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113
lib_v5/nets.py
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113
lib_v5/nets.py
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import torch
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from torch import nn
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import torch.nn.functional as F
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from lib_v5 import layers
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from lib_v5 import spec_utils
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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, n_fft):
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super(CascadedASPPNet, self).__init__()
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self.stg1_low_band_net = BaseASPPNet(2, 16)
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self.stg1_high_band_net = BaseASPPNet(2, 16)
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self.stg2_bridge = layers.Conv2DBNActiv(18, 8, 1, 1, 0)
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self.stg2_full_band_net = BaseASPPNet(8, 16)
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self.stg3_bridge = layers.Conv2DBNActiv(34, 16, 1, 1, 0)
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self.stg3_full_band_net = BaseASPPNet(16, 32)
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self.out = nn.Conv2d(32, 2, 1, bias=False)
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self.aux1_out = nn.Conv2d(16, 2, 1, bias=False)
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self.aux2_out = nn.Conv2d(16, 2, 1, bias=False)
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self.max_bin = n_fft // 2
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self.output_bin = n_fft // 2 + 1
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self.offset = 128
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def forward(self, x, aggressiveness=None):
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mix = x.detach()
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x = x.clone()
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x = x[:, :, :self.max_bin]
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bandw = x.size()[2] // 2
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aux1 = torch.cat([
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self.stg1_low_band_net(x[:, :, :bandw]),
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self.stg1_high_band_net(x[:, :, bandw:])
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], dim=2)
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h = torch.cat([x, aux1], dim=1)
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aux2 = self.stg2_full_band_net(self.stg2_bridge(h))
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h = torch.cat([x, aux1, aux2], dim=1)
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h = self.stg3_full_band_net(self.stg3_bridge(h))
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mask = torch.sigmoid(self.out(h))
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mask = F.pad(
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input=mask,
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pad=(0, 0, 0, self.output_bin - mask.size()[2]),
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mode='replicate')
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if self.training:
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aux1 = torch.sigmoid(self.aux1_out(aux1))
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aux1 = F.pad(
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input=aux1,
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pad=(0, 0, 0, self.output_bin - aux1.size()[2]),
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mode='replicate')
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aux2 = torch.sigmoid(self.aux2_out(aux2))
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aux2 = F.pad(
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input=aux2,
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pad=(0, 0, 0, self.output_bin - aux2.size()[2]),
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mode='replicate')
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return mask * mix, aux1 * mix, aux2 * mix
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else:
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if aggressiveness:
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mask[:, :, :aggressiveness['split_bin']] = torch.pow(mask[:, :, :aggressiveness['split_bin']], 1 + aggressiveness['value'] / 3)
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mask[:, :, aggressiveness['split_bin']:] = torch.pow(mask[:, :, aggressiveness['split_bin']:], 1 + aggressiveness['value'])
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return mask * mix
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def predict(self, x_mag, aggressiveness=None):
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h = self.forward(x_mag, aggressiveness)
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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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112
lib_v5/nets_33966KB.py
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112
lib_v5/nets_33966KB.py
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import torch
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from torch import nn
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import torch.nn.functional as F
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from lib_v5 import layers_33966KB as layers
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class BaseASPPNet(nn.Module):
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def __init__(self, nin, ch, dilations=(4, 8, 16, 32)):
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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, n_fft):
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super(CascadedASPPNet, self).__init__()
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self.stg1_low_band_net = BaseASPPNet(2, 16)
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self.stg1_high_band_net = BaseASPPNet(2, 16)
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self.stg2_bridge = layers.Conv2DBNActiv(18, 8, 1, 1, 0)
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self.stg2_full_band_net = BaseASPPNet(8, 16)
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self.stg3_bridge = layers.Conv2DBNActiv(34, 16, 1, 1, 0)
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self.stg3_full_band_net = BaseASPPNet(16, 32)
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self.out = nn.Conv2d(32, 2, 1, bias=False)
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self.aux1_out = nn.Conv2d(16, 2, 1, bias=False)
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self.aux2_out = nn.Conv2d(16, 2, 1, bias=False)
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self.max_bin = n_fft // 2
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self.output_bin = n_fft // 2 + 1
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self.offset = 128
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def forward(self, x, aggressiveness=None):
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mix = x.detach()
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x = x.clone()
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x = x[:, :, :self.max_bin]
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bandw = x.size()[2] // 2
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aux1 = torch.cat([
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self.stg1_low_band_net(x[:, :, :bandw]),
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self.stg1_high_band_net(x[:, :, bandw:])
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], dim=2)
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h = torch.cat([x, aux1], dim=1)
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aux2 = self.stg2_full_band_net(self.stg2_bridge(h))
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h = torch.cat([x, aux1, aux2], dim=1)
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h = self.stg3_full_band_net(self.stg3_bridge(h))
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mask = torch.sigmoid(self.out(h))
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mask = F.pad(
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input=mask,
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pad=(0, 0, 0, self.output_bin - mask.size()[2]),
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mode='replicate')
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if self.training:
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aux1 = torch.sigmoid(self.aux1_out(aux1))
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aux1 = F.pad(
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input=aux1,
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pad=(0, 0, 0, self.output_bin - aux1.size()[2]),
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mode='replicate')
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aux2 = torch.sigmoid(self.aux2_out(aux2))
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aux2 = F.pad(
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input=aux2,
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pad=(0, 0, 0, self.output_bin - aux2.size()[2]),
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mode='replicate')
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return mask * mix, aux1 * mix, aux2 * mix
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else:
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if aggressiveness:
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mask[:, :, :aggressiveness['split_bin']] = torch.pow(mask[:, :, :aggressiveness['split_bin']], 1 + aggressiveness['value'] / 3)
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mask[:, :, aggressiveness['split_bin']:] = torch.pow(mask[:, :, aggressiveness['split_bin']:], 1 + aggressiveness['value'])
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return mask * mix
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def predict(self, x_mag, aggressiveness=None):
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h = self.forward(x_mag, aggressiveness)
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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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113
lib_v5/nets_537227KB.py
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113
lib_v5/nets_537227KB.py
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import torch
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import numpy as np
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from torch import nn
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import torch.nn.functional as F
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from lib_v5 import layers_537238KB as 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, n_fft):
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super(CascadedASPPNet, self).__init__()
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self.stg1_low_band_net = BaseASPPNet(2, 64)
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self.stg1_high_band_net = BaseASPPNet(2, 64)
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self.stg2_bridge = layers.Conv2DBNActiv(66, 32, 1, 1, 0)
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self.stg2_full_band_net = BaseASPPNet(32, 64)
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self.stg3_bridge = layers.Conv2DBNActiv(130, 64, 1, 1, 0)
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self.stg3_full_band_net = BaseASPPNet(64, 128)
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self.out = nn.Conv2d(128, 2, 1, bias=False)
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self.aux1_out = nn.Conv2d(64, 2, 1, bias=False)
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self.aux2_out = nn.Conv2d(64, 2, 1, bias=False)
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self.max_bin = n_fft // 2
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self.output_bin = n_fft // 2 + 1
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self.offset = 128
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def forward(self, x, aggressiveness=None):
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mix = x.detach()
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x = x.clone()
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x = x[:, :, :self.max_bin]
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bandw = x.size()[2] // 2
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aux1 = torch.cat([
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self.stg1_low_band_net(x[:, :, :bandw]),
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self.stg1_high_band_net(x[:, :, bandw:])
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], dim=2)
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h = torch.cat([x, aux1], dim=1)
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aux2 = self.stg2_full_band_net(self.stg2_bridge(h))
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h = torch.cat([x, aux1, aux2], dim=1)
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h = self.stg3_full_band_net(self.stg3_bridge(h))
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mask = torch.sigmoid(self.out(h))
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mask = F.pad(
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input=mask,
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pad=(0, 0, 0, self.output_bin - mask.size()[2]),
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mode='replicate')
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if self.training:
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aux1 = torch.sigmoid(self.aux1_out(aux1))
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aux1 = F.pad(
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input=aux1,
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pad=(0, 0, 0, self.output_bin - aux1.size()[2]),
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mode='replicate')
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aux2 = torch.sigmoid(self.aux2_out(aux2))
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aux2 = F.pad(
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input=aux2,
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pad=(0, 0, 0, self.output_bin - aux2.size()[2]),
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mode='replicate')
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return mask * mix, aux1 * mix, aux2 * mix
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else:
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if aggressiveness:
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mask[:, :, :aggressiveness['split_bin']] = torch.pow(mask[:, :, :aggressiveness['split_bin']], 1 + aggressiveness['value'] / 3)
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mask[:, :, aggressiveness['split_bin']:] = torch.pow(mask[:, :, aggressiveness['split_bin']:], 1 + aggressiveness['value'])
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return mask * mix
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def predict(self, x_mag, aggressiveness=None):
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h = self.forward(x_mag, aggressiveness)
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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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@ -369,6 +369,23 @@ def ensembling(a, specs):
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return spec
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def stft(wave, nfft, hl):
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wave_left = np.asfortranarray(wave[0])
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wave_right = np.asfortranarray(wave[1])
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spec_left = librosa.stft(wave_left, nfft, hop_length=hl)
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spec_right = librosa.stft(wave_right, nfft, hop_length=hl)
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spec = np.asfortranarray([spec_left, spec_right])
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return spec
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def istft(spec, hl):
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spec_left = np.asfortranarray(spec[0])
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spec_right = np.asfortranarray(spec[1])
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wave_left = librosa.istft(spec_left, hop_length=hl)
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wave_right = librosa.istft(spec_right, hop_length=hl)
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wave = np.asfortranarray([wave_left, wave_right])
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if __name__ == "__main__":
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import cv2
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