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