | |
| and hook.__class__.__name__ == "WeightNorm" | |
| ): | |
| torch.nn.utils.remove_weight_norm(l) | |
| for l in self.res_skip_layers: | |
| for hook in l._forward_pre_hooks.values(): | |
| if ( | |
| hook.__module__ == "torch.nn.utils.parametrizations.weight_norm" | |
| and hook.__class__.__name__ == "WeightNorm" | |
| ): | |
| torch.nn.utils.remove_weight_norm(l) | |
| return self | |
| class ResBlock1(torch.nn.Module): | |
| def __init__(self, channels, kernel_size=3, dilation=(1, 3, 5)): | |
| super(ResBlock1, self).__init__() | |
| self.convs1 = nn.ModuleList( | |
| [ | |
| weight_norm( | |
| Conv1d( | |
| channels, | |
| channels, | |
| kernel_size, | |
| 1, | |
| dilation=dilation[0], | |
| padding=get_padding(kernel_size, dilation[0]), | |
| ) | |
| ), | |
| weight_norm( | |
| Conv1d( | |
| channels, | |
| channels, | |
| kernel_size, | |
| 1, | |
| dilation=dilation[1], | |
| padding=get_padding(kernel_size, dilation[1]), | |
| ) | |
| ), | |
| weight_norm( | |
| Conv1d( | |
| channels, | |
| channels, | |
| kernel_size, | |
| 1, | |
| dilation=dilation[2], | |
| padding=get_padding(kernel_size, dilation[2]), | |
| ) | |
| ), | |
| ] | |
| ) | |
| self.convs1.apply(init_weights) | |
| self.convs2 = nn.ModuleList( | |
| [ | |
| weight_norm( | |
| Conv1d( | |
| channels, | |
| channels, | |
| kernel_size, | |
| 1, | |
| dilation=1, | |
| padding=get_padding(kernel_size, 1), | |
| ) | |
| ), | |
| weight_norm( | |
| Conv1d( | |
| channels, | |
| channels, | |
| kernel_size, | |
| 1, | |
| dilation=1, | |
| padding=get_padding(kernel_size, 1), | |
| ) | |
| ), | |
| weight_norm( | |
| Conv1d( | |
| channels, | |
| channels, | |
| kernel_size, | |
| 1, | |
| dilation=1, | |
| padding=get_padding(kernel_size, 1), | |
| ) | |
| ), | |
| ] | |
| ) | |
| self.convs2.apply(init_weights) | |
| self.lrelu_slope = LRELU_SLOPE | |
| def forward(self, x: torch.Tensor, x_mask: Optional[torch.Tensor] = None): | |
| 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.parametrizations.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.parametrizations.weight_norm" | |
| and hook.__class__.__name__ == "WeightNorm" | |
| ): | |
| torch.nn.utils.remove_weight_norm(l) | |
| return self | |
| class ResBlock2(torch.nn.Module): | |
| def __init__(self, channels, kernel_size=3, dilation=(1, 3)): | |
| super(ResBlock2, self).__init__() | |
| self.convs = nn.ModuleList( | |
| [ | |
| weight_norm( | |
| Conv1d( | |
| channels, | |
| channels, | |
| kernel_size, | |
| 1, | |
| dilation=dilation[0], | |
| padding=get_padding(kernel_size, dilation[0]), | |
| ) | |
| ), | |
| weight_norm( | |
| Conv1d( | |
| channels, | |
| channels, | |
| kernel_size, | |
| 1, | |
| dilation=dilation[1], | |
| padding=get_padding(kernel_size, dilation[1]), | |
| ) | |
| ), | |
| ] | |
| ) | |
| self.convs.apply(init_weights) | |
| self.lrelu_slope = LRELU_SLOPE | |
| def forward(self, x, x_mask: Optional[torch.Tensor] = None): | |
| 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.parametrizations.weight_norm" | |
| and hook.__class__.__name__ == "WeightNorm" | |
| ): | |
| torch.nn.utils.remove_weight_norm(l) | |
| return self | |
| class Log(nn.Module): | |
| def forward( | |
| self, | |
| x: torch.Tensor, | |
| x_mask: torch.Tensor, | |
| g: Optional[torch.Tensor] = None, | |
| reverse: bool = False, | |
| ) -> Tuple[torch.Tensor, Optional[torch.Tensor]]: | |
| 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: 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) | |
| class ElementwiseAffine(nn.Module): | |
| def __init__(self, channels): | |
| super(ElementwiseAffine, self).__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, "Kênh Phải Chia Cho 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 forward( | |
| self, | |
| x: torch.Tensor, | |
| x_mask: torch.Tensor, | |
| g: Optional[torch.Tensor] = None, | |
| reverse: bool = 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, 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.parametrizations.weight_norm" | |
| and hook.__class__.__name__ == "WeightNorm" | |
| ): | |
| torch.nn.utils.remove_weight_norm(self.enc) | |
| return self | |
| class ConvFlow(nn.Module): | |
| def __init__( | |
| self, | |
| in_channels, | |
| filter_channels, | |
| kernel_size, | |
| n_layers, | |
| num_bins=10, | |
| tail_bound=5.0, | |
| ): | |
| super(ConvFlow, self).__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: torch.Tensor, | |
| x_mask: torch.Tensor, | |
| g: Optional[torch.Tensor] = 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) | |
| 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 | |