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import torch
from torch import nn
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_size, kernel_size, dilation_rate, n_layers, c_cond=0,
p_dropout=0, share_cond_layers=False, is_BTC=False):
super(WN, self).__init__()
assert (kernel_size % 2 == 1)
assert (hidden_size % 2 == 0)
self.is_BTC = is_BTC
self.hidden_size = hidden_size
self.kernel_size = kernel_size
self.dilation_rate = dilation_rate
self.n_layers = n_layers
self.gin_channels = c_cond
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)
if c_cond != 0 and not share_cond_layers:
cond_layer = torch.nn.Conv1d(c_cond, 2 * hidden_size * 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_size, 2 * hidden_size, 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_size
else:
res_skip_channels = hidden_size
res_skip_layer = torch.nn.Conv1d(hidden_size, 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, nonpadding=None, cond=None):
if self.is_BTC:
x = x.transpose(1, 2)
cond = cond.transpose(1, 2) if cond is not None else None
nonpadding = nonpadding.transpose(1, 2) if nonpadding is not None else None
if nonpadding is None:
nonpadding = 1
output = torch.zeros_like(x)
n_channels_tensor = torch.IntTensor([self.hidden_size])
if cond is not None and not self.share_cond_layers:
cond = self.cond_layer(cond)
for i in range(self.n_layers):
x_in = self.in_layers[i](x)
x_in = self.drop(x_in)
if cond is not None:
cond_offset = i * 2 * self.hidden_size
cond_l = cond[:, cond_offset:cond_offset + 2 * self.hidden_size, :]
else:
cond_l = torch.zeros_like(x_in)
acts = fused_add_tanh_sigmoid_multiply(x_in, cond_l, n_channels_tensor)
res_skip_acts = self.res_skip_layers[i](acts)
if i < self.n_layers - 1:
x = (x + res_skip_acts[:, :self.hidden_size, :]) * nonpadding
output = output + res_skip_acts[:, self.hidden_size:, :]
else:
output = output + res_skip_acts
output = output * nonpadding
if self.is_BTC:
output = output.transpose(1, 2)
return output
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)
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