124 lines
3.8 KiB
Python
124 lines
3.8 KiB
Python
import numpy as np
|
|
import torch
|
|
import torch.nn.functional as F
|
|
from torch import nn
|
|
|
|
from . import layers_537238KB as layers
|
|
|
|
|
|
class BaseASPPNet(nn.Module):
|
|
def __init__(self, nin, ch, dilations=(4, 8, 16)):
|
|
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, 64)
|
|
self.stg1_high_band_net = BaseASPPNet(2, 64)
|
|
|
|
self.stg2_bridge = layers.Conv2DBNActiv(66, 32, 1, 1, 0)
|
|
self.stg2_full_band_net = BaseASPPNet(32, 64)
|
|
|
|
self.stg3_bridge = layers.Conv2DBNActiv(130, 64, 1, 1, 0)
|
|
self.stg3_full_band_net = BaseASPPNet(64, 128)
|
|
|
|
self.out = nn.Conv2d(128, 2, 1, bias=False)
|
|
self.aux1_out = nn.Conv2d(64, 2, 1, bias=False)
|
|
self.aux2_out = nn.Conv2d(64, 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
|