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Running
on
Zero
import math | |
import torch | |
from torch import nn | |
from torch.nn import functional as F | |
from torch.nn import Conv1d | |
from torch.nn.utils import remove_weight_norm | |
from torch.nn.utils.parametrizations import weight_norm | |
from . import commons | |
from .commons import init_weights, get_padding | |
from .transforms import piecewise_rational_quadratic_transform | |
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): | |
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.parametrizations.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.parametrizations.weight_norm( | |
in_layer, name="weight" | |
) | |
self.in_layers.append(in_layer) | |
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.parametrizations.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 | |
def remove_weight_norm(self): | |
self.enc.remove_weight_norm() | |
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) | |
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 | |