mirror of
https://github.com/Anjok07/ultimatevocalremovergui.git
synced 2024-12-18 02:16:00 +01:00
75 lines
2.0 KiB
Python
75 lines
2.0 KiB
Python
import torch
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import torch.nn as nn
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class TFC(nn.Module):
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def __init__(self, c, l, k, norm):
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super(TFC, self).__init__()
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self.H = nn.ModuleList()
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for i in range(l):
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self.H.append(
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nn.Sequential(
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nn.Conv2d(in_channels=c, out_channels=c, kernel_size=k, stride=1, padding=k // 2),
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norm(c),
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nn.ReLU(),
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)
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)
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def forward(self, x):
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for h in self.H:
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x = h(x)
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return x
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class DenseTFC(nn.Module):
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def __init__(self, c, l, k, norm):
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super(DenseTFC, self).__init__()
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self.conv = nn.ModuleList()
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for i in range(l):
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self.conv.append(
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nn.Sequential(
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nn.Conv2d(in_channels=c, out_channels=c, kernel_size=k, stride=1, padding=k // 2),
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norm(c),
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nn.ReLU(),
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)
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)
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def forward(self, x):
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for layer in self.conv[:-1]:
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x = torch.cat([layer(x), x], 1)
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return self.conv[-1](x)
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class TFC_TDF(nn.Module):
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def __init__(self, c, l, f, k, bn, dense=False, bias=True, norm=nn.BatchNorm2d):
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super(TFC_TDF, self).__init__()
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self.use_tdf = bn is not None
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self.tfc = DenseTFC(c, l, k, norm) if dense else TFC(c, l, k, norm)
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if self.use_tdf:
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if bn == 0:
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self.tdf = nn.Sequential(
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nn.Linear(f, f, bias=bias),
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norm(c),
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nn.ReLU()
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)
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else:
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self.tdf = nn.Sequential(
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nn.Linear(f, f // bn, bias=bias),
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norm(c),
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nn.ReLU(),
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nn.Linear(f // bn, f, bias=bias),
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norm(c),
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nn.ReLU()
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)
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def forward(self, x):
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x = self.tfc(x)
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return x + self.tdf(x) if self.use_tdf else x
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