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| import copy | |
| import math | |
| import numpy as np | |
| import scipy | |
| import torch | |
| from torch import nn | |
| from torch.nn import functional as F | |
| from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d | |
| from torch.nn.utils import weight_norm, remove_weight_norm | |
| import commons | |
| from commons import init_weights, get_padding | |
| from transforms import piecewise_rational_quadratic_transform | |
| LRELU_SLOPE = 0.1 | |
| class LayerNorm(nn.Module): | |
| def __init__(self, channels, eps=1e-5): | |
| super().__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().__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 = p_dropout | |
| assert n_layers > 1, "Number of layers should be larger than 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(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): | |
| """ | |
| Dialted and Depth-Separable Convolution | |
| """ | |
| def __init__(self, channels, kernel_size, n_layers, p_dropout=0.): | |
| super().__init__() | |
| self.channels = channels | |
| self.kernel_size = kernel_size | |
| self.n_layers = n_layers | |
| self.p_dropout = p_dropout | |
| self.drop = nn.Dropout(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=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 = p_dropout | |
| self.in_layers = torch.nn.ModuleList() | |
| self.res_skip_layers = torch.nn.ModuleList() | |
| self.drop = nn.Dropout(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.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.weight_norm(in_layer, name='weight') | |
| self.in_layers.append(in_layer) | |
| # last one is not necessary | |
| 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.weight_norm(res_skip_layer, name='weight') | |
| self.res_skip_layers.append(res_skip_layer) | |
| def forward(self, x, x_mask, g=None, **kwargs): | |
| 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 range(self.n_layers): | |
| x_in = self.in_layers[i](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 = self.res_skip_layers[i](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) | |
| 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) | |
| def forward(self, x, x_mask=None): | |
| for c1, c2 in zip(self.convs1, self.convs2): | |
| xt = F.leaky_relu(x, LRELU_SLOPE) | |
| if x_mask is not None: | |
| xt = xt * x_mask | |
| xt = c1(xt) | |
| xt = F.leaky_relu(xt, 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) | |
| 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) | |
| def forward(self, x, x_mask=None): | |
| for c in self.convs: | |
| xt = F.leaky_relu(x, 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) | |
| 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 | |
| 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.) | |
| 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 | |
| # modules from StarGANv2-VC | |
| class DownSample(nn.Module): | |
| def __init__(self, layer_type): | |
| super().__init__() | |
| self.layer_type = layer_type | |
| def forward(self, x): | |
| if self.layer_type == 'none': | |
| return x | |
| elif self.layer_type == 'timepreserve': | |
| return F.avg_pool2d(x, (2, 1)) | |
| elif self.layer_type == 'half': | |
| return F.avg_pool2d(x, 2) | |
| else: | |
| raise RuntimeError('Got unexpected donwsampletype %s, expected is [none, timepreserve, half]' % self.layer_type) | |
| class ResBlock3(nn.Module): | |
| def __init__(self, dim_in, dim_out, actv=nn.LeakyReLU(0.2), | |
| normalize=False, downsample='none'): | |
| super().__init__() | |
| self.actv = actv | |
| self.normalize = normalize | |
| self.downsample = DownSample(downsample) | |
| self.learned_sc = dim_in != dim_out | |
| self._build_weights(dim_in, dim_out) | |
| def _build_weights(self, dim_in, dim_out): | |
| self.conv1 = nn.Conv2d(dim_in, dim_in, 3, 1, 1) | |
| self.conv2 = nn.Conv2d(dim_in, dim_out, 3, 1, 1) | |
| if self.normalize: | |
| self.norm1 = nn.InstanceNorm2d(dim_in, affine=True) | |
| self.norm2 = nn.InstanceNorm2d(dim_in, affine=True) | |
| if self.learned_sc: | |
| self.conv1x1 = nn.Conv2d(dim_in, dim_out, 1, 1, 0, bias=False) | |
| def _shortcut(self, x): | |
| if self.learned_sc: | |
| x = self.conv1x1(x) | |
| if self.downsample: | |
| x = self.downsample(x) | |
| return x | |
| def _residual(self, x): | |
| if self.normalize: | |
| x = self.norm1(x) | |
| x = self.actv(x) | |
| x = self.conv1(x) | |
| x = self.downsample(x) | |
| if self.normalize: | |
| x = self.norm2(x) | |
| x = self.actv(x) | |
| x = self.conv2(x) | |
| return x | |
| def forward(self, x): | |
| x = self._shortcut(x) + self._residual(x) | |
| return x / math.sqrt(2) # unit variance | |