133 lines
4.1 KiB
Python
133 lines
4.1 KiB
Python
import torch
|
|
from torch import nn
|
|
import torch.nn.functional as F
|
|
from . import layers_new
|
|
|
|
|
|
class BaseNet(nn.Module):
|
|
def __init__(
|
|
self, nin, nout, nin_lstm, nout_lstm, dilations=((4, 2), (8, 4), (12, 6))
|
|
):
|
|
super(BaseNet, self).__init__()
|
|
self.enc1 = layers_new.Conv2DBNActiv(nin, nout, 3, 1, 1)
|
|
self.enc2 = layers_new.Encoder(nout, nout * 2, 3, 2, 1)
|
|
self.enc3 = layers_new.Encoder(nout * 2, nout * 4, 3, 2, 1)
|
|
self.enc4 = layers_new.Encoder(nout * 4, nout * 6, 3, 2, 1)
|
|
self.enc5 = layers_new.Encoder(nout * 6, nout * 8, 3, 2, 1)
|
|
|
|
self.aspp = layers_new.ASPPModule(nout * 8, nout * 8, dilations, dropout=True)
|
|
|
|
self.dec4 = layers_new.Decoder(nout * (6 + 8), nout * 6, 3, 1, 1)
|
|
self.dec3 = layers_new.Decoder(nout * (4 + 6), nout * 4, 3, 1, 1)
|
|
self.dec2 = layers_new.Decoder(nout * (2 + 4), nout * 2, 3, 1, 1)
|
|
self.lstm_dec2 = layers_new.LSTMModule(nout * 2, nin_lstm, nout_lstm)
|
|
self.dec1 = layers_new.Decoder(nout * (1 + 2) + 1, nout * 1, 3, 1, 1)
|
|
|
|
def __call__(self, x):
|
|
e1 = self.enc1(x)
|
|
e2 = self.enc2(e1)
|
|
e3 = self.enc3(e2)
|
|
e4 = self.enc4(e3)
|
|
e5 = self.enc5(e4)
|
|
|
|
h = self.aspp(e5)
|
|
|
|
h = self.dec4(h, e4)
|
|
h = self.dec3(h, e3)
|
|
h = self.dec2(h, e2)
|
|
h = torch.cat([h, self.lstm_dec2(h)], dim=1)
|
|
h = self.dec1(h, e1)
|
|
|
|
return h
|
|
|
|
|
|
class CascadedNet(nn.Module):
|
|
def __init__(self, n_fft, nout=32, nout_lstm=128):
|
|
super(CascadedNet, self).__init__()
|
|
|
|
self.max_bin = n_fft // 2
|
|
self.output_bin = n_fft // 2 + 1
|
|
self.nin_lstm = self.max_bin // 2
|
|
self.offset = 64
|
|
|
|
self.stg1_low_band_net = nn.Sequential(
|
|
BaseNet(2, nout // 2, self.nin_lstm // 2, nout_lstm),
|
|
layers_new.Conv2DBNActiv(nout // 2, nout // 4, 1, 1, 0),
|
|
)
|
|
|
|
self.stg1_high_band_net = BaseNet(
|
|
2, nout // 4, self.nin_lstm // 2, nout_lstm // 2
|
|
)
|
|
|
|
self.stg2_low_band_net = nn.Sequential(
|
|
BaseNet(nout // 4 + 2, nout, self.nin_lstm // 2, nout_lstm),
|
|
layers_new.Conv2DBNActiv(nout, nout // 2, 1, 1, 0),
|
|
)
|
|
self.stg2_high_band_net = BaseNet(
|
|
nout // 4 + 2, nout // 2, self.nin_lstm // 2, nout_lstm // 2
|
|
)
|
|
|
|
self.stg3_full_band_net = BaseNet(
|
|
3 * nout // 4 + 2, nout, self.nin_lstm, nout_lstm
|
|
)
|
|
|
|
self.out = nn.Conv2d(nout, 2, 1, bias=False)
|
|
self.aux_out = nn.Conv2d(3 * nout // 4, 2, 1, bias=False)
|
|
|
|
def forward(self, x):
|
|
x = x[:, :, : self.max_bin]
|
|
|
|
bandw = x.size()[2] // 2
|
|
l1_in = x[:, :, :bandw]
|
|
h1_in = x[:, :, bandw:]
|
|
l1 = self.stg1_low_band_net(l1_in)
|
|
h1 = self.stg1_high_band_net(h1_in)
|
|
aux1 = torch.cat([l1, h1], dim=2)
|
|
|
|
l2_in = torch.cat([l1_in, l1], dim=1)
|
|
h2_in = torch.cat([h1_in, h1], dim=1)
|
|
l2 = self.stg2_low_band_net(l2_in)
|
|
h2 = self.stg2_high_band_net(h2_in)
|
|
aux2 = torch.cat([l2, h2], dim=2)
|
|
|
|
f3_in = torch.cat([x, aux1, aux2], dim=1)
|
|
f3 = self.stg3_full_band_net(f3_in)
|
|
|
|
mask = torch.sigmoid(self.out(f3))
|
|
mask = F.pad(
|
|
input=mask,
|
|
pad=(0, 0, 0, self.output_bin - mask.size()[2]),
|
|
mode="replicate",
|
|
)
|
|
|
|
if self.training:
|
|
aux = torch.cat([aux1, aux2], dim=1)
|
|
aux = torch.sigmoid(self.aux_out(aux))
|
|
aux = F.pad(
|
|
input=aux,
|
|
pad=(0, 0, 0, self.output_bin - aux.size()[2]),
|
|
mode="replicate",
|
|
)
|
|
return mask, aux
|
|
else:
|
|
return mask
|
|
|
|
def predict_mask(self, x):
|
|
mask = self.forward(x)
|
|
|
|
if self.offset > 0:
|
|
mask = mask[:, :, :, self.offset : -self.offset]
|
|
assert mask.size()[3] > 0
|
|
|
|
return mask
|
|
|
|
def predict(self, x, aggressiveness=None):
|
|
mask = self.forward(x)
|
|
pred_mag = x * mask
|
|
|
|
if self.offset > 0:
|
|
pred_mag = pred_mag[:, :, :, self.offset : -self.offset]
|
|
assert pred_mag.size()[3] > 0
|
|
|
|
return pred_mag
|