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from numpy import isin
import torch
import torch.nn as nn
from modules.audio2motion.transformer_base import *
DEFAULT_MAX_SOURCE_POSITIONS = 2000
DEFAULT_MAX_TARGET_POSITIONS = 2000
class TransformerEncoderLayer(nn.Module):
def __init__(self, hidden_size, dropout, kernel_size=None, num_heads=2, norm='ln'):
super().__init__()
self.hidden_size = hidden_size
self.dropout = dropout
self.num_heads = num_heads
self.op = EncSALayer(
hidden_size, num_heads, dropout=dropout,
attention_dropout=0.0, relu_dropout=dropout,
kernel_size=kernel_size
if kernel_size is not None else 9,
padding='SAME',
norm=norm, act='gelu'
)
def forward(self, x, **kwargs):
return self.op(x, **kwargs)
######################
# fastspeech modules
######################
class LayerNorm(torch.nn.LayerNorm):
"""Layer normalization module.
:param int nout: output dim size
:param int dim: dimension to be normalized
"""
def __init__(self, nout, dim=-1, eps=1e-5):
"""Construct an LayerNorm object."""
super(LayerNorm, self).__init__(nout, eps=eps)
self.dim = dim
def forward(self, x):
"""Apply layer normalization.
:param torch.Tensor x: input tensor
:return: layer normalized tensor
:rtype torch.Tensor
"""
if self.dim == -1:
return super(LayerNorm, self).forward(x)
return super(LayerNorm, self).forward(x.transpose(1, -1)).transpose(1, -1)
class FFTBlocks(nn.Module):
def __init__(self, hidden_size, num_layers, ffn_kernel_size=9, dropout=None,
num_heads=2, use_pos_embed=True, use_last_norm=True, norm='ln',
use_pos_embed_alpha=True):
super().__init__()
self.num_layers = num_layers
embed_dim = self.hidden_size = hidden_size
self.dropout = dropout if dropout is not None else 0.1
self.use_pos_embed = use_pos_embed
self.use_last_norm = use_last_norm
if use_pos_embed:
self.max_source_positions = DEFAULT_MAX_TARGET_POSITIONS
self.padding_idx = 0
self.pos_embed_alpha = nn.Parameter(torch.Tensor([1])) if use_pos_embed_alpha else 1
self.embed_positions = SinusoidalPositionalEmbedding(
embed_dim, self.padding_idx, init_size=DEFAULT_MAX_TARGET_POSITIONS,
)
self.layers = nn.ModuleList([])
self.layers.extend([
TransformerEncoderLayer(self.hidden_size, self.dropout,
kernel_size=ffn_kernel_size, num_heads=num_heads,
norm=norm)
for _ in range(self.num_layers)
])
if self.use_last_norm:
if norm == 'ln':
self.layer_norm = nn.LayerNorm(embed_dim)
elif norm == 'bn':
self.layer_norm = BatchNorm1dTBC(embed_dim)
elif norm == 'gn':
self.layer_norm = GroupNorm1DTBC(8, embed_dim)
else:
self.layer_norm = None
def forward(self, x, padding_mask=None, attn_mask=None, return_hiddens=False):
"""
:param x: [B, T, C]
:param padding_mask: [B, T]
:return: [B, T, C] or [L, B, T, C]
"""
padding_mask = x.abs().sum(-1).eq(0).data if padding_mask is None else padding_mask
nonpadding_mask_TB = 1 - padding_mask.transpose(0, 1).float()[:, :, None] # [T, B, 1]
if self.use_pos_embed:
positions = self.pos_embed_alpha * self.embed_positions(x[..., 0])
x = x + positions
x = F.dropout(x, p=self.dropout, training=self.training)
# B x T x C -> T x B x C
x = x.transpose(0, 1) * nonpadding_mask_TB
hiddens = []
for layer in self.layers:
x = layer(x, encoder_padding_mask=padding_mask, attn_mask=attn_mask) * nonpadding_mask_TB
hiddens.append(x)
if self.use_last_norm:
x = self.layer_norm(x) * nonpadding_mask_TB
if return_hiddens:
x = torch.stack(hiddens, 0) # [L, T, B, C]
x = x.transpose(1, 2) # [L, B, T, C]
else:
x = x.transpose(0, 1) # [B, T, C]
return x
class SequentialSA(nn.Module):
def __init__(self,layers):
super(SequentialSA,self).__init__()
self.layers = nn.ModuleList(layers)
def forward(self,x,x_mask):
"""
x: [batch, T, H]
x_mask: [batch, T]
"""
pad_mask = 1. - x_mask
for layer in self.layers:
if isinstance(layer, EncSALayer):
x = x.permute(1,0,2)
x = layer(x,pad_mask)
x = x.permute(1,0,2)
elif isinstance(layer, nn.Linear):
x = layer(x) * x_mask.unsqueeze(2)
elif isinstance(layer, nn.AvgPool1d):
x = x.permute(0,2,1)
x = layer(x)
x = x.permute(0,2,1)
elif isinstance(layer, nn.PReLU):
bs, t, hid = x.shape
x = x.reshape([bs*t,hid])
x = layer(x)
x = x.reshape([bs, t, hid])
else: # Relu
x = layer(x)
return x
class TransformerStyleFusionModel(nn.Module):
def __init__(self, num_heads=4, dropout = 0.1, out_dim = 64):
super(TransformerStyleFusionModel, self).__init__()
self.audio_layer = SequentialSA([
nn.Linear(29, 48),
nn.ReLU(48),
nn.Linear(48, 128),
])
self.energy_layer = SequentialSA([
nn.Linear(1, 16),
nn.ReLU(16),
nn.Linear(16, 64),
])
self.backbone1 = FFTBlocks(hidden_size=192,num_layers=3)
self.sty_encoder = nn.Sequential(*[
nn.Linear(135, 64),
nn.ReLU(),
nn.Linear(64, 128)
])
self.backbone2 = FFTBlocks(hidden_size=320,num_layers=3)
self.out_layer = SequentialSA([
nn.AvgPool1d(kernel_size=2,stride=2,padding=0), #[b,hid,t_audio]=>[b,hid,t_audio//2]
nn.Linear(320,out_dim),
nn.PReLU(out_dim),
nn.Linear(out_dim,out_dim),
])
self.dropout = nn.Dropout(p = dropout)
def forward(self, audio, energy, style, x_mask, y_mask):
pad_mask = 1. - x_mask
audio_feat = self.audio_layer(audio, x_mask)
energy_feat = self.energy_layer(energy, x_mask)
feat = torch.cat((audio_feat, energy_feat), dim=-1) # [batch, T, H=48+16]
feat = self.backbone1(feat, pad_mask)
feat = self.dropout(feat)
sty_feat = self.sty_encoder(style) # [batch,135]=>[batch, H=64]
sty_feat = sty_feat.unsqueeze(1).repeat(1, feat.shape[1], 1) # [batch, T, H=64]
feat = torch.cat([feat, sty_feat], dim=-1) # [batch, T, H=64+64]
feat = self.backbone2(feat, pad_mask) # [batch, T, H=128]
out = self.out_layer(feat, y_mask) # [batch, T//2, H=out_dim]
return out
if __name__ == '__main__':
model = TransformerStyleFusionModel()
audio = torch.rand(4,200,29) # [B,T,H]
energy = torch.rand(4,200,1) # [B,T,H]
style = torch.ones(4,135) # [B,T]
x_mask = torch.ones(4,200) # [B,T]
x_mask[3,10:] = 0
ret = model(audio,energy,style, x_mask)
print(" ") |