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import copy |
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import math |
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import numpy as np |
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import scipy |
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import torch |
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from torch import nn |
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from torch.nn import functional as F |
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from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d |
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from torch.nn.utils import weight_norm, remove_weight_norm |
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import commons |
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from commons import init_weights, get_padding |
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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__(self, in_channels, hidden_channels, out_channels, kernel_size, n_layers, p_dropout): |
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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(nn.Conv1d(in_channels, hidden_channels, kernel_size, padding=kernel_size//2)) |
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self.norm_layers.append(LayerNorm(hidden_channels)) |
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self.relu_drop = nn.Sequential( |
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nn.ReLU(), |
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nn.Dropout(p_dropout)) |
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for _ in range(n_layers-1): |
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self.conv_layers.append(nn.Conv1d(hidden_channels, hidden_channels, kernel_size, padding=kernel_size//2)) |
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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.): |
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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(nn.Conv1d(channels, channels, kernel_size, |
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groups=channels, dilation=dilation, padding=padding |
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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__(self, hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=0, p_dropout=0): |
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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(gin_channels, 2*hidden_channels*n_layers, 1) |
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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(hidden_channels, 2*hidden_channels, kernel_size, |
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dilation=dilation, padding=padding) |
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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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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( |
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x_in, |
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g_l, |
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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 ResBlock1(torch.nn.Module): |
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def __init__(self, channels, kernel_size=3, dilation=(1, 3, 5)): |
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super(ResBlock1, self).__init__() |
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self.convs1 = nn.ModuleList([ |
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weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0], |
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padding=get_padding(kernel_size, dilation[0]))), |
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weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1], |
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padding=get_padding(kernel_size, dilation[1]))), |
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weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[2], |
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padding=get_padding(kernel_size, dilation[2]))) |
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]) |
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self.convs1.apply(init_weights) |
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self.convs2 = nn.ModuleList([ |
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weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1, |
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padding=get_padding(kernel_size, 1))), |
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weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1, |
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padding=get_padding(kernel_size, 1))), |
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weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1, |
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padding=get_padding(kernel_size, 1))) |
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]) |
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self.convs2.apply(init_weights) |
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def forward(self, x, x_mask=None): |
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for c1, c2 in zip(self.convs1, self.convs2): |
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xt = F.leaky_relu(x, LRELU_SLOPE) |
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if x_mask is not None: |
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xt = xt * x_mask |
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xt = c1(xt) |
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xt = F.leaky_relu(xt, LRELU_SLOPE) |
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if x_mask is not None: |
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xt = xt * x_mask |
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xt = c2(xt) |
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x = xt + x |
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if x_mask is not None: |
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x = x * x_mask |
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return x |
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def remove_weight_norm(self): |
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for l in self.convs1: |
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remove_weight_norm(l) |
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for l in self.convs2: |
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remove_weight_norm(l) |
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class ResBlock2(torch.nn.Module): |
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def __init__(self, channels, kernel_size=3, dilation=(1, 3)): |
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super(ResBlock2, self).__init__() |
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self.convs = nn.ModuleList([ |
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weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0], |
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padding=get_padding(kernel_size, dilation[0]))), |
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weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1], |
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padding=get_padding(kernel_size, dilation[1]))) |
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]) |
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self.convs.apply(init_weights) |
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def forward(self, x, x_mask=None): |
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for c in self.convs: |
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xt = F.leaky_relu(x, LRELU_SLOPE) |
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if x_mask is not None: |
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xt = xt * x_mask |
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xt = c(xt) |
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x = xt + x |
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if x_mask is not None: |
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x = x * x_mask |
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return x |
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def remove_weight_norm(self): |
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for l in self.convs: |
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remove_weight_norm(l) |
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class Log(nn.Module): |
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def forward(self, x, x_mask, reverse=False, **kwargs): |
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if not reverse: |
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y = torch.log(torch.clamp_min(x, 1e-5)) * x_mask |
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logdet = torch.sum(-y, [1, 2]) |
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return y, logdet |
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else: |
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x = torch.exp(x) * x_mask |
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return x |
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class Flip(nn.Module): |
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def forward(self, x, *args, reverse=False, **kwargs): |
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x = torch.flip(x, [1]) |
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if not reverse: |
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logdet = torch.zeros(x.size(0)).to(dtype=x.dtype, device=x.device) |
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return x, logdet |
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else: |
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return x |
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class ElementwiseAffine(nn.Module): |
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def __init__(self, channels): |
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super().__init__() |
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self.channels = channels |
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self.m = nn.Parameter(torch.zeros(channels,1)) |
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self.logs = nn.Parameter(torch.zeros(channels,1)) |
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def forward(self, x, x_mask, reverse=False, **kwargs): |
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if not reverse: |
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y = self.m + torch.exp(self.logs) * x |
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y = y * x_mask |
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logdet = torch.sum(self.logs * x_mask, [1,2]) |
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return y, logdet |
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else: |
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x = (x - self.m) * torch.exp(-self.logs) * x_mask |
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return x |
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class ResidualCouplingLayer(nn.Module): |
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def __init__(self, |
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channels, |
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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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p_dropout=0, |
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gin_channels=0, |
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mean_only=False): |
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assert channels % 2 == 0, "channels should be divisible by 2" |
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super().__init__() |
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self.channels = channels |
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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.half_channels = channels // 2 |
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self.mean_only = mean_only |
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self.pre = nn.Conv1d(self.half_channels, hidden_channels, 1) |
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self.enc = WN(hidden_channels, kernel_size, dilation_rate, n_layers, p_dropout=p_dropout, gin_channels=gin_channels) |
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self.post = nn.Conv1d(hidden_channels, self.half_channels * (2 - mean_only), 1) |
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self.post.weight.data.zero_() |
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self.post.bias.data.zero_() |
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def forward(self, x, x_mask, g=None, reverse=False): |
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x0, x1 = torch.split(x, [self.half_channels]*2, 1) |
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h = self.pre(x0) * x_mask |
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h = self.enc(h, x_mask, g=g) |
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stats = self.post(h) * x_mask |
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if not self.mean_only: |
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m, logs = torch.split(stats, [self.half_channels]*2, 1) |
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else: |
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m = stats |
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logs = torch.zeros_like(m) |
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if not reverse: |
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x1 = m + x1 * torch.exp(logs) * x_mask |
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x = torch.cat([x0, x1], 1) |
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logdet = torch.sum(logs, [1,2]) |
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return x, logdet |
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else: |
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x1 = (x1 - m) * torch.exp(-logs) * x_mask |
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x = torch.cat([x0, x1], 1) |
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return x |
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