import math
import numpy as np
import torch
from torch import nn
from torch.nn import functional as F
from torch.nn import Conv1d
from torch.nn.utils import weight_norm, remove_weight_norm
from module import commons
from module.commons import init_weights, get_padding
from module.transforms import piecewise_rational_quadratic_transform
import torch.distributions as D
LRELU_SLOPE = 0.1
class LayerNorm(nn.Module):
def __init__(self, channels, eps=1e-5):
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):
x = x.transpose(1, -1)
x = F.layer_norm(x, (self.channels,), self.gamma, self.beta, self.eps)
return x.transpose(1, -1)
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 DDSConv(nn.Module):
"""
Dialted and Depth-Separable Convolution
"""
def __init__(self, channels, kernel_size, n_layers, p_dropout=0.0):
super().__init__()
self.channels = channels
self.kernel_size = kernel_size
self.n_layers = n_layers
self.p_dropout = p_dropout
self.drop = nn.Dropout(p_dropout)
self.convs_sep = nn.ModuleList()
self.convs_1x1 = nn.ModuleList()
self.norms_1 = nn.ModuleList()
self.norms_2 = nn.ModuleList()
for i in range(n_layers):
dilation = kernel_size**i
padding = (kernel_size * dilation - dilation) // 2
self.convs_sep.append(
nn.Conv1d(
channels,
channels,
kernel_size,
groups=channels,
dilation=dilation,
padding=padding,
)
)
self.convs_1x1.append(nn.Conv1d(channels, channels, 1))
self.norms_1.append(LayerNorm(channels))
self.norms_2.append(LayerNorm(channels))
def forward(self, x, x_mask, g=None):
if g is not None:
x = x + g
for i in range(self.n_layers):
y = self.convs_sep[i](x * x_mask)
y = self.norms_1[i](y)
y = F.gelu(y)
y = self.convs_1x1[i](y)
y = self.norms_2[i](y)
y = F.gelu(y)
y = self.drop(y)
x = x + y
return x * x_mask
class WN(torch.nn.Module):
def __init__(
self,
hidden_channels,
kernel_size,
dilation_rate,
n_layers,
gin_channels=0,
p_dropout=0,
):
super(WN, self).__init__()
assert kernel_size % 2 == 1
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.in_layers = torch.nn.ModuleList()
self.res_skip_layers = torch.nn.ModuleList()
self.drop = nn.Dropout(p_dropout)
if gin_channels != 0:
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)
def forward(self, x, x_mask, g=None, **kwargs):
output = torch.zeros_like(x)
n_channels_tensor = torch.IntTensor([self.hidden_channels])
if g is not None:
g = self.cond_layer(g)
for i in range(self.n_layers):
x_in = self.in_layers[i](x)
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 = commons.fused_add_tanh_sigmoid_multiply(x_in, g_l, n_channels_tensor)
acts = self.drop(acts)
res_skip_acts = self.res_skip_layers[i](acts)
if i < self.n_layers - 1:
res_acts = res_skip_acts[:, : self.hidden_channels, :]
x = (x + res_acts) * 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):
if self.gin_channels != 0:
torch.nn.utils.remove_weight_norm(self.cond_layer)
for l in self.in_layers:
torch.nn.utils.remove_weight_norm(l)
for l in self.res_skip_layers:
torch.nn.utils.remove_weight_norm(l)
class ResBlock1(torch.nn.Module):
def __init__(self, channels, kernel_size=3, dilation=(1, 3, 5)):
super(ResBlock1, self).__init__()
self.convs1 = nn.ModuleList(
[
weight_norm(
Conv1d(
channels,
channels,
kernel_size,
1,
dilation=dilation[0],
padding=get_padding(kernel_size, dilation[0]),
)
),
weight_norm(
Conv1d(
channels,
channels,
kernel_size,
1,
dilation=dilation[1],
padding=get_padding(kernel_size, dilation[1]),
)
),
weight_norm(
Conv1d(
channels,
channels,
kernel_size,
1,
dilation=dilation[2],
padding=get_padding(kernel_size, dilation[2]),
)
),
]
)
self.convs1.apply(init_weights)
self.convs2 = nn.ModuleList(
[
weight_norm(
Conv1d(
channels,
channels,
kernel_size,
1,
dilation=1,
padding=get_padding(kernel_size, 1),
)
),
weight_norm(
Conv1d(
channels,
channels,
kernel_size,
1,
dilation=1,
padding=get_padding(kernel_size, 1),
)
),
weight_norm(
Conv1d(
channels,
channels,
kernel_size,
1,
dilation=1,
padding=get_padding(kernel_size, 1),
)
),
]
)
self.convs2.apply(init_weights)
def forward(self, x, x_mask=None):
for c1, c2 in zip(self.convs1, self.convs2):
xt = F.leaky_relu(x, LRELU_SLOPE)
if x_mask is not None:
xt = xt * x_mask
xt = c1(xt)
xt = F.leaky_relu(xt, LRELU_SLOPE)
if x_mask is not None:
xt = xt * x_mask
xt = c2(xt)
x = xt + x
if x_mask is not None:
x = x * x_mask
return x
def remove_weight_norm(self):
for l in self.convs1:
remove_weight_norm(l)
for l in self.convs2:
remove_weight_norm(l)
class ResBlock2(torch.nn.Module):
def __init__(self, channels, kernel_size=3, dilation=(1, 3)):
super(ResBlock2, self).__init__()
self.convs = nn.ModuleList(
[
weight_norm(
Conv1d(
channels,
channels,
kernel_size,
1,
dilation=dilation[0],
padding=get_padding(kernel_size, dilation[0]),
)
),
weight_norm(
Conv1d(
channels,
channels,
kernel_size,
1,
dilation=dilation[1],
padding=get_padding(kernel_size, dilation[1]),
)
),
]
)
self.convs.apply(init_weights)
def forward(self, x, x_mask=None):
for c in self.convs:
xt = F.leaky_relu(x, LRELU_SLOPE)
if x_mask is not None:
xt = xt * x_mask
xt = c(xt)
x = xt + x
if x_mask is not None:
x = x * x_mask
return x
def remove_weight_norm(self):
for l in self.convs:
remove_weight_norm(l)
class Log(nn.Module):
def forward(self, x, x_mask, reverse=False, **kwargs):
if not reverse:
y = torch.log(torch.clamp_min(x, 1e-5)) * x_mask
logdet = torch.sum(-y, [1, 2])
return y, logdet
else:
x = torch.exp(x) * x_mask
return x
class Flip(nn.Module):
def forward(self, x, *args, reverse=False, **kwargs):
x = torch.flip(x, [1])
if not reverse:
logdet = torch.zeros(x.size(0)).to(dtype=x.dtype, device=x.device)
return x, logdet
else:
return x
class ElementwiseAffine(nn.Module):
def __init__(self, channels):
super().__init__()
self.channels = channels
self.m = nn.Parameter(torch.zeros(channels, 1))
self.logs = nn.Parameter(torch.zeros(channels, 1))
def forward(self, x, x_mask, reverse=False, **kwargs):
if not reverse:
y = self.m + torch.exp(self.logs) * x
y = y * x_mask
logdet = torch.sum(self.logs * x_mask, [1, 2])
return y, logdet
else:
x = (x - self.m) * torch.exp(-self.logs) * x_mask
return x
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,
):
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)
self.enc = WN(
hidden_channels,
kernel_size,
dilation_rate,
n_layers,
p_dropout=p_dropout,
gin_channels=gin_channels,
)
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, 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)
return x
class ConvFlow(nn.Module):
def __init__(
self,
in_channels,
filter_channels,
kernel_size,
n_layers,
num_bins=10,
tail_bound=5.0,
):
super().__init__()
self.in_channels = in_channels
self.filter_channels = filter_channels
self.kernel_size = kernel_size
self.n_layers = n_layers
self.num_bins = num_bins
self.tail_bound = tail_bound
self.half_channels = in_channels // 2
self.pre = nn.Conv1d(self.half_channels, filter_channels, 1)
self.convs = DDSConv(filter_channels, kernel_size, n_layers, p_dropout=0.0)
self.proj = nn.Conv1d(
filter_channels, self.half_channels * (num_bins * 3 - 1), 1
)
self.proj.weight.data.zero_()
self.proj.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)
h = self.convs(h, x_mask, g=g)
h = self.proj(h) * x_mask
b, c, t = x0.shape
h = h.reshape(b, c, -1, t).permute(0, 1, 3, 2) # [b, cx?, t] -> [b, c, t, ?]
unnormalized_widths = h[..., : self.num_bins] / math.sqrt(self.filter_channels)
unnormalized_heights = h[..., self.num_bins : 2 * self.num_bins] / math.sqrt(
self.filter_channels
)
unnormalized_derivatives = h[..., 2 * self.num_bins :]
x1, logabsdet = piecewise_rational_quadratic_transform(
x1,
unnormalized_widths,
unnormalized_heights,
unnormalized_derivatives,
inverse=reverse,
tails="linear",
tail_bound=self.tail_bound,
)
x = torch.cat([x0, x1], 1) * x_mask
logdet = torch.sum(logabsdet * x_mask, [1, 2])
if not reverse:
return x, logdet
else:
return x
class LinearNorm(nn.Module):
def __init__(
self,
in_channels,
out_channels,
bias=True,
spectral_norm=False,
):
super(LinearNorm, self).__init__()
self.fc = nn.Linear(in_channels, out_channels, bias)
if spectral_norm:
self.fc = nn.utils.spectral_norm(self.fc)
def forward(self, input):
out = self.fc(input)
return out
class Mish(nn.Module):
def __init__(self):
super(Mish, self).__init__()
def forward(self, x):
return x * torch.tanh(F.softplus(x))
class Conv1dGLU(nn.Module):
"""
Conv1d + GLU(Gated Linear Unit) with residual connection.
For GLU refer to https://arxiv.org/abs/1612.08083 paper.
"""
def __init__(self, in_channels, out_channels, kernel_size, dropout):
super(Conv1dGLU, self).__init__()
self.out_channels = out_channels
self.conv1 = ConvNorm(in_channels, 2 * out_channels, kernel_size=kernel_size)
self.dropout = nn.Dropout(dropout)
def forward(self, x):
residual = x
x = self.conv1(x)
x1, x2 = torch.split(x, split_size_or_sections=self.out_channels, dim=1)
x = x1 * torch.sigmoid(x2)
x = residual + self.dropout(x)
return x
class ConvNorm(nn.Module):
def __init__(
self,
in_channels,
out_channels,
kernel_size=1,
stride=1,
padding=None,
dilation=1,
bias=True,
spectral_norm=False,
):
super(ConvNorm, self).__init__()
if padding is None:
assert kernel_size % 2 == 1
padding = int(dilation * (kernel_size - 1) / 2)
self.conv = torch.nn.Conv1d(
in_channels,
out_channels,
kernel_size=kernel_size,
stride=stride,
padding=padding,
dilation=dilation,
bias=bias,
)
if spectral_norm:
self.conv = nn.utils.spectral_norm(self.conv)
def forward(self, input):
out = self.conv(input)
return out
class MultiHeadAttention(nn.Module):
"""Multi-Head Attention module"""
def __init__(self, n_head, d_model, d_k, d_v, dropout=0.0, spectral_norm=False):
super().__init__()
self.n_head = n_head
self.d_k = d_k
self.d_v = d_v
self.w_qs = nn.Linear(d_model, n_head * d_k)
self.w_ks = nn.Linear(d_model, n_head * d_k)
self.w_vs = nn.Linear(d_model, n_head * d_v)
self.attention = ScaledDotProductAttention(
temperature=np.power(d_model, 0.5), dropout=dropout
)
self.fc = nn.Linear(n_head * d_v, d_model)
self.dropout = nn.Dropout(dropout)
if spectral_norm:
self.w_qs = nn.utils.spectral_norm(self.w_qs)
self.w_ks = nn.utils.spectral_norm(self.w_ks)
self.w_vs = nn.utils.spectral_norm(self.w_vs)
self.fc = nn.utils.spectral_norm(self.fc)
def forward(self, x, mask=None):
d_k, d_v, n_head = self.d_k, self.d_v, self.n_head
sz_b, len_x, _ = x.size()
residual = x
q = self.w_qs(x).view(sz_b, len_x, n_head, d_k)
k = self.w_ks(x).view(sz_b, len_x, n_head, d_k)
v = self.w_vs(x).view(sz_b, len_x, n_head, d_v)
q = q.permute(2, 0, 1, 3).contiguous().view(-1, len_x, d_k) # (n*b) x lq x dk
k = k.permute(2, 0, 1, 3).contiguous().view(-1, len_x, d_k) # (n*b) x lk x dk
v = v.permute(2, 0, 1, 3).contiguous().view(-1, len_x, d_v) # (n*b) x lv x dv
if mask is not None:
slf_mask = mask.repeat(n_head, 1, 1) # (n*b) x .. x ..
else:
slf_mask = None
output, attn = self.attention(q, k, v, mask=slf_mask)
output = output.view(n_head, sz_b, len_x, d_v)
output = (
output.permute(1, 2, 0, 3).contiguous().view(sz_b, len_x, -1)
) # b x lq x (n*dv)
output = self.fc(output)
output = self.dropout(output) + residual
return output, attn
class ScaledDotProductAttention(nn.Module):
"""Scaled Dot-Product Attention"""
def __init__(self, temperature, dropout):
super().__init__()
self.temperature = temperature
self.softmax = nn.Softmax(dim=2)
self.dropout = nn.Dropout(dropout)
def forward(self, q, k, v, mask=None):
attn = torch.bmm(q, k.transpose(1, 2))
attn = attn / self.temperature
if mask is not None:
attn = attn.masked_fill(mask, -np.inf)
attn = self.softmax(attn)
p_attn = self.dropout(attn)
output = torch.bmm(p_attn, v)
return output, attn
class MelStyleEncoder(nn.Module):
"""MelStyleEncoder"""
def __init__(
self,
n_mel_channels=80,
style_hidden=128,
style_vector_dim=256,
style_kernel_size=5,
style_head=2,
dropout=0.1,
):
super(MelStyleEncoder, self).__init__()
self.in_dim = n_mel_channels
self.hidden_dim = style_hidden
self.out_dim = style_vector_dim
self.kernel_size = style_kernel_size
self.n_head = style_head
self.dropout = dropout
self.spectral = nn.Sequential(
LinearNorm(self.in_dim, self.hidden_dim),
Mish(),
nn.Dropout(self.dropout),
LinearNorm(self.hidden_dim, self.hidden_dim),
Mish(),
nn.Dropout(self.dropout),
)
self.temporal = nn.Sequential(
Conv1dGLU(self.hidden_dim, self.hidden_dim, self.kernel_size, self.dropout),
Conv1dGLU(self.hidden_dim, self.hidden_dim, self.kernel_size, self.dropout),
)
self.slf_attn = MultiHeadAttention(
self.n_head,
self.hidden_dim,
self.hidden_dim // self.n_head,
self.hidden_dim // self.n_head,
self.dropout,
)
self.fc = LinearNorm(self.hidden_dim, self.out_dim)
def temporal_avg_pool(self, x, mask=None):
if mask is None:
out = torch.mean(x, dim=1)
else:
len_ = (~mask).sum(dim=1).unsqueeze(1)
x = x.masked_fill(mask.unsqueeze(-1), 0)
x = x.sum(dim=1)
out = torch.div(x, len_)
return out
def forward(self, x, mask=None):
x = x.transpose(1, 2)
if mask is not None:
mask = (mask.int() == 0).squeeze(1)
max_len = x.shape[1]
slf_attn_mask = (
mask.unsqueeze(1).expand(-1, max_len, -1) if mask is not None else None
)
# spectral
x = self.spectral(x)
# temporal
x = x.transpose(1, 2)
x = self.temporal(x)
x = x.transpose(1, 2)
# self-attention
if mask is not None:
x = x.masked_fill(mask.unsqueeze(-1), 0)
x, _ = self.slf_attn(x, mask=slf_attn_mask)
# fc
x = self.fc(x)
# temoral average pooling
w = self.temporal_avg_pool(x, mask=mask)
return w.unsqueeze(-1)
class MelStyleEncoderVAE(nn.Module):
def __init__(self, spec_channels, z_latent_dim, emb_dim):
super().__init__()
self.ref_encoder = MelStyleEncoder(spec_channels, style_vector_dim=emb_dim)
self.fc1 = nn.Linear(emb_dim, z_latent_dim)
self.fc2 = nn.Linear(emb_dim, z_latent_dim)
self.fc3 = nn.Linear(z_latent_dim, emb_dim)
self.z_latent_dim = z_latent_dim
def reparameterize(self, mu, logvar):
if self.training:
std = torch.exp(0.5 * logvar)
eps = torch.randn_like(std)
return eps.mul(std).add_(mu)
else:
return mu
def forward(self, inputs, mask=None):
enc_out = self.ref_encoder(inputs.squeeze(-1), mask).squeeze(-1)
mu = self.fc1(enc_out)
logvar = self.fc2(enc_out)
posterior = D.Normal(mu, torch.exp(logvar))
kl_divergence = D.kl_divergence(
posterior, D.Normal(torch.zeros_like(mu), torch.ones_like(logvar))
)
loss_kl = kl_divergence.mean()
z = posterior.rsample()
style_embed = self.fc3(z)
return style_embed.unsqueeze(-1), loss_kl
def infer(self, inputs=None, random_sample=False, manual_latent=None):
if manual_latent is None:
if random_sample:
dev = next(self.parameters()).device
posterior = D.Normal(
torch.zeros(1, self.z_latent_dim, device=dev),
torch.ones(1, self.z_latent_dim, device=dev),
)
z = posterior.rsample()
else:
enc_out = self.ref_encoder(inputs.transpose(1, 2))
mu = self.fc1(enc_out)
z = mu
else:
z = manual_latent
style_embed = self.fc3(z)
return style_embed.unsqueeze(-1), z
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, g=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 = None
return z
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, **kwargs):
super().__init__()
assert n_split % 2 == 0
self.channels = channels
self.n_split = n_split
self.no_jacobian = no_jacobian
w_init = torch.linalg.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.weight = nn.Parameter(w_init)
def forward(self, x, x_mask=None, g=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, 2, c // self.n_split, self.n_split // 2, t)
x = (
x.permute(0, 1, 3, 2, 4)
.contiguous()
.view(b, self.n_split, c // self.n_split, t)
)
if reverse:
if hasattr(self, "weight_inv"):
weight = self.weight_inv
else:
weight = torch.inverse(self.weight.float()).to(dtype=self.weight.dtype)
logdet = None
else:
weight = self.weight
if self.no_jacobian:
logdet = 0
else:
logdet = torch.logdet(self.weight) * (c / self.n_split) * x_len # [b]
weight = weight.view(self.n_split, self.n_split, 1, 1)
z = F.conv2d(x, weight)
z = z.view(b, 2, self.n_split // 2, c // self.n_split, t)
z = z.permute(0, 1, 3, 2, 4).contiguous().view(b, c, t) * x_mask
if reverse:
return z
else:
return z, logdet
def store_inverse(self):
self.weight_inv = torch.inverse(self.weight.float()).to(dtype=self.weight.dtype)