import scipy
from scipy import linalg
from torch.nn import functional as F
import torch
from torch import nn
import numpy as np
import modules.audio2motion.utils as utils
from modules.audio2motion.transformer_models import FFTBlocks
from utils.commons.hparams import hparams
def fused_add_tanh_sigmoid_multiply(input_a, input_b, n_channels):
n_channels_int = n_channels[0]
in_act = input_a + input_b
t_act = torch.tanh(in_act[:, :n_channels_int, :])
s_act = torch.sigmoid(in_act[:, n_channels_int:, :])
acts = t_act * s_act
return acts
class WN(torch.nn.Module):
def __init__(self, hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=0,
p_dropout=0, share_cond_layers=False):
super(WN, self).__init__()
assert (kernel_size % 2 == 1)
assert (hidden_channels % 2 == 0)
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.share_cond_layers = share_cond_layers
self.in_layers = torch.nn.ModuleList()
self.res_skip_layers = torch.nn.ModuleList()
self.drop = nn.Dropout(p_dropout)
self.use_adapters = hparams.get("use_adapters", False)
if self.use_adapters:
self.adapter_layers = torch.nn.ModuleList()
if gin_channels != 0 and not share_cond_layers:
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)
if self.use_adapters:
adapter_layer = MlpAdapter(in_out_dim=res_skip_channels, hid_dim=res_skip_channels//4)
self.adapter_layers.append(adapter_layer)
def forward(self, x, x_mask=None, g=None, **kwargs):
output = torch.zeros_like(x)
n_channels_tensor = torch.IntTensor([self.hidden_channels])
if g is not None and not self.share_cond_layers:
g = self.cond_layer(g)
for i in range(self.n_layers):
x_in = self.in_layers[i](x)
x_in = self.drop(x_in)
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 = fused_add_tanh_sigmoid_multiply(x_in, g_l, n_channels_tensor)
res_skip_acts = self.res_skip_layers[i](acts)
if self.use_adapters:
res_skip_acts = self.adapter_layers[i](res_skip_acts.transpose(1,2)).transpose(1,2)
if i < self.n_layers - 1:
x = (x + res_skip_acts[:, :self.hidden_channels, :]) * 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):
def remove_weight_norm(m):
try:
nn.utils.remove_weight_norm(m)
except ValueError: # this module didn't have weight norm
return
self.apply(remove_weight_norm)
def enable_adapters(self):
if not self.use_adapters:
return
for adapter_layer in self.adapter_layers:
adapter_layer.enable()
def disable_adapters(self):
if not self.use_adapters:
return
for adapter_layer in self.adapter_layers:
adapter_layer.disable()
class Permute(nn.Module):
def __init__(self, *args):
super(Permute, self).__init__()
self.args = args
def forward(self, x):
return x.permute(self.args)
class LayerNorm(nn.Module):
def __init__(self, channels, eps=1e-4):
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):
n_dims = len(x.shape)
mean = torch.mean(x, 1, keepdim=True)
variance = torch.mean((x - mean) ** 2, 1, keepdim=True)
x = (x - mean) * torch.rsqrt(variance + self.eps)
shape = [1, -1] + [1] * (n_dims - 2)
x = x * self.gamma.view(*shape) + self.beta.view(*shape)
return x
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 ActNorm(nn.Module):
def __init__(self, channels, ddi=False, **kwargs):
super().__init__()
self.channels = channels
self.initialized = not ddi
self.logs = nn.Parameter(torch.zeros(1, channels, 1))
self.bias = nn.Parameter(torch.zeros(1, channels, 1))
def forward(self, x, x_mask=None, reverse=False, **kwargs):
if x_mask is None:
x_mask = torch.ones(x.size(0), 1, x.size(2)).to(device=x.device, dtype=x.dtype)
x_len = torch.sum(x_mask, [1, 2])
if not self.initialized:
self.initialize(x, x_mask)
self.initialized = True
if reverse:
z = (x - self.bias) * torch.exp(-self.logs) * x_mask
logdet = torch.sum(-self.logs) * x_len
else:
z = (self.bias + torch.exp(self.logs) * x) * x_mask
logdet = torch.sum(self.logs) * x_len # [b]
return z, logdet
def store_inverse(self):
pass
def set_ddi(self, ddi):
self.initialized = not ddi
def initialize(self, x, x_mask):
with torch.no_grad():
denom = torch.sum(x_mask, [0, 2])
m = torch.sum(x * x_mask, [0, 2]) / denom
m_sq = torch.sum(x * x * x_mask, [0, 2]) / denom
v = m_sq - (m ** 2)
logs = 0.5 * torch.log(torch.clamp_min(v, 1e-6))
bias_init = (-m * torch.exp(-logs)).view(*self.bias.shape).to(dtype=self.bias.dtype)
logs_init = (-logs).view(*self.logs.shape).to(dtype=self.logs.dtype)
self.bias.data.copy_(bias_init)
self.logs.data.copy_(logs_init)
class InvConvNear(nn.Module):
def __init__(self, channels, n_split=4, no_jacobian=False, lu=True, n_sqz=2, **kwargs):
super().__init__()
assert (n_split % 2 == 0)
self.channels = channels
self.n_split = n_split
self.n_sqz = n_sqz
self.no_jacobian = no_jacobian
w_init = torch.qr(torch.FloatTensor(self.n_split, self.n_split).normal_())[0]
if torch.det(w_init) < 0:
w_init[:, 0] = -1 * w_init[:, 0]
self.lu = lu
if lu:
# LU decomposition can slightly speed up the inverse
np_p, np_l, np_u = linalg.lu(w_init)
np_s = np.diag(np_u)
np_sign_s = np.sign(np_s)
np_log_s = np.log(np.abs(np_s))
np_u = np.triu(np_u, k=1)
l_mask = np.tril(np.ones(w_init.shape, dtype=float), -1)
eye = np.eye(*w_init.shape, dtype=float)
self.register_buffer('p', torch.Tensor(np_p.astype(float)))
self.register_buffer('sign_s', torch.Tensor(np_sign_s.astype(float)))
self.l = nn.Parameter(torch.Tensor(np_l.astype(float)), requires_grad=True)
self.log_s = nn.Parameter(torch.Tensor(np_log_s.astype(float)), requires_grad=True)
self.u = nn.Parameter(torch.Tensor(np_u.astype(float)), requires_grad=True)
self.register_buffer('l_mask', torch.Tensor(l_mask))
self.register_buffer('eye', torch.Tensor(eye))
else:
self.weight = nn.Parameter(w_init)
def forward(self, x, x_mask=None, reverse=False, **kwargs):
b, c, t = x.size()
assert (c % self.n_split == 0)
if x_mask is None:
x_mask = 1
x_len = torch.ones((b,), dtype=x.dtype, device=x.device) * t
else:
x_len = torch.sum(x_mask, [1, 2])
x = x.view(b, self.n_sqz, c // self.n_split, self.n_split // self.n_sqz, t)
x = x.permute(0, 1, 3, 2, 4).contiguous().view(b, self.n_split, c // self.n_split, t)
if self.lu:
self.weight, log_s = self._get_weight()
logdet = log_s.sum()
logdet = logdet * (c / self.n_split) * x_len
else:
logdet = torch.logdet(self.weight) * (c / self.n_split) * x_len # [b]
if reverse:
if hasattr(self, "weight_inv"):
weight = self.weight_inv
else:
weight = torch.inverse(self.weight.float()).to(dtype=self.weight.dtype)
logdet = -logdet
else:
weight = self.weight
if self.no_jacobian:
logdet = 0
weight = weight.view(self.n_split, self.n_split, 1, 1)
z = F.conv2d(x, weight)
z = z.view(b, self.n_sqz, self.n_split // self.n_sqz, c // self.n_split, t)
z = z.permute(0, 1, 3, 2, 4).contiguous().view(b, c, t) * x_mask
return z, logdet
def _get_weight(self):
l, log_s, u = self.l, self.log_s, self.u
l = l * self.l_mask + self.eye
u = u * self.l_mask.transpose(0, 1).contiguous() + torch.diag(self.sign_s * torch.exp(log_s))
weight = torch.matmul(self.p, torch.matmul(l, u))
return weight, log_s
def store_inverse(self):
weight, _ = self._get_weight()
self.weight_inv = torch.inverse(weight.float()).to(next(self.parameters()).device)
class InvConv(nn.Module):
def __init__(self, channels, no_jacobian=False, lu=True, **kwargs):
super().__init__()
w_shape = [channels, channels]
w_init = np.linalg.qr(np.random.randn(*w_shape))[0].astype(float)
LU_decomposed = lu
if not LU_decomposed:
# Sample a random orthogonal matrix:
self.register_parameter("weight", nn.Parameter(torch.Tensor(w_init)))
else:
np_p, np_l, np_u = linalg.lu(w_init)
np_s = np.diag(np_u)
np_sign_s = np.sign(np_s)
np_log_s = np.log(np.abs(np_s))
np_u = np.triu(np_u, k=1)
l_mask = np.tril(np.ones(w_shape, dtype=float), -1)
eye = np.eye(*w_shape, dtype=float)
self.register_buffer('p', torch.Tensor(np_p.astype(float)))
self.register_buffer('sign_s', torch.Tensor(np_sign_s.astype(float)))
self.l = nn.Parameter(torch.Tensor(np_l.astype(float)))
self.log_s = nn.Parameter(torch.Tensor(np_log_s.astype(float)))
self.u = nn.Parameter(torch.Tensor(np_u.astype(float)))
self.l_mask = torch.Tensor(l_mask)
self.eye = torch.Tensor(eye)
self.w_shape = w_shape
self.LU = LU_decomposed
self.weight = None
def get_weight(self, device, reverse):
w_shape = self.w_shape
self.p = self.p.to(device)
self.sign_s = self.sign_s.to(device)
self.l_mask = self.l_mask.to(device)
self.eye = self.eye.to(device)
l = self.l * self.l_mask + self.eye
u = self.u * self.l_mask.transpose(0, 1).contiguous() + torch.diag(self.sign_s * torch.exp(self.log_s))
dlogdet = self.log_s.sum()
if not reverse:
w = torch.matmul(self.p, torch.matmul(l, u))
else:
l = torch.inverse(l.double()).float()
u = torch.inverse(u.double()).float()
w = torch.matmul(u, torch.matmul(l, self.p.inverse()))
return w.view(w_shape[0], w_shape[1], 1), dlogdet
def forward(self, x, x_mask=None, reverse=False, **kwargs):
"""
log-det = log|abs(|W|)| * pixels
"""
b, c, t = x.size()
if x_mask is None:
x_len = torch.ones((b,), dtype=x.dtype, device=x.device) * t
else:
x_len = torch.sum(x_mask, [1, 2])
logdet = 0
if not reverse:
weight, dlogdet = self.get_weight(x.device, reverse)
z = F.conv1d(x, weight)
if logdet is not None:
logdet = logdet + dlogdet * x_len
return z, logdet
else:
if self.weight is None:
weight, dlogdet = self.get_weight(x.device, reverse)
else:
weight, dlogdet = self.weight, self.dlogdet
z = F.conv1d(x, weight)
if logdet is not None:
logdet = logdet - dlogdet * x_len
return z, logdet
def store_inverse(self):
self.weight, self.dlogdet = self.get_weight('cuda', reverse=True)
class Flip(nn.Module):
def forward(self, x, *args, reverse=False, **kwargs):
x = torch.flip(x, [1])
logdet = torch.zeros(x.size(0)).to(dtype=x.dtype, device=x.device)
return x, logdet
def store_inverse(self):
pass
class CouplingBlock(nn.Module):
def __init__(self, in_channels, hidden_channels, kernel_size, dilation_rate, n_layers,
gin_channels=0, p_dropout=0, sigmoid_scale=False,
share_cond_layers=False, wn=None):
super().__init__()
self.in_channels = in_channels
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.sigmoid_scale = sigmoid_scale
start = torch.nn.Conv1d(in_channels // 2, hidden_channels, 1)
start = torch.nn.utils.weight_norm(start)
self.start = start
# Initializing last layer to 0 makes the affine coupling layers
# do nothing at first. This helps with training stability
end = torch.nn.Conv1d(hidden_channels, in_channels, 1)
end.weight.data.zero_()
end.bias.data.zero_()
self.end = end
self.wn = WN(hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels,
p_dropout, share_cond_layers)
if wn is not None:
self.wn.in_layers = wn.in_layers
self.wn.res_skip_layers = wn.res_skip_layers
def forward(self, x, x_mask=None, reverse=False, g=None, **kwargs):
if x_mask is None:
x_mask = 1
x_0, x_1 = x[:, :self.in_channels // 2], x[:, self.in_channels // 2:]
x = self.start(x_0) * x_mask
x = self.wn(x, x_mask, g)
out = self.end(x)
z_0 = x_0
m = out[:, :self.in_channels // 2, :]
logs = out[:, self.in_channels // 2:, :]
if self.sigmoid_scale:
logs = torch.log(1e-6 + torch.sigmoid(logs + 2))
if reverse:
z_1 = (x_1 - m) * torch.exp(-logs) * x_mask
logdet = torch.sum(-logs * x_mask, [1, 2])
else:
z_1 = (m + torch.exp(logs) * x_1) * x_mask
logdet = torch.sum(logs * x_mask, [1, 2])
z = torch.cat([z_0, z_1], 1)
return z, logdet
def store_inverse(self):
self.wn.remove_weight_norm()
class GlowFFTBlocks(FFTBlocks):
def __init__(self, hidden_size=128, gin_channels=256, num_layers=2, ffn_kernel_size=5,
dropout=None, num_heads=4, use_pos_embed=True, use_last_norm=True,
norm='ln', use_pos_embed_alpha=True):
super().__init__(hidden_size, num_layers, ffn_kernel_size, dropout, num_heads, use_pos_embed,
use_last_norm, norm, use_pos_embed_alpha)
self.inp_proj = nn.Conv1d(hidden_size + gin_channels, hidden_size, 1)
def forward(self, x, x_mask=None, g=None):
"""
:param x: [B, C_x, T]
:param x_mask: [B, 1, T]
:param g: [B, C_g, T]
:return: [B, C_x, T]
"""
if g is not None:
x = self.inp_proj(torch.cat([x, g], 1))
x = x.transpose(1, 2)
x = super(GlowFFTBlocks, self).forward(x, x_mask[:, 0] == 0)
x = x.transpose(1, 2)
return x
class TransformerCouplingBlock(nn.Module):
def __init__(self, in_channels, hidden_channels, n_layers,
gin_channels=0, p_dropout=0, sigmoid_scale=False):
super().__init__()
self.in_channels = in_channels
self.hidden_channels = hidden_channels
self.n_layers = n_layers
self.gin_channels = gin_channels
self.p_dropout = p_dropout
self.sigmoid_scale = sigmoid_scale
start = torch.nn.Conv1d(in_channels // 2, hidden_channels, 1)
self.start = start
# Initializing last layer to 0 makes the affine coupling layers
# do nothing at first. This helps with training stability
end = torch.nn.Conv1d(hidden_channels, in_channels, 1)
end.weight.data.zero_()
end.bias.data.zero_()
self.end = end
self.fft_blocks = GlowFFTBlocks(
hidden_size=hidden_channels,
ffn_kernel_size=3,
gin_channels=gin_channels,
num_layers=n_layers)
def forward(self, x, x_mask=None, reverse=False, g=None, **kwargs):
if x_mask is None:
x_mask = 1
x_0, x_1 = x[:, :self.in_channels // 2], x[:, self.in_channels // 2:]
x = self.start(x_0) * x_mask
x = self.fft_blocks(x, x_mask, g)
out = self.end(x)
z_0 = x_0
m = out[:, :self.in_channels // 2, :]
logs = out[:, self.in_channels // 2:, :]
if self.sigmoid_scale:
logs = torch.log(1e-6 + torch.sigmoid(logs + 2))
if reverse:
z_1 = (x_1 - m) * torch.exp(-logs) * x_mask
logdet = torch.sum(-logs * x_mask, [1, 2])
else:
z_1 = (m + torch.exp(logs) * x_1) * x_mask
logdet = torch.sum(logs * x_mask, [1, 2])
z = torch.cat([z_0, z_1], 1)
return z, logdet
def store_inverse(self):
pass
class FreqFFTCouplingBlock(nn.Module):
def __init__(self, in_channels, hidden_channels, n_layers,
gin_channels=0, p_dropout=0, sigmoid_scale=False):
super().__init__()
self.in_channels = in_channels
self.hidden_channels = hidden_channels
self.n_layers = n_layers
self.gin_channels = gin_channels
self.p_dropout = p_dropout
self.sigmoid_scale = sigmoid_scale
hs = hidden_channels
stride = 8
self.start = torch.nn.Conv2d(3, hs, kernel_size=stride * 2,
stride=stride, padding=stride // 2)
end = nn.ConvTranspose2d(hs, 2, kernel_size=stride, stride=stride)
end.weight.data.zero_()
end.bias.data.zero_()
self.end = nn.Sequential(
nn.Conv2d(hs * 3, hs, 3, 1, 1),
nn.ReLU(),
nn.GroupNorm(4, hs),
nn.Conv2d(hs, hs, 3, 1, 1),
end
)
self.fft_v = FFTBlocks(hidden_size=hs, ffn_kernel_size=1, num_layers=n_layers)
self.fft_h = nn.Sequential(
nn.Conv1d(hs, hs, 3, 1, 1),
nn.ReLU(),
nn.Conv1d(hs, hs, 3, 1, 1),
)
self.fft_g = nn.Sequential(
nn.Conv1d(
gin_channels - 160, hs, kernel_size=stride * 2, stride=stride, padding=stride // 2),
Permute(0, 2, 1),
FFTBlocks(hidden_size=hs, ffn_kernel_size=1, num_layers=n_layers),
Permute(0, 2, 1),
)
def forward(self, x, x_mask=None, reverse=False, g=None, **kwargs):
g_, _ = utils.unsqueeze(g)
g_mel = g_[:, :80]
g_txt = g_[:, 80:]
g_mel, _ = utils.squeeze(g_mel)
g_txt, _ = utils.squeeze(g_txt) # [B, C, T]
if x_mask is None:
x_mask = 1
x_0, x_1 = x[:, :self.in_channels // 2], x[:, self.in_channels // 2:]
x = torch.stack([x_0, g_mel[:, :80], g_mel[:, 80:]], 1)
x = self.start(x) # [B, C, N_bins, T]
B, C, N_bins, T = x.shape
x_v = self.fft_v(x.permute(0, 3, 2, 1).reshape(B * T, N_bins, C))
x_v = x_v.reshape(B, T, N_bins, -1).permute(0, 3, 2, 1)
# x_v = x
x_h = self.fft_h(x.permute(0, 2, 1, 3).reshape(B * N_bins, C, T))
x_h = x_h.reshape(B, N_bins, -1, T).permute(0, 2, 1, 3)
# x_h = x
x_g = self.fft_g(g_txt)[:, :, None, :].repeat(1, 1, 10, 1)
x = torch.cat([x_v, x_h, x_g], 1)
out = self.end(x)
z_0 = x_0
m = out[:, 0]
logs = out[:, 1]
if self.sigmoid_scale:
logs = torch.log(1e-6 + torch.sigmoid(logs + 2))
if reverse:
z_1 = (x_1 - m) * torch.exp(-logs) * x_mask
logdet = torch.sum(-logs * x_mask, [1, 2])
else:
z_1 = (m + torch.exp(logs) * x_1) * x_mask
logdet = torch.sum(logs * x_mask, [1, 2])
z = torch.cat([z_0, z_1], 1)
return z, logdet
def store_inverse(self):
pass
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,
nn_type='wn'):
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)
if nn_type == 'wn':
self.enc = WN(hidden_channels, kernel_size, dilation_rate, n_layers, p_dropout=p_dropout,
gin_channels=gin_channels)
# elif nn_type == 'conv':
# self.enc = ConditionalConvBlocks(
# hidden_channels, gin_channels, hidden_channels, [1] * n_layers, kernel_size,
# layers_in_block=1, is_BTC=False)
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=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)
logdet = -torch.sum(logs, [1, 2])
return x, logdet
class ResidualCouplingBlock(nn.Module):
def __init__(self,
channels,
hidden_channels,
kernel_size,
dilation_rate,
n_layers,
n_flows=4,
gin_channels=0,
nn_type='wn'):
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.n_flows = n_flows
self.gin_channels = gin_channels
self.flows = nn.ModuleList()
for i in range(n_flows):
self.flows.append(
ResidualCouplingLayer(channels, hidden_channels, kernel_size, dilation_rate, n_layers,
gin_channels=gin_channels, mean_only=True, nn_type=nn_type))
self.flows.append(Flip())
def forward(self, x, x_mask, g=None, reverse=False):
if not reverse:
for flow in self.flows:
x, _ = flow(x, x_mask, g=g, reverse=reverse)
else:
for flow in reversed(self.flows):
x, _ = flow(x, x_mask, g=g, reverse=reverse)
return x
class Glow(nn.Module):
def __init__(self,
in_channels,
hidden_channels,
kernel_size,
dilation_rate,
n_blocks,
n_layers,
p_dropout=0.,
n_split=4,
n_sqz=2,
sigmoid_scale=False,
gin_channels=0,
inv_conv_type='near',
share_cond_layers=False,
share_wn_layers=0,
):
super().__init__()
"""
Note that regularization likes weight decay can leads to Nan error!
"""
self.in_channels = in_channels
self.hidden_channels = hidden_channels
self.kernel_size = kernel_size
self.dilation_rate = dilation_rate
self.n_blocks = n_blocks
self.n_layers = n_layers
self.p_dropout = p_dropout
self.n_split = n_split
self.n_sqz = n_sqz
self.sigmoid_scale = sigmoid_scale
self.gin_channels = gin_channels
self.share_cond_layers = share_cond_layers
if gin_channels != 0 and share_cond_layers:
cond_layer = torch.nn.Conv1d(gin_channels * n_sqz, 2 * hidden_channels * n_layers, 1)
self.cond_layer = torch.nn.utils.weight_norm(cond_layer, name='weight')
wn = None
self.flows = nn.ModuleList()
for b in range(n_blocks):
self.flows.append(ActNorm(channels=in_channels * n_sqz))
if inv_conv_type == 'near':
self.flows.append(InvConvNear(channels=in_channels * n_sqz, n_split=n_split, n_sqz=n_sqz))
if inv_conv_type == 'invconv':
self.flows.append(InvConv(channels=in_channels * n_sqz))
if share_wn_layers > 0:
if b % share_wn_layers == 0:
wn = WN(hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels * n_sqz,
p_dropout, share_cond_layers)
self.flows.append(
CouplingBlock(
in_channels * n_sqz,
hidden_channels,
kernel_size=kernel_size,
dilation_rate=dilation_rate,
n_layers=n_layers,
gin_channels=gin_channels * n_sqz,
p_dropout=p_dropout,
sigmoid_scale=sigmoid_scale,
share_cond_layers=share_cond_layers,
wn=wn
))
def forward(self, x, x_mask=None, g=None, reverse=False, return_hiddens=False):
"""
x: [B,T,C]
x_mask: [B,T]
g: [B,T,C]
"""
x = x.transpose(1,2)
x_mask = x_mask.unsqueeze(1)
if g is not None:
g = g.transpose(1,2)
logdet_tot = 0
if not reverse:
flows = self.flows
else:
flows = reversed(self.flows)
if return_hiddens:
hs = []
if self.n_sqz > 1:
x, x_mask_ = utils.squeeze(x, x_mask, self.n_sqz)
if g is not None:
g, _ = utils.squeeze(g, x_mask, self.n_sqz)
x_mask = x_mask_
if self.share_cond_layers and g is not None:
g = self.cond_layer(g)
for f in flows:
x, logdet = f(x, x_mask, g=g, reverse=reverse)
if return_hiddens:
hs.append(x)
logdet_tot += logdet
if self.n_sqz > 1:
x, x_mask = utils.unsqueeze(x, x_mask, self.n_sqz)
x = x.transpose(1,2)
if return_hiddens:
return x, logdet_tot, hs
return x, logdet_tot
def store_inverse(self):
def remove_weight_norm(m):
try:
nn.utils.remove_weight_norm(m)
except ValueError: # this module didn't have weight norm
return
self.apply(remove_weight_norm)
for f in self.flows:
f.store_inverse()
if __name__ == '__main__':
model = Glow(in_channels=64,
hidden_channels=128,
kernel_size=5,
dilation_rate=1,
n_blocks=12,
n_layers=4,
p_dropout=0.0,
n_split=4,
n_sqz=2,
sigmoid_scale=False,
gin_channels=80
)
exp = torch.rand([1,1440,64])
mel = torch.rand([1,1440,80])
x_mask = torch.ones([1,1440],dtype=torch.float32)
y, logdet = model(exp, x_mask,g=mel, reverse=False)
pred_exp, logdet = model(y, x_mask,g=mel, reverse=False)
# y: [b, t,c=64]
print(" ")