import torch import torch.nn.functional as F from torch import nn 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