| import torch | |
| import torch.nn as nn | |
| from torch.nn import Conv1d, ConvTranspose1d | |
| from torch.nn import functional as F | |
| from torch.nn.utils import remove_weight_norm, weight_norm | |
| import module as modules | |
| from module.resblocks import ResBlock1, ResBlock2 | |
| from tools.commons import init_weights | |
| class Generator(torch.nn.Module): | |
| def __init__( | |
| self, | |
| initial_channel, | |
| resblock, | |
| resblock_kernel_sizes, | |
| resblock_dilation_sizes, | |
| upsample_rates, | |
| upsample_initial_channel, | |
| upsample_kernel_sizes, | |
| gin_channels, | |
| activation="snakebeta", | |
| snake_logscale=True, | |
| ): | |
| super(Generator, self).__init__() | |
| self.num_kernels = len(resblock_kernel_sizes) | |
| self.num_upsamples = len(upsample_rates) | |
| self.conv_pre = Conv1d(initial_channel, upsample_initial_channel, 7, 1, 3) | |
| resblock = ResBlock1 if resblock == "1" else ResBlock2 | |
| self.ups = nn.ModuleList() | |
| for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)): | |
| self.ups.append( | |
| weight_norm( | |
| ConvTranspose1d( | |
| in_channels=upsample_initial_channel // (2**i), | |
| out_channels=upsample_initial_channel // (2 ** (i + 1)), | |
| kernel_size=k, | |
| stride=u, | |
| padding=(k - u) // 2, | |
| ) | |
| ) | |
| ) | |
| self.resblocks = nn.ModuleList() | |
| for i in range(len(self.ups)): | |
| ch = upsample_initial_channel // (2 ** (i + 1)) | |
| for j, (k, d) in enumerate( | |
| zip(resblock_kernel_sizes, resblock_dilation_sizes) | |
| ): | |
| self.resblocks.append(resblock(ch, k, d)) | |
| self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False) | |
| self.ups.apply(init_weights) | |
| if gin_channels != 0: | |
| self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1) | |
| def forward(self, x, g=None): | |
| x = self.conv_pre(x) | |
| if g is not None: | |
| x = x + self.cond(g) | |
| for i in range(self.num_upsamples): | |
| x = F.leaky_relu(x, modules.LRELU_SLOPE) | |
| x = self.ups[i](x) | |
| xs = None | |
| for j in range(self.num_kernels): | |
| if xs is None: | |
| xs = self.resblocks[i * self.num_kernels + j](x) | |
| else: | |
| xs += self.resblocks[i * self.num_kernels + j](x) | |
| x = xs / self.num_kernels | |
| x = F.leaky_relu(x) | |
| x = self.conv_post(x) | |
| x = torch.tanh(x) | |
| return x | |
| def remove_weight_norm(self): | |
| print("Removing weight norm...") | |
| for l in self.ups: | |
| remove_weight_norm(l) | |
| for l in self.resblocks: | |
| l.remove_weight_norm() | |