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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 vocoder_utils import init_weights, get_padding
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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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weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0],
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padding=get_padding(kernel_size, dilation[0]))),
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weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1],
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padding=get_padding(kernel_size, dilation[1]))),
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weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[2],
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padding=get_padding(kernel_size, dilation[2])))
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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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weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
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padding=get_padding(kernel_size, 1))),
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weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
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padding=get_padding(kernel_size, 1))),
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weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
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padding=get_padding(kernel_size, 1)))
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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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weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0],
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padding=get_padding(kernel_size, dilation[0]))),
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weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1],
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padding=get_padding(kernel_size, dilation[1])))
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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(Conv1d(h.num_mels, h.upsample_initial_channel, 7, 1, padding=3))
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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(weight_norm(ConvTranspose1d(h.upsample_initial_channel//(2**i),
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h.upsample_initial_channel//(2**(i+1)),
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k, u, padding=(u//2 + u%2), output_padding=u%2)))
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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(zip(h.resblock_kernel_sizes, h.resblock_dilation_sizes)):
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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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norm_f(Conv2d(1, 32, (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))),
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norm_f(Conv2d(32, 128, (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))),
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norm_f(Conv2d(128, 512, (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))),
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norm_f(Conv2d(512, 1024, (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))),
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norm_f(Conv2d(1024, 1024, (kernel_size, 1), 1, padding=(2, 0))),
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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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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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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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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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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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DiscriminatorS(use_spectral_norm=True),
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DiscriminatorS(),
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DiscriminatorS(),
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])
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self.meanpools = nn.ModuleList([
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AvgPool1d(4, 2, padding=2),
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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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