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Running
on
Zero
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"""
MIT License
Copyright (c) 2022 Yi Ren
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
"""
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, use_weightnorm=True):
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)
if use_weightnorm:
self.cond_layer = torch.nn.utils.weight_norm(cond_layer, name='weight')
else:
self.cond_layer = cond_layer
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)
if use_weightnorm:
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)
if use_weightnorm:
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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