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import math |
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from typing import Tuple, Optional |
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import torch |
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from torch import nn |
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from .attentions import MultiHeadAttention, FFN |
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from .norms import LayerNorm, WN |
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from .utils import sequence_mask |
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class Encoder(nn.Module): |
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def __init__( |
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self, |
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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 = 1, |
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p_dropout: float = 0.0, |
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window_size: int = 10, |
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): |
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super(Encoder, self).__init__() |
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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.window_size = window_size |
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self.drop = nn.Dropout(p_dropout) |
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self.attn_layers = nn.ModuleList() |
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self.norm_layers_1 = nn.ModuleList() |
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self.ffn_layers = nn.ModuleList() |
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self.norm_layers_2 = nn.ModuleList() |
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for _ in range(self.n_layers): |
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self.attn_layers.append( |
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MultiHeadAttention( |
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hidden_channels, |
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hidden_channels, |
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n_heads, |
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p_dropout=p_dropout, |
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window_size=window_size, |
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) |
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) |
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self.norm_layers_1.append(LayerNorm(hidden_channels)) |
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self.ffn_layers.append( |
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FFN( |
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hidden_channels, |
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hidden_channels, |
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filter_channels, |
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kernel_size, |
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p_dropout=p_dropout, |
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) |
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) |
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self.norm_layers_2.append(LayerNorm(hidden_channels)) |
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def __call__(self, x: torch.Tensor, x_mask: torch.Tensor) -> torch.Tensor: |
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return super().__call__(x, x_mask) |
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def forward(self, x: torch.Tensor, x_mask: torch.Tensor) -> torch.Tensor: |
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attn_mask = x_mask.unsqueeze(2) * x_mask.unsqueeze(-1) |
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x = x * x_mask |
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for attn, norm1, ffn, norm2 in zip( |
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self.attn_layers, |
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self.norm_layers_1, |
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self.ffn_layers, |
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self.norm_layers_2, |
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): |
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y = attn(x, x, attn_mask) |
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y = self.drop(y) |
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x = norm1(x + y) |
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y = ffn(x, x_mask) |
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y = self.drop(y) |
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x = norm2(x + y) |
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x = x * x_mask |
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return x |
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class TextEncoder(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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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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f0: bool = True, |
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): |
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super(TextEncoder, self).__init__() |
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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 = float(p_dropout) |
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self.emb_phone = nn.Linear(in_channels, hidden_channels) |
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self.lrelu = nn.LeakyReLU(0.1, inplace=True) |
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if f0 == True: |
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self.emb_pitch = nn.Embedding(256, hidden_channels) |
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self.encoder = Encoder( |
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hidden_channels, |
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filter_channels, |
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n_heads, |
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n_layers, |
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kernel_size, |
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float(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 __call__( |
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self, |
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phone: torch.Tensor, |
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pitch: torch.Tensor, |
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lengths: torch.Tensor, |
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skip_head: Optional[int] = None, |
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) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: |
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return super().__call__( |
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phone, |
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pitch, |
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lengths, |
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skip_head=skip_head, |
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) |
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def forward( |
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self, |
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phone: torch.Tensor, |
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pitch: torch.Tensor, |
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lengths: torch.Tensor, |
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skip_head: Optional[int] = None, |
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) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: |
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x = self.emb_phone(phone) |
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if pitch is not None: |
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x += self.emb_pitch(pitch) |
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x = x * math.sqrt(self.hidden_channels) |
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x = self.lrelu(x) |
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x = torch.transpose(x, 1, -1) |
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x_mask = torch.unsqueeze( |
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sequence_mask(lengths, x.size(2)), |
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1, |
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).to(x.dtype) |
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x = self.encoder(x * x_mask, x_mask) |
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if skip_head is not None: |
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head = int(skip_head) |
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x = x[:, :, head:] |
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x_mask = x_mask[:, :, head:] |
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stats: torch.Tensor = self.proj(x) * x_mask |
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m, logs = torch.split(stats, self.out_channels, dim=1) |
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return m, logs, x_mask |
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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=0, |
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): |
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super(PosteriorEncoder, self).__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 = 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 __call__( |
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self, x: torch.Tensor, x_lengths: torch.Tensor, g: Optional[torch.Tensor] = None |
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) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]: |
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return super().__call__(x, x_lengths, g=g) |
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def forward( |
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self, x: torch.Tensor, x_lengths: torch.Tensor, g: Optional[torch.Tensor] = None |
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) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]: |
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x_mask = torch.unsqueeze( |
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sequence_mask(x_lengths, x.size(2)), |
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1, |
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).to(x.dtype) |
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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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def remove_weight_norm(self): |
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self.enc.remove_weight_norm() |
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def __prepare_scriptable__(self): |
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for hook in self.enc._forward_pre_hooks.values(): |
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if ( |
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hook.__module__ == "torch.nn.utils.weight_norm" |
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and hook.__class__.__name__ == "WeightNorm" |
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): |
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torch.nn.utils.remove_weight_norm(self.enc) |
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return self |
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