File size: 3,637 Bytes
1c75048 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 |
import torch
from torch import nn
import torch.nn.functional as F
from uvr5_pack.lib_v5 import layers_33966KB as layers
class BaseASPPNet(nn.Module):
def __init__(self, nin, ch, dilations=(4, 8, 16, 32)):
super(BaseASPPNet, self).__init__()
self.enc1 = layers.Encoder(nin, ch, 3, 2, 1)
self.enc2 = layers.Encoder(ch, ch * 2, 3, 2, 1)
self.enc3 = layers.Encoder(ch * 2, ch * 4, 3, 2, 1)
self.enc4 = layers.Encoder(ch * 4, ch * 8, 3, 2, 1)
self.aspp = layers.ASPPModule(ch * 8, ch * 16, dilations)
self.dec4 = layers.Decoder(ch * (8 + 16), ch * 8, 3, 1, 1)
self.dec3 = layers.Decoder(ch * (4 + 8), ch * 4, 3, 1, 1)
self.dec2 = layers.Decoder(ch * (2 + 4), ch * 2, 3, 1, 1)
self.dec1 = layers.Decoder(ch * (1 + 2), ch, 3, 1, 1)
def __call__(self, x):
h, e1 = self.enc1(x)
h, e2 = self.enc2(h)
h, e3 = self.enc3(h)
h, e4 = self.enc4(h)
h = self.aspp(h)
h = self.dec4(h, e4)
h = self.dec3(h, e3)
h = self.dec2(h, e2)
h = self.dec1(h, e1)
return h
class CascadedASPPNet(nn.Module):
def __init__(self, n_fft):
super(CascadedASPPNet, self).__init__()
self.stg1_low_band_net = BaseASPPNet(2, 16)
self.stg1_high_band_net = BaseASPPNet(2, 16)
self.stg2_bridge = layers.Conv2DBNActiv(18, 8, 1, 1, 0)
self.stg2_full_band_net = BaseASPPNet(8, 16)
self.stg3_bridge = layers.Conv2DBNActiv(34, 16, 1, 1, 0)
self.stg3_full_band_net = BaseASPPNet(16, 32)
self.out = nn.Conv2d(32, 2, 1, bias=False)
self.aux1_out = nn.Conv2d(16, 2, 1, bias=False)
self.aux2_out = nn.Conv2d(16, 2, 1, bias=False)
self.max_bin = n_fft // 2
self.output_bin = n_fft // 2 + 1
self.offset = 128
def forward(self, x, aggressiveness=None):
mix = x.detach()
x = x.clone()
x = x[:, :, :self.max_bin]
bandw = x.size()[2] // 2
aux1 = torch.cat([
self.stg1_low_band_net(x[:, :, :bandw]),
self.stg1_high_band_net(x[:, :, bandw:])
], dim=2)
h = torch.cat([x, aux1], dim=1)
aux2 = self.stg2_full_band_net(self.stg2_bridge(h))
h = torch.cat([x, aux1, aux2], dim=1)
h = self.stg3_full_band_net(self.stg3_bridge(h))
mask = torch.sigmoid(self.out(h))
mask = F.pad(
input=mask,
pad=(0, 0, 0, self.output_bin - mask.size()[2]),
mode='replicate')
if self.training:
aux1 = torch.sigmoid(self.aux1_out(aux1))
aux1 = F.pad(
input=aux1,
pad=(0, 0, 0, self.output_bin - aux1.size()[2]),
mode='replicate')
aux2 = torch.sigmoid(self.aux2_out(aux2))
aux2 = F.pad(
input=aux2,
pad=(0, 0, 0, self.output_bin - aux2.size()[2]),
mode='replicate')
return mask * mix, aux1 * mix, aux2 * mix
else:
if aggressiveness:
mask[:, :, :aggressiveness['split_bin']] = torch.pow(mask[:, :, :aggressiveness['split_bin']], 1 + aggressiveness['value'] / 3)
mask[:, :, aggressiveness['split_bin']:] = torch.pow(mask[:, :, aggressiveness['split_bin']:], 1 + aggressiveness['value'])
return mask * mix
def predict(self, x_mag, aggressiveness=None):
h = self.forward(x_mag, aggressiveness)
if self.offset > 0:
h = h[:, :, :, self.offset:-self.offset]
assert h.size()[3] > 0
return h
|