| | 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) |
| |
|
| | |
| | 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: |
| | return |
| |
|
| | self.apply(remove_weight_norm) |
| |
|
