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import torch
from torch import nn
import torch.nn.functional as F
from . import layers
class BaseASPPNet(nn.Module):
def __init__(self, nn_architecture, nin, ch, dilations=(4, 8, 16)):
super(BaseASPPNet, self).__init__()
self.nn_architecture = nn_architecture
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)
if self.nn_architecture == 129605:
self.enc5 = layers.Encoder(ch * 8, ch * 16, 3, 2, 1)
self.aspp = layers.ASPPModule(nn_architecture, ch * 16, ch * 32, dilations)
self.dec5 = layers.Decoder(ch * (16 + 32), ch * 16, 3, 1, 1)
else:
self.aspp = layers.ASPPModule(nn_architecture, 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)
if self.nn_architecture == 129605:
h, e5 = self.enc5(h)
h = self.aspp(h)
h = self.dec5(h, e5)
else:
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
def determine_model_capacity(n_fft_bins, nn_architecture):
sp_model_arch = [31191, 33966, 129605]
hp_model_arch = [123821, 123812]
hp2_model_arch = [537238, 537227]
if nn_architecture in sp_model_arch:
model_capacity_data = [
(2, 16),
(2, 16),
(18, 8, 1, 1, 0),
(8, 16),
(34, 16, 1, 1, 0),
(16, 32),
(32, 2, 1),
(16, 2, 1),
(16, 2, 1),
]
if nn_architecture in hp_model_arch:
model_capacity_data = [
(2, 32),
(2, 32),
(34, 16, 1, 1, 0),
(16, 32),
(66, 32, 1, 1, 0),
(32, 64),
(64, 2, 1),
(32, 2, 1),
(32, 2, 1),
]
if nn_architecture in hp2_model_arch:
model_capacity_data = [
(2, 64),
(2, 64),
(66, 32, 1, 1, 0),
(32, 64),
(130, 64, 1, 1, 0),
(64, 128),
(128, 2, 1),
(64, 2, 1),
(64, 2, 1),
]
cascaded = CascadedASPPNet
model = cascaded(n_fft_bins, model_capacity_data, nn_architecture)
return model
class CascadedASPPNet(nn.Module):
def __init__(self, n_fft, model_capacity_data, nn_architecture):
super(CascadedASPPNet, self).__init__()
self.stg1_low_band_net = BaseASPPNet(nn_architecture, *model_capacity_data[0])
self.stg1_high_band_net = BaseASPPNet(nn_architecture, *model_capacity_data[1])
self.stg2_bridge = layers.Conv2DBNActiv(*model_capacity_data[2])
self.stg2_full_band_net = BaseASPPNet(nn_architecture, *model_capacity_data[3])
self.stg3_bridge = layers.Conv2DBNActiv(*model_capacity_data[4])
self.stg3_full_band_net = BaseASPPNet(nn_architecture, *model_capacity_data[5])
self.out = nn.Conv2d(*model_capacity_data[6], bias=False)
self.aux1_out = nn.Conv2d(*model_capacity_data[7], bias=False)
self.aux2_out = nn.Conv2d(*model_capacity_data[8], bias=False)
self.max_bin = n_fft // 2
self.output_bin = n_fft // 2 + 1
self.offset = 128
def forward(self, x):
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:
return mask# * mix
def predict_mask(self, x):
mask = self.forward(x)
if self.offset > 0:
mask = mask[:, :, :, self.offset:-self.offset]
return mask