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728 lines
24 KiB
Python
728 lines
24 KiB
Python
import math
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import typing
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import torch
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from torch import nn
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from torch.nn import Conv1d, Conv2d, ConvTranspose1d
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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 attentions, commons, modules, monotonic_align
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from .commons import get_padding, init_weights
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class StochasticDurationPredictor(nn.Module):
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def __init__(
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self,
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in_channels: int,
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filter_channels: int,
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kernel_size: int,
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p_dropout: float,
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n_flows: int = 4,
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gin_channels: int = 0,
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):
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super().__init__()
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filter_channels = in_channels # it needs to be removed from future version.
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self.in_channels = in_channels
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self.filter_channels = filter_channels
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self.kernel_size = kernel_size
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self.p_dropout = p_dropout
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self.n_flows = n_flows
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self.gin_channels = gin_channels
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self.log_flow = modules.Log()
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self.flows = nn.ModuleList()
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self.flows.append(modules.ElementwiseAffine(2))
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for i in range(n_flows):
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self.flows.append(
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modules.ConvFlow(2, filter_channels, kernel_size, n_layers=3)
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)
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self.flows.append(modules.Flip())
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self.post_pre = nn.Conv1d(1, filter_channels, 1)
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self.post_proj = nn.Conv1d(filter_channels, filter_channels, 1)
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self.post_convs = modules.DDSConv(
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filter_channels, kernel_size, n_layers=3, p_dropout=p_dropout
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)
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self.post_flows = nn.ModuleList()
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self.post_flows.append(modules.ElementwiseAffine(2))
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for i in range(4):
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self.post_flows.append(
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modules.ConvFlow(2, filter_channels, kernel_size, n_layers=3)
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)
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self.post_flows.append(modules.Flip())
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self.pre = nn.Conv1d(in_channels, filter_channels, 1)
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self.proj = nn.Conv1d(filter_channels, filter_channels, 1)
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self.convs = modules.DDSConv(
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filter_channels, kernel_size, n_layers=3, p_dropout=p_dropout
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)
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if gin_channels != 0:
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self.cond = nn.Conv1d(gin_channels, filter_channels, 1)
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def forward(self, x, x_mask, w=None, g=None, reverse=False, noise_scale=1.0):
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x = torch.detach(x)
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x = self.pre(x)
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if g is not None:
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g = torch.detach(g)
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x = x + self.cond(g)
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x = self.convs(x, x_mask)
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x = self.proj(x) * x_mask
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if not reverse:
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flows = self.flows
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assert w is not None
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logdet_tot_q = 0
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h_w = self.post_pre(w)
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h_w = self.post_convs(h_w, x_mask)
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h_w = self.post_proj(h_w) * x_mask
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e_q = torch.randn(w.size(0), 2, w.size(2)).type_as(x) * x_mask
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z_q = e_q
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for flow in self.post_flows:
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z_q, logdet_q = flow(z_q, x_mask, g=(x + h_w))
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logdet_tot_q += logdet_q
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z_u, z1 = torch.split(z_q, [1, 1], 1)
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u = torch.sigmoid(z_u) * x_mask
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z0 = (w - u) * x_mask
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logdet_tot_q += torch.sum(
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(F.logsigmoid(z_u) + F.logsigmoid(-z_u)) * x_mask, [1, 2]
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)
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logq = (
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torch.sum(-0.5 * (math.log(2 * math.pi) + (e_q**2)) * x_mask, [1, 2])
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- logdet_tot_q
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)
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logdet_tot = 0
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z0, logdet = self.log_flow(z0, x_mask)
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logdet_tot += logdet
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z = torch.cat([z0, z1], 1)
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for flow in flows:
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z, logdet = flow(z, x_mask, g=x, reverse=reverse)
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logdet_tot = logdet_tot + logdet
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nll = (
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torch.sum(0.5 * (math.log(2 * math.pi) + (z**2)) * x_mask, [1, 2])
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- logdet_tot
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)
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return nll + logq # [b]
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else:
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flows = list(reversed(self.flows))
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flows = flows[:-2] + [flows[-1]] # remove a useless vflow
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z = torch.randn(x.size(0), 2, x.size(2)).type_as(x) * noise_scale
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for flow in flows:
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z = flow(z, x_mask, g=x, reverse=reverse)
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z0, z1 = torch.split(z, [1, 1], 1)
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logw = z0
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return logw
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class DurationPredictor(nn.Module):
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def __init__(
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self,
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in_channels: int,
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filter_channels: int,
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kernel_size: int,
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p_dropout: float,
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gin_channels: int = 0,
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):
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super().__init__()
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self.in_channels = in_channels
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self.filter_channels = filter_channels
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self.kernel_size = kernel_size
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self.p_dropout = p_dropout
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self.gin_channels = gin_channels
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self.drop = nn.Dropout(p_dropout)
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self.conv_1 = nn.Conv1d(
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in_channels, filter_channels, kernel_size, padding=kernel_size // 2
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)
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self.norm_1 = modules.LayerNorm(filter_channels)
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self.conv_2 = nn.Conv1d(
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filter_channels, filter_channels, kernel_size, padding=kernel_size // 2
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)
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self.norm_2 = modules.LayerNorm(filter_channels)
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self.proj = nn.Conv1d(filter_channels, 1, 1)
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if gin_channels != 0:
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self.cond = nn.Conv1d(gin_channels, in_channels, 1)
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def forward(self, x, x_mask, g=None):
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x = torch.detach(x)
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if g is not None:
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g = torch.detach(g)
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x = x + self.cond(g)
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x = self.conv_1(x * x_mask)
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x = torch.relu(x)
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x = self.norm_1(x)
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x = self.drop(x)
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x = self.conv_2(x * x_mask)
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x = torch.relu(x)
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x = self.norm_2(x)
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x = self.drop(x)
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x = self.proj(x * x_mask)
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return x * x_mask
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class TextEncoder(nn.Module):
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def __init__(
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self,
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n_vocab: int,
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out_channels: int,
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hidden_channels: int,
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filter_channels: int,
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n_heads: int,
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n_layers: int,
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kernel_size: int,
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p_dropout: float,
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):
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super().__init__()
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self.n_vocab = n_vocab
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self.out_channels = out_channels
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self.hidden_channels = hidden_channels
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self.filter_channels = filter_channels
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self.n_heads = n_heads
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self.n_layers = n_layers
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self.kernel_size = kernel_size
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self.p_dropout = p_dropout
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self.emb = nn.Embedding(n_vocab, hidden_channels)
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nn.init.normal_(self.emb.weight, 0.0, hidden_channels**-0.5)
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self.encoder = attentions.Encoder(
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hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout
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)
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self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
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def forward(self, x, x_lengths):
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x = self.emb(x) * math.sqrt(self.hidden_channels) # [b, t, h]
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x = torch.transpose(x, 1, -1) # [b, h, t]
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x_mask = torch.unsqueeze(
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commons.sequence_mask(x_lengths, x.size(2)), 1
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).type_as(x)
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x = self.encoder(x * x_mask, x_mask)
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stats = self.proj(x) * x_mask
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m, logs = torch.split(stats, self.out_channels, dim=1)
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return x, m, logs, x_mask
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class ResidualCouplingBlock(nn.Module):
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def __init__(
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self,
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channels: int,
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hidden_channels: int,
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kernel_size: int,
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dilation_rate: int,
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n_layers: int,
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n_flows: int = 4,
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gin_channels: int = 0,
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):
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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.n_flows = n_flows
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self.gin_channels = gin_channels
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self.flows = nn.ModuleList()
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for i in range(n_flows):
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self.flows.append(
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modules.ResidualCouplingLayer(
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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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gin_channels=gin_channels,
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mean_only=True,
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)
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)
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self.flows.append(modules.Flip())
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def forward(self, x, x_mask, g=None, reverse=False):
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if not reverse:
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for flow in self.flows:
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x, _ = flow(x, x_mask, g=g, reverse=reverse)
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else:
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for flow in reversed(self.flows):
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x = flow(x, x_mask, g=g, reverse=reverse)
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return x
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class PosteriorEncoder(nn.Module):
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def __init__(
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self,
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in_channels: int,
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out_channels: int,
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hidden_channels: int,
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kernel_size: int,
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dilation_rate: int,
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n_layers: int,
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gin_channels: int = 0,
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):
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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.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.pre = nn.Conv1d(in_channels, hidden_channels, 1)
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self.enc = modules.WN(
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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=gin_channels,
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)
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self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
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def forward(self, x, x_lengths, g=None):
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x_mask = torch.unsqueeze(
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commons.sequence_mask(x_lengths, x.size(2)), 1
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).type_as(x)
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x = self.pre(x) * x_mask
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x = self.enc(x, x_mask, g=g)
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stats = self.proj(x) * x_mask
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m, logs = torch.split(stats, self.out_channels, dim=1)
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z = (m + torch.randn_like(m) * torch.exp(logs)) * x_mask
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return z, m, logs, x_mask
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class Generator(torch.nn.Module):
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def __init__(
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self,
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initial_channel: int,
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resblock: typing.Optional[str],
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resblock_kernel_sizes: typing.Tuple[int, ...],
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resblock_dilation_sizes: typing.Tuple[typing.Tuple[int, ...], ...],
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upsample_rates: typing.Tuple[int, ...],
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upsample_initial_channel: int,
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upsample_kernel_sizes: typing.Tuple[int, ...],
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gin_channels: int = 0,
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):
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super(Generator, self).__init__()
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self.num_kernels = len(resblock_kernel_sizes)
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self.num_upsamples = len(upsample_rates)
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self.conv_pre = Conv1d(
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initial_channel, upsample_initial_channel, 7, 1, padding=3
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)
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resblock_module = modules.ResBlock1 if resblock == "1" else modules.ResBlock2
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self.ups = nn.ModuleList()
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for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
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self.ups.append(
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weight_norm(
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ConvTranspose1d(
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upsample_initial_channel // (2**i),
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upsample_initial_channel // (2 ** (i + 1)),
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k,
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u,
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padding=(k - u) // 2,
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)
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)
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)
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self.resblocks = nn.ModuleList()
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for i in range(len(self.ups)):
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ch = upsample_initial_channel // (2 ** (i + 1))
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for j, (k, d) in enumerate(
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zip(resblock_kernel_sizes, resblock_dilation_sizes)
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):
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self.resblocks.append(resblock_module(ch, k, d))
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self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False)
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self.ups.apply(init_weights)
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if gin_channels != 0:
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self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1)
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def forward(self, x, g=None):
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x = self.conv_pre(x)
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if g is not None:
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x = x + self.cond(g)
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for i in range(self.num_upsamples):
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x = F.leaky_relu(x, modules.LRELU_SLOPE)
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x = self.ups[i](x)
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xs = None
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for j in range(self.num_kernels):
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if xs is None:
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xs = self.resblocks[i * self.num_kernels + j](x)
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else:
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xs += self.resblocks[i * self.num_kernels + j](x)
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x = xs / self.num_kernels
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x = F.leaky_relu(x)
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x = self.conv_post(x)
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x = torch.tanh(x)
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return x
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def remove_weight_norm(self):
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print("Removing weight norm...")
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for l in self.ups:
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remove_weight_norm(l)
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for l in self.resblocks:
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l.remove_weight_norm()
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class DiscriminatorP(torch.nn.Module):
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def __init__(
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self,
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period: int,
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kernel_size: int = 5,
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stride: int = 3,
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use_spectral_norm: bool = False,
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):
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super(DiscriminatorP, self).__init__()
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self.period = period
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self.use_spectral_norm = use_spectral_norm
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norm_f = weight_norm if not use_spectral_norm else spectral_norm
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self.convs = nn.ModuleList(
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[
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norm_f(
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Conv2d(
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1,
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32,
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(kernel_size, 1),
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(stride, 1),
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padding=(get_padding(kernel_size, 1), 0),
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)
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),
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norm_f(
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Conv2d(
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32,
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128,
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(kernel_size, 1),
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(stride, 1),
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padding=(get_padding(kernel_size, 1), 0),
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)
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),
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norm_f(
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Conv2d(
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128,
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512,
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(kernel_size, 1),
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(stride, 1),
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padding=(get_padding(kernel_size, 1), 0),
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)
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),
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norm_f(
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Conv2d(
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512,
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1024,
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(kernel_size, 1),
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(stride, 1),
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padding=(get_padding(kernel_size, 1), 0),
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)
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),
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norm_f(
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Conv2d(
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1024,
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1024,
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(kernel_size, 1),
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1,
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padding=(get_padding(kernel_size, 1), 0),
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)
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),
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]
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)
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self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0)))
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def forward(self, x):
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fmap = []
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# 1d to 2d
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b, c, t = x.shape
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if t % self.period != 0: # pad first
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n_pad = self.period - (t % self.period)
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x = F.pad(x, (0, n_pad), "reflect")
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t = t + n_pad
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x = x.view(b, c, t // self.period, self.period)
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for l in self.convs:
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x = l(x)
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x = F.leaky_relu(x, modules.LRELU_SLOPE)
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fmap.append(x)
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x = self.conv_post(x)
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fmap.append(x)
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x = torch.flatten(x, 1, -1)
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return x, fmap
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class DiscriminatorS(torch.nn.Module):
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def __init__(self, use_spectral_norm=False):
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super(DiscriminatorS, self).__init__()
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norm_f = spectral_norm if use_spectral_norm else weight_norm
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self.convs = nn.ModuleList(
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[
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norm_f(Conv1d(1, 16, 15, 1, padding=7)),
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norm_f(Conv1d(16, 64, 41, 4, groups=4, padding=20)),
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norm_f(Conv1d(64, 256, 41, 4, groups=16, padding=20)),
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norm_f(Conv1d(256, 1024, 41, 4, groups=64, padding=20)),
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norm_f(Conv1d(1024, 1024, 41, 4, groups=256, padding=20)),
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norm_f(Conv1d(1024, 1024, 5, 1, padding=2)),
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]
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)
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self.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1))
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def forward(self, x):
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fmap = []
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for l in self.convs:
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x = l(x)
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x = F.leaky_relu(x, modules.LRELU_SLOPE)
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fmap.append(x)
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x = self.conv_post(x)
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fmap.append(x)
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x = torch.flatten(x, 1, -1)
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return x, fmap
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class MultiPeriodDiscriminator(torch.nn.Module):
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def __init__(self, use_spectral_norm=False):
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super(MultiPeriodDiscriminator, self).__init__()
|
|
periods = [2, 3, 5, 7, 11]
|
|
|
|
discs = [DiscriminatorS(use_spectral_norm=use_spectral_norm)]
|
|
discs = discs + [
|
|
DiscriminatorP(i, use_spectral_norm=use_spectral_norm) for i in periods
|
|
]
|
|
self.discriminators = nn.ModuleList(discs)
|
|
|
|
def forward(self, y, y_hat):
|
|
y_d_rs = []
|
|
y_d_gs = []
|
|
fmap_rs = []
|
|
fmap_gs = []
|
|
for i, d in enumerate(self.discriminators):
|
|
y_d_r, fmap_r = d(y)
|
|
y_d_g, fmap_g = d(y_hat)
|
|
y_d_rs.append(y_d_r)
|
|
y_d_gs.append(y_d_g)
|
|
fmap_rs.append(fmap_r)
|
|
fmap_gs.append(fmap_g)
|
|
|
|
return y_d_rs, y_d_gs, fmap_rs, fmap_gs
|
|
|
|
|
|
class SynthesizerTrn(nn.Module):
|
|
"""
|
|
Synthesizer for Training
|
|
"""
|
|
|
|
def __init__(
|
|
self,
|
|
n_vocab: int,
|
|
spec_channels: int,
|
|
segment_size: int,
|
|
inter_channels: int,
|
|
hidden_channels: int,
|
|
filter_channels: int,
|
|
n_heads: int,
|
|
n_layers: int,
|
|
kernel_size: int,
|
|
p_dropout: float,
|
|
resblock: str,
|
|
resblock_kernel_sizes: typing.Tuple[int, ...],
|
|
resblock_dilation_sizes: typing.Tuple[typing.Tuple[int, ...], ...],
|
|
upsample_rates: typing.Tuple[int, ...],
|
|
upsample_initial_channel: int,
|
|
upsample_kernel_sizes: typing.Tuple[int, ...],
|
|
n_speakers: int = 1,
|
|
gin_channels: int = 0,
|
|
use_sdp: bool = True,
|
|
):
|
|
|
|
super().__init__()
|
|
self.n_vocab = n_vocab
|
|
self.spec_channels = spec_channels
|
|
self.inter_channels = inter_channels
|
|
self.hidden_channels = hidden_channels
|
|
self.filter_channels = filter_channels
|
|
self.n_heads = n_heads
|
|
self.n_layers = n_layers
|
|
self.kernel_size = kernel_size
|
|
self.p_dropout = p_dropout
|
|
self.resblock = resblock
|
|
self.resblock_kernel_sizes = resblock_kernel_sizes
|
|
self.resblock_dilation_sizes = resblock_dilation_sizes
|
|
self.upsample_rates = upsample_rates
|
|
self.upsample_initial_channel = upsample_initial_channel
|
|
self.upsample_kernel_sizes = upsample_kernel_sizes
|
|
self.segment_size = segment_size
|
|
self.n_speakers = n_speakers
|
|
self.gin_channels = gin_channels
|
|
|
|
self.use_sdp = use_sdp
|
|
|
|
self.enc_p = TextEncoder(
|
|
n_vocab,
|
|
inter_channels,
|
|
hidden_channels,
|
|
filter_channels,
|
|
n_heads,
|
|
n_layers,
|
|
kernel_size,
|
|
p_dropout,
|
|
)
|
|
self.dec = Generator(
|
|
inter_channels,
|
|
resblock,
|
|
resblock_kernel_sizes,
|
|
resblock_dilation_sizes,
|
|
upsample_rates,
|
|
upsample_initial_channel,
|
|
upsample_kernel_sizes,
|
|
gin_channels=gin_channels,
|
|
)
|
|
self.enc_q = PosteriorEncoder(
|
|
spec_channels,
|
|
inter_channels,
|
|
hidden_channels,
|
|
5,
|
|
1,
|
|
16,
|
|
gin_channels=gin_channels,
|
|
)
|
|
self.flow = ResidualCouplingBlock(
|
|
inter_channels, hidden_channels, 5, 1, 4, gin_channels=gin_channels
|
|
)
|
|
|
|
if use_sdp:
|
|
self.dp = StochasticDurationPredictor(
|
|
hidden_channels, 192, 3, 0.5, 4, gin_channels=gin_channels
|
|
)
|
|
else:
|
|
self.dp = DurationPredictor(
|
|
hidden_channels, 256, 3, 0.5, gin_channels=gin_channels
|
|
)
|
|
|
|
if n_speakers > 1:
|
|
self.emb_g = nn.Embedding(n_speakers, gin_channels)
|
|
|
|
def forward(self, x, x_lengths, y, y_lengths, sid=None):
|
|
|
|
x, m_p, logs_p, x_mask = self.enc_p(x, x_lengths)
|
|
if self.n_speakers > 1:
|
|
g = self.emb_g(sid).unsqueeze(-1) # [b, h, 1]
|
|
else:
|
|
g = None
|
|
|
|
z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g)
|
|
z_p = self.flow(z, y_mask, g=g)
|
|
|
|
with torch.no_grad():
|
|
# negative cross-entropy
|
|
s_p_sq_r = torch.exp(-2 * logs_p) # [b, d, t]
|
|
neg_cent1 = torch.sum(
|
|
-0.5 * math.log(2 * math.pi) - logs_p, [1], keepdim=True
|
|
) # [b, 1, t_s]
|
|
neg_cent2 = torch.matmul(
|
|
-0.5 * (z_p**2).transpose(1, 2), s_p_sq_r
|
|
) # [b, t_t, d] x [b, d, t_s] = [b, t_t, t_s]
|
|
neg_cent3 = torch.matmul(
|
|
z_p.transpose(1, 2), (m_p * s_p_sq_r)
|
|
) # [b, t_t, d] x [b, d, t_s] = [b, t_t, t_s]
|
|
neg_cent4 = torch.sum(
|
|
-0.5 * (m_p**2) * s_p_sq_r, [1], keepdim=True
|
|
) # [b, 1, t_s]
|
|
neg_cent = neg_cent1 + neg_cent2 + neg_cent3 + neg_cent4
|
|
|
|
attn_mask = torch.unsqueeze(x_mask, 2) * torch.unsqueeze(y_mask, -1)
|
|
attn = (
|
|
monotonic_align.maximum_path(neg_cent, attn_mask.squeeze(1))
|
|
.unsqueeze(1)
|
|
.detach()
|
|
)
|
|
|
|
w = attn.sum(2)
|
|
if self.use_sdp:
|
|
l_length = self.dp(x, x_mask, w, g=g)
|
|
l_length = l_length / torch.sum(x_mask)
|
|
else:
|
|
logw_ = torch.log(w + 1e-6) * x_mask
|
|
logw = self.dp(x, x_mask, g=g)
|
|
l_length = torch.sum((logw - logw_) ** 2, [1, 2]) / torch.sum(
|
|
x_mask
|
|
) # for averaging
|
|
|
|
# expand prior
|
|
m_p = torch.matmul(attn.squeeze(1), m_p.transpose(1, 2)).transpose(1, 2)
|
|
logs_p = torch.matmul(attn.squeeze(1), logs_p.transpose(1, 2)).transpose(1, 2)
|
|
|
|
z_slice, ids_slice = commons.rand_slice_segments(
|
|
z, y_lengths, self.segment_size
|
|
)
|
|
o = self.dec(z_slice, g=g)
|
|
return (
|
|
o,
|
|
l_length,
|
|
attn,
|
|
ids_slice,
|
|
x_mask,
|
|
y_mask,
|
|
(z, z_p, m_p, logs_p, m_q, logs_q),
|
|
)
|
|
|
|
def infer(
|
|
self,
|
|
x,
|
|
x_lengths,
|
|
sid=None,
|
|
noise_scale=0.667,
|
|
length_scale=1,
|
|
noise_scale_w=0.8,
|
|
max_len=None,
|
|
):
|
|
x, m_p, logs_p, x_mask = self.enc_p(x, x_lengths)
|
|
if self.n_speakers > 1:
|
|
g = self.emb_g(sid).unsqueeze(-1) # [b, h, 1]
|
|
else:
|
|
g = None
|
|
|
|
if self.use_sdp:
|
|
logw = self.dp(x, x_mask, g=g, reverse=True, noise_scale=noise_scale_w)
|
|
else:
|
|
logw = self.dp(x, x_mask, g=g)
|
|
w = torch.exp(logw) * x_mask * length_scale
|
|
w_ceil = torch.ceil(w)
|
|
y_lengths = torch.clamp_min(torch.sum(w_ceil, [1, 2]), 1).long()
|
|
y_mask = torch.unsqueeze(
|
|
commons.sequence_mask(y_lengths, y_lengths.max()), 1
|
|
).type_as(x_mask)
|
|
attn_mask = torch.unsqueeze(x_mask, 2) * torch.unsqueeze(y_mask, -1)
|
|
attn = commons.generate_path(w_ceil, attn_mask)
|
|
|
|
m_p = torch.matmul(attn.squeeze(1), m_p.transpose(1, 2)).transpose(
|
|
1, 2
|
|
) # [b, t', t], [b, t, d] -> [b, d, t']
|
|
logs_p = torch.matmul(attn.squeeze(1), logs_p.transpose(1, 2)).transpose(
|
|
1, 2
|
|
) # [b, t', t], [b, t, d] -> [b, d, t']
|
|
|
|
z_p = m_p + torch.randn_like(m_p) * torch.exp(logs_p) * noise_scale
|
|
z = self.flow(z_p, y_mask, g=g, reverse=True)
|
|
o = self.dec((z * y_mask)[:, :, :max_len], g=g)
|
|
|
|
return o, attn, y_mask, (z, z_p, m_p, logs_p)
|
|
|
|
def voice_conversion(self, y, y_lengths, sid_src, sid_tgt):
|
|
assert self.n_speakers > 1, "n_speakers have to be larger than 1."
|
|
g_src = self.emb_g(sid_src).unsqueeze(-1)
|
|
g_tgt = self.emb_g(sid_tgt).unsqueeze(-1)
|
|
z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g_src)
|
|
z_p = self.flow(z, y_mask, g=g_src)
|
|
z_hat = self.flow(z_p, y_mask, g=g_tgt, reverse=True)
|
|
o_hat = self.dec(z_hat * y_mask, g=g_tgt)
|
|
return o_hat, y_mask, (z, z_p, z_hat)
|