mirror of
https://github.com/Anjok07/ultimatevocalremovergui.git
synced 2024-11-12 01:50:48 +01:00
87 lines
2.5 KiB
Python
87 lines
2.5 KiB
Python
import torch
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from torch import nn
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from lib_v2 import layers
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class BaseASPPNet(nn.Module):
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def __init__(self, nin, ch, dilations=(4, 8, 16)):
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super(BaseASPPNet, self).__init__()
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self.enc1 = layers.Encoder(nin, ch, 3, 2, 1)
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self.enc2 = layers.Encoder(ch, ch * 2, 3, 2, 1)
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self.enc3 = layers.Encoder(ch * 2, ch * 4, 3, 2, 1)
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self.enc4 = layers.Encoder(ch * 4, ch * 8, 3, 2, 1)
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self.aspp = layers.ASPPModule(ch * 8, ch * 16, dilations)
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self.dec4 = layers.Decoder(ch * (8 + 16), ch * 8, 3, 1, 1)
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self.dec3 = layers.Decoder(ch * (4 + 8), ch * 4, 3, 1, 1)
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self.dec2 = layers.Decoder(ch * (2 + 4), ch * 2, 3, 1, 1)
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self.dec1 = layers.Decoder(ch * (1 + 2), ch, 3, 1, 1)
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def __call__(self, x):
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h, e1 = self.enc1(x)
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h, e2 = self.enc2(h)
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h, e3 = self.enc3(h)
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h, e4 = self.enc4(h)
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h = self.aspp(h)
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h = self.dec4(h, e4)
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h = self.dec3(h, e3)
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h = self.dec2(h, e2)
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h = self.dec1(h, e1)
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return h
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class CascadedASPPNet(nn.Module):
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def __init__(self):
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super(CascadedASPPNet, self).__init__()
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self.low_band_net = BaseASPPNet(2, 32, ((2, 4), (4, 8), (8, 16)))
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self.high_band_net = BaseASPPNet(2, 32, ((2, 4), (4, 8), (8, 16)))
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self.bridge = layers.Conv2DBNActiv(34, 16, 1, 1, 0)
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self.full_band_net = BaseASPPNet(16, 32)
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self.out = nn.Sequential(
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layers.Conv2DBNActiv(32, 16, 3, 1, 1),
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nn.Conv2d(16, 2, 1, bias=False))
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self.aux_out = nn.Conv2d(32, 2, 1, bias=False)
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self.offset = 128
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def __call__(self, x):
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bandw = x.size()[2] // 2
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aux = torch.cat([
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self.low_band_net(x[:, :, :bandw]),
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self.high_band_net(x[:, :, bandw:])
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], dim=2)
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h = torch.cat([x, aux], dim=1)
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h = self.full_band_net(self.bridge(h))
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h = torch.sigmoid(self.out(h))
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aux = torch.sigmoid(self.aux_out(aux))
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return h, aux
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def predict(self, x):
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bandw = x.size()[2] // 2
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aux = torch.cat([
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self.low_band_net(x[:, :, :bandw]),
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self.high_band_net(x[:, :, bandw:])
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], dim=2)
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h = torch.cat([x, aux], dim=1)
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h = self.full_band_net(self.bridge(h))
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h = torch.sigmoid(self.out(h))
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if self.offset > 0:
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h = h[:, :, :, self.offset:-self.offset]
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assert h.size()[3] > 0
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return h
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