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