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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, AvgPool1d, Conv2d |
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from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm |
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from models.tta.ldm.inference_utils.utils import get_padding, init_weights |
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LRELU_SLOPE = 0.1 |
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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(80, 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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k, |
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u, |
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padding=(k - 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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print("Removing weight norm...") |
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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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class DiscriminatorP(torch.nn.Module): |
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def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False): |
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super(DiscriminatorP, self).__init__() |
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self.period = period |
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norm_f = weight_norm if use_spectral_norm == False else spectral_norm |
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self.convs = nn.ModuleList( |
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[ |
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norm_f( |
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Conv2d( |
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1, |
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32, |
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(kernel_size, 1), |
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(stride, 1), |
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padding=(get_padding(5, 1), 0), |
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) |
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), |
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norm_f( |
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Conv2d( |
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32, |
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128, |
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(kernel_size, 1), |
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(stride, 1), |
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padding=(get_padding(5, 1), 0), |
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) |
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), |
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norm_f( |
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Conv2d( |
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128, |
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512, |
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(kernel_size, 1), |
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(stride, 1), |
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padding=(get_padding(5, 1), 0), |
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) |
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), |
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norm_f( |
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Conv2d( |
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512, |
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1024, |
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(kernel_size, 1), |
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(stride, 1), |
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padding=(get_padding(5, 1), 0), |
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) |
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), |
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norm_f(Conv2d(1024, 1024, (kernel_size, 1), 1, padding=(2, 0))), |
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] |
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) |
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self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0))) |
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def forward(self, x): |
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fmap = [] |
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b, c, t = x.shape |
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if t % self.period != 0: |
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n_pad = self.period - (t % self.period) |
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x = F.pad(x, (0, n_pad), "reflect") |
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t = t + n_pad |
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x = x.view(b, c, t // self.period, self.period) |
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for l in self.convs: |
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x = l(x) |
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x = F.leaky_relu(x, LRELU_SLOPE) |
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fmap.append(x) |
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x = self.conv_post(x) |
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fmap.append(x) |
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x = torch.flatten(x, 1, -1) |
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return x, fmap |
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class MultiPeriodDiscriminator(torch.nn.Module): |
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def __init__(self): |
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super(MultiPeriodDiscriminator, self).__init__() |
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self.discriminators = nn.ModuleList( |
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[ |
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DiscriminatorP(2), |
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DiscriminatorP(3), |
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DiscriminatorP(5), |
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DiscriminatorP(7), |
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DiscriminatorP(11), |
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] |
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) |
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def forward(self, y, y_hat): |
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y_d_rs = [] |
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y_d_gs = [] |
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fmap_rs = [] |
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fmap_gs = [] |
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for i, d in enumerate(self.discriminators): |
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y_d_r, fmap_r = d(y) |
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y_d_g, fmap_g = d(y_hat) |
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y_d_rs.append(y_d_r) |
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fmap_rs.append(fmap_r) |
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y_d_gs.append(y_d_g) |
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fmap_gs.append(fmap_g) |
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return y_d_rs, y_d_gs, fmap_rs, fmap_gs |
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class DiscriminatorS(torch.nn.Module): |
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def __init__(self, use_spectral_norm=False): |
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super(DiscriminatorS, self).__init__() |
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norm_f = weight_norm if use_spectral_norm == False else spectral_norm |
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self.convs = nn.ModuleList( |
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[ |
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norm_f(Conv1d(1, 128, 15, 1, padding=7)), |
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norm_f(Conv1d(128, 128, 41, 2, groups=4, padding=20)), |
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norm_f(Conv1d(128, 256, 41, 2, groups=16, padding=20)), |
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norm_f(Conv1d(256, 512, 41, 4, groups=16, padding=20)), |
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norm_f(Conv1d(512, 1024, 41, 4, groups=16, padding=20)), |
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norm_f(Conv1d(1024, 1024, 41, 1, groups=16, padding=20)), |
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norm_f(Conv1d(1024, 1024, 5, 1, padding=2)), |
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] |
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) |
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self.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1)) |
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def forward(self, x): |
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fmap = [] |
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for l in self.convs: |
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x = l(x) |
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x = F.leaky_relu(x, LRELU_SLOPE) |
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fmap.append(x) |
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x = self.conv_post(x) |
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fmap.append(x) |
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x = torch.flatten(x, 1, -1) |
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return x, fmap |
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class MultiScaleDiscriminator(torch.nn.Module): |
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def __init__(self): |
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super(MultiScaleDiscriminator, self).__init__() |
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self.discriminators = nn.ModuleList( |
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[ |
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DiscriminatorS(use_spectral_norm=True), |
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DiscriminatorS(), |
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DiscriminatorS(), |
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] |
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) |
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self.meanpools = nn.ModuleList( |
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[AvgPool1d(4, 2, padding=2), AvgPool1d(4, 2, padding=2)] |
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) |
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def forward(self, y, y_hat): |
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y_d_rs = [] |
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y_d_gs = [] |
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fmap_rs = [] |
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fmap_gs = [] |
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for i, d in enumerate(self.discriminators): |
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if i != 0: |
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y = self.meanpools[i - 1](y) |
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y_hat = self.meanpools[i - 1](y_hat) |
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y_d_r, fmap_r = d(y) |
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y_d_g, fmap_g = d(y_hat) |
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y_d_rs.append(y_d_r) |
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fmap_rs.append(fmap_r) |
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y_d_gs.append(y_d_g) |
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fmap_gs.append(fmap_g) |
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return y_d_rs, y_d_gs, fmap_rs, fmap_gs |
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def feature_loss(fmap_r, fmap_g): |
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loss = 0 |
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for dr, dg in zip(fmap_r, fmap_g): |
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for rl, gl in zip(dr, dg): |
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loss += torch.mean(torch.abs(rl - gl)) |
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return loss * 2 |
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def discriminator_loss(disc_real_outputs, disc_generated_outputs): |
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loss = 0 |
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r_losses = [] |
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g_losses = [] |
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for dr, dg in zip(disc_real_outputs, disc_generated_outputs): |
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r_loss = torch.mean((1 - dr) ** 2) |
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g_loss = torch.mean(dg**2) |
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loss += r_loss + g_loss |
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r_losses.append(r_loss.item()) |
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g_losses.append(g_loss.item()) |
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return loss, r_losses, g_losses |
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def generator_loss(disc_outputs): |
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loss = 0 |
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gen_losses = [] |
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for dg in disc_outputs: |
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l = torch.mean((1 - dg) ** 2) |
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gen_losses.append(l) |
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loss += l |
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return loss, gen_losses |
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