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| import torch | |
| import torch.nn.functional as F | |
| import torch.nn as nn | |
| from torch.nn import Conv1d, ConvTranspose1d | |
| from torch.nn.utils import weight_norm, remove_weight_norm | |
| LRELU_SLOPE = 0.1 | |
| def init_weights(m, mean=0.0, std=0.01): | |
| classname = m.__class__.__name__ | |
| if classname.find("Conv") != -1: | |
| m.weight.data.normal_(mean, std) | |
| def get_padding(kernel_size, dilation=1): | |
| return int((kernel_size * dilation - dilation) / 2) | |
| class ResBlock1(torch.nn.Module): | |
| def __init__(self, h, channels, kernel_size=3, dilation=(1, 3, 5)): | |
| super(ResBlock1, self).__init__() | |
| self.h = h | |
| self.convs1 = nn.ModuleList( | |
| [ | |
| weight_norm( | |
| Conv1d( | |
| channels, | |
| channels, | |
| kernel_size, | |
| 1, | |
| dilation=dilation[0], | |
| padding=get_padding(kernel_size, dilation[0]), | |
| ) | |
| ), | |
| weight_norm( | |
| Conv1d( | |
| channels, | |
| channels, | |
| kernel_size, | |
| 1, | |
| dilation=dilation[1], | |
| padding=get_padding(kernel_size, dilation[1]), | |
| ) | |
| ), | |
| weight_norm( | |
| Conv1d( | |
| channels, | |
| channels, | |
| kernel_size, | |
| 1, | |
| dilation=dilation[2], | |
| padding=get_padding(kernel_size, dilation[2]), | |
| ) | |
| ), | |
| ] | |
| ) | |
| self.convs1.apply(init_weights) | |
| self.convs2 = nn.ModuleList( | |
| [ | |
| weight_norm( | |
| Conv1d( | |
| channels, | |
| channels, | |
| kernel_size, | |
| 1, | |
| dilation=1, | |
| padding=get_padding(kernel_size, 1), | |
| ) | |
| ), | |
| weight_norm( | |
| Conv1d( | |
| channels, | |
| channels, | |
| kernel_size, | |
| 1, | |
| dilation=1, | |
| padding=get_padding(kernel_size, 1), | |
| ) | |
| ), | |
| weight_norm( | |
| Conv1d( | |
| channels, | |
| channels, | |
| kernel_size, | |
| 1, | |
| dilation=1, | |
| padding=get_padding(kernel_size, 1), | |
| ) | |
| ), | |
| ] | |
| ) | |
| self.convs2.apply(init_weights) | |
| def forward(self, x): | |
| for c1, c2 in zip(self.convs1, self.convs2): | |
| xt = F.leaky_relu(x, LRELU_SLOPE) | |
| xt = c1(xt) | |
| xt = F.leaky_relu(xt, LRELU_SLOPE) | |
| xt = c2(xt) | |
| x = xt + x | |
| return x | |
| def remove_weight_norm(self): | |
| for l in self.convs1: | |
| remove_weight_norm(l) | |
| for l in self.convs2: | |
| remove_weight_norm(l) | |
| class ResBlock2(torch.nn.Module): | |
| def __init__(self, h, channels, kernel_size=3, dilation=(1, 3)): | |
| super(ResBlock2, self).__init__() | |
| self.h = h | |
| self.convs = nn.ModuleList( | |
| [ | |
| weight_norm( | |
| Conv1d( | |
| channels, | |
| channels, | |
| kernel_size, | |
| 1, | |
| dilation=dilation[0], | |
| padding=get_padding(kernel_size, dilation[0]), | |
| ) | |
| ), | |
| weight_norm( | |
| Conv1d( | |
| channels, | |
| channels, | |
| kernel_size, | |
| 1, | |
| dilation=dilation[1], | |
| padding=get_padding(kernel_size, dilation[1]), | |
| ) | |
| ), | |
| ] | |
| ) | |
| self.convs.apply(init_weights) | |
| def forward(self, x): | |
| for c in self.convs: | |
| xt = F.leaky_relu(x, LRELU_SLOPE) | |
| xt = c(xt) | |
| x = xt + x | |
| return x | |
| def remove_weight_norm(self): | |
| for l in self.convs: | |
| remove_weight_norm(l) | |
| class Generator(torch.nn.Module): | |
| def __init__(self, h): | |
| super(Generator, self).__init__() | |
| self.h = h | |
| self.num_kernels = len(h.resblock_kernel_sizes) | |
| self.num_upsamples = len(h.upsample_rates) | |
| self.conv_pre = weight_norm( | |
| Conv1d(256, h.upsample_initial_channel, 7, 1, padding=3) | |
| ) | |
| resblock = ResBlock1 if h.resblock == "1" else ResBlock2 | |
| self.ups = nn.ModuleList() | |
| for i, (u, k) in enumerate(zip(h.upsample_rates, h.upsample_kernel_sizes)): | |
| self.ups.append( | |
| weight_norm( | |
| ConvTranspose1d( | |
| h.upsample_initial_channel // (2**i), | |
| h.upsample_initial_channel // (2 ** (i + 1)), | |
| u * 2, | |
| u, | |
| padding=u // 2 + u % 2, | |
| output_padding=u % 2, | |
| ) | |
| ) | |
| ) | |
| self.resblocks = nn.ModuleList() | |
| for i in range(len(self.ups)): | |
| ch = h.upsample_initial_channel // (2 ** (i + 1)) | |
| for j, (k, d) in enumerate( | |
| zip(h.resblock_kernel_sizes, h.resblock_dilation_sizes) | |
| ): | |
| self.resblocks.append(resblock(h, ch, k, d)) | |
| self.conv_post = weight_norm(Conv1d(ch, 1, 7, 1, padding=3)) | |
| self.ups.apply(init_weights) | |
| self.conv_post.apply(init_weights) | |
| def forward(self, x): | |
| # import ipdb; ipdb.set_trace() | |
| x = self.conv_pre(x) | |
| for i in range(self.num_upsamples): | |
| x = F.leaky_relu(x, 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() | |
| remove_weight_norm(self.conv_pre) | |
| remove_weight_norm(self.conv_post) | |
| ################################################################################################## | |
| # import torch | |
| # import torch.nn as nn | |
| # import torch.nn.functional as F | |
| # from torch.nn import Conv1d, ConvTranspose1d | |
| # from torch.nn.utils import weight_norm, remove_weight_norm | |
| # LRELU_SLOPE = 0.1 | |
| # def init_weights(m, mean=0.0, std=0.01): | |
| # classname = m.__class__.__name__ | |
| # if classname.find("Conv") != -1: | |
| # m.weight.data.normal_(mean, std) | |
| # def get_padding(kernel_size, dilation=1): | |
| # return int((kernel_size * dilation - dilation) / 2) | |
| # class ResBlock(torch.nn.Module): | |
| # def __init__(self, h, channels, kernel_size=3, dilation=(1, 3, 5)): | |
| # super(ResBlock, self).__init__() | |
| # self.h = h | |
| # self.convs1 = nn.ModuleList( | |
| # [ | |
| # weight_norm( | |
| # Conv1d( | |
| # channels, | |
| # channels, | |
| # kernel_size, | |
| # 1, | |
| # dilation=dilation[0], | |
| # padding=get_padding(kernel_size, dilation[0]), | |
| # ) | |
| # ), | |
| # weight_norm( | |
| # Conv1d( | |
| # channels, | |
| # channels, | |
| # kernel_size, | |
| # 1, | |
| # dilation=dilation[1], | |
| # padding=get_padding(kernel_size, dilation[1]), | |
| # ) | |
| # ), | |
| # weight_norm( | |
| # Conv1d( | |
| # channels, | |
| # channels, | |
| # kernel_size, | |
| # 1, | |
| # dilation=dilation[2], | |
| # padding=get_padding(kernel_size, dilation[2]), | |
| # ) | |
| # ), | |
| # ] | |
| # ) | |
| # self.convs1.apply(init_weights) | |
| # self.convs2 = nn.ModuleList( | |
| # [ | |
| # weight_norm( | |
| # Conv1d( | |
| # channels, | |
| # channels, | |
| # kernel_size, | |
| # 1, | |
| # dilation=1, | |
| # padding=get_padding(kernel_size, 1), | |
| # ) | |
| # ), | |
| # weight_norm( | |
| # Conv1d( | |
| # channels, | |
| # channels, | |
| # kernel_size, | |
| # 1, | |
| # dilation=1, | |
| # padding=get_padding(kernel_size, 1), | |
| # ) | |
| # ), | |
| # weight_norm( | |
| # Conv1d( | |
| # channels, | |
| # channels, | |
| # kernel_size, | |
| # 1, | |
| # dilation=1, | |
| # padding=get_padding(kernel_size, 1), | |
| # ) | |
| # ), | |
| # ] | |
| # ) | |
| # self.convs2.apply(init_weights) | |
| # def forward(self, x): | |
| # for c1, c2 in zip(self.convs1, self.convs2): | |
| # xt = F.leaky_relu(x, LRELU_SLOPE) | |
| # xt = c1(xt) | |
| # xt = F.leaky_relu(xt, LRELU_SLOPE) | |
| # xt = c2(xt) | |
| # x = xt + x | |
| # return x | |
| # def remove_weight_norm(self): | |
| # for l in self.convs1: | |
| # remove_weight_norm(l) | |
| # for l in self.convs2: | |
| # remove_weight_norm(l) | |
| # class Generator(torch.nn.Module): | |
| # def __init__(self, h): | |
| # super(Generator, self).__init__() | |
| # self.h = h | |
| # self.num_kernels = len(h.resblock_kernel_sizes) | |
| # self.num_upsamples = len(h.upsample_rates) | |
| # self.conv_pre = weight_norm( | |
| # Conv1d(h.num_mels, h.upsample_initial_channel, 7, 1, padding=3) | |
| # ) | |
| # resblock = ResBlock | |
| # self.ups = nn.ModuleList() | |
| # for i, (u, k) in enumerate(zip(h.upsample_rates, h.upsample_kernel_sizes)): | |
| # self.ups.append( | |
| # weight_norm( | |
| # ConvTranspose1d( | |
| # h.upsample_initial_channel // (2**i), | |
| # h.upsample_initial_channel // (2 ** (i + 1)), | |
| # k, | |
| # u, | |
| # padding=(k - u) // 2, | |
| # ) | |
| # ) | |
| # ) | |
| # self.resblocks = nn.ModuleList() | |
| # for i in range(len(self.ups)): | |
| # ch = h.upsample_initial_channel // (2 ** (i + 1)) | |
| # for j, (k, d) in enumerate( | |
| # zip(h.resblock_kernel_sizes, h.resblock_dilation_sizes) | |
| # ): | |
| # self.resblocks.append(resblock(h, ch, k, d)) | |
| # self.conv_post = weight_norm(Conv1d(ch, 1, 7, 1, padding=3)) | |
| # self.ups.apply(init_weights) | |
| # self.conv_post.apply(init_weights) | |
| # def forward(self, x): | |
| # x = self.conv_pre(x) | |
| # for i in range(self.num_upsamples): | |
| # x = F.leaky_relu(x, 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() | |
| # remove_weight_norm(self.conv_pre) | |
| # remove_weight_norm(self.conv_post) | |