Spaces:
Build error
Build error
File size: 5,747 Bytes
d1b91e7 e75aa39 d1b91e7 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 |
import numpy as np
import torch
import torch.distributions as dist
from torch import nn
from modules.commons.conv import ConditionalConvBlocks
from modules.commons.normalizing_flow.res_flow import ResFlow
from modules.commons.wavenet import WN
class FVAEEncoder(nn.Module):
def __init__(self, c_in, hidden_size, c_latent, kernel_size,
n_layers, c_cond=0, p_dropout=0, strides=[4], nn_type='wn'):
super().__init__()
self.strides = strides
self.hidden_size = hidden_size
if np.prod(strides) == 1:
self.pre_net = nn.Conv1d(c_in, hidden_size, kernel_size=1)
else:
self.pre_net = nn.Sequential(*[
nn.Conv1d(c_in, hidden_size, kernel_size=s * 2, stride=s, padding=s // 2)
if i == 0 else
nn.Conv1d(hidden_size, hidden_size, kernel_size=s * 2, stride=s, padding=s // 2)
for i, s in enumerate(strides)
])
if nn_type == 'wn':
self.nn = WN(hidden_size, kernel_size, 1, n_layers, c_cond, p_dropout)
elif nn_type == 'conv':
self.nn = ConditionalConvBlocks(
hidden_size, c_cond, hidden_size, None, kernel_size,
layers_in_block=2, is_BTC=False, num_layers=n_layers)
self.out_proj = nn.Conv1d(hidden_size, c_latent * 2, 1)
self.latent_channels = c_latent
def forward(self, x, nonpadding, cond):
x = self.pre_net(x)
nonpadding = nonpadding[:, :, ::np.prod(self.strides)][:, :, :x.shape[-1]]
x = x * nonpadding
x = self.nn(x, nonpadding=nonpadding, cond=cond) * nonpadding
x = self.out_proj(x)
m, logs = torch.split(x, self.latent_channels, dim=1)
z = (m + torch.randn_like(m) * torch.exp(logs))
return z, m, logs, nonpadding
class FVAEDecoder(nn.Module):
def __init__(self, c_latent, hidden_size, out_channels, kernel_size,
n_layers, c_cond=0, p_dropout=0, strides=[4], nn_type='wn'):
super().__init__()
self.strides = strides
self.hidden_size = hidden_size
self.pre_net = nn.Sequential(*[
nn.ConvTranspose1d(c_latent, hidden_size, kernel_size=s, stride=s)
if i == 0 else
nn.ConvTranspose1d(hidden_size, hidden_size, kernel_size=s, stride=s)
for i, s in enumerate(strides)
])
if nn_type == 'wn':
self.nn = WN(hidden_size, kernel_size, 1, n_layers, c_cond, p_dropout)
elif nn_type == 'conv':
self.nn = ConditionalConvBlocks(
hidden_size, c_cond, hidden_size, [1] * n_layers, kernel_size,
layers_in_block=2, is_BTC=False)
self.out_proj = nn.Conv1d(hidden_size, out_channels, 1)
def forward(self, x, nonpadding, cond):
x = self.pre_net(x)
x = x * nonpadding
x = self.nn(x, nonpadding=nonpadding, cond=cond) * nonpadding
x = self.out_proj(x)
return x
class FVAE(nn.Module):
def __init__(self,
c_in_out, hidden_size, c_latent,
kernel_size, enc_n_layers, dec_n_layers, c_cond, strides,
use_prior_flow, flow_hidden=None, flow_kernel_size=None, flow_n_steps=None,
encoder_type='wn', decoder_type='wn'):
super(FVAE, self).__init__()
self.strides = strides
self.hidden_size = hidden_size
self.latent_size = c_latent
self.use_prior_flow = use_prior_flow
if np.prod(strides) == 1:
self.g_pre_net = nn.Conv1d(c_cond, c_cond, kernel_size=1)
else:
self.g_pre_net = nn.Sequential(*[
nn.Conv1d(c_cond, c_cond, kernel_size=s * 2, stride=s, padding=s // 2)
for i, s in enumerate(strides)
])
self.encoder = FVAEEncoder(c_in_out, hidden_size, c_latent, kernel_size,
enc_n_layers, c_cond, strides=strides, nn_type=encoder_type)
if use_prior_flow:
self.prior_flow = ResFlow(
c_latent, flow_hidden, flow_kernel_size, flow_n_steps, 4, c_cond=c_cond)
self.decoder = FVAEDecoder(c_latent, hidden_size, c_in_out, kernel_size,
dec_n_layers, c_cond, strides=strides, nn_type=decoder_type)
self.prior_dist = dist.Normal(0, 1)
def forward(self, x=None, nonpadding=None, cond=None, infer=False, noise_scale=1.0):
"""
:param x: [B, C_in_out, T]
:param nonpadding: [B, 1, T]
:param cond: [B, C_g, T]
:return:
"""
if nonpadding is None:
nonpadding = 1
cond_sqz = self.g_pre_net(cond)
if not infer:
z_q, m_q, logs_q, nonpadding_sqz = self.encoder(x, nonpadding, cond_sqz)
q_dist = dist.Normal(m_q, logs_q.exp())
if self.use_prior_flow:
logqx = q_dist.log_prob(z_q)
z_p = self.prior_flow(z_q, nonpadding_sqz, cond_sqz)
logpx = self.prior_dist.log_prob(z_p)
loss_kl = ((logqx - logpx) * nonpadding_sqz).sum() / nonpadding_sqz.sum() / logqx.shape[1]
else:
loss_kl = torch.distributions.kl_divergence(q_dist, self.prior_dist)
loss_kl = (loss_kl * nonpadding_sqz).sum() / nonpadding_sqz.sum() / z_q.shape[1]
z_p = None
return z_q, loss_kl, z_p, m_q, logs_q
else:
latent_shape = [cond_sqz.shape[0], self.latent_size, cond_sqz.shape[2]]
z_p = torch.randn(latent_shape).to(cond.device) * noise_scale
if self.use_prior_flow:
z_p = self.prior_flow(z_p, 1, cond_sqz, reverse=True)
return z_p
|