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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) | |