53 lines
1.8 KiB
Python
53 lines
1.8 KiB
Python
import torch.nn as nn
|
|
|
|
def conv_block(in_channels, out_channels, kernel_size=3, dilation=1):
|
|
return nn.Sequential(
|
|
nn.Conv1d(in_channels, out_channels, kernel_size=kernel_size, dilation=dilation, padding=(kernel_size // 2) * dilation),
|
|
nn.BatchNorm1d(out_channels),
|
|
nn.PReLU()
|
|
)
|
|
|
|
class AttentionBlock(nn.Module):
|
|
def __init__(self, channels):
|
|
super(AttentionBlock, self).__init__()
|
|
self.attention = nn.Sequential(
|
|
nn.Conv1d(channels, channels // 4, kernel_size=1),
|
|
nn.ReLU(),
|
|
nn.Conv1d(channels // 4, channels, kernel_size=1),
|
|
nn.Sigmoid()
|
|
)
|
|
|
|
def forward(self, x):
|
|
attention_weights = self.attention(x)
|
|
return x * attention_weights
|
|
|
|
class ResidualInResidualBlock(nn.Module):
|
|
def __init__(self, channels, num_convs=3):
|
|
super(ResidualInResidualBlock, self).__init__()
|
|
self.conv_layers = nn.Sequential(*[conv_block(channels, channels) for _ in range(num_convs)])
|
|
self.attention = AttentionBlock(channels)
|
|
|
|
def forward(self, x):
|
|
residual = x
|
|
x = self.conv_layers(x)
|
|
x = self.attention(x)
|
|
return x + residual
|
|
|
|
class SISUGenerator(nn.Module):
|
|
def __init__(self, layer=64, num_rirb=4): #increased base layer and rirb amounts
|
|
super(SISUGenerator, self).__init__()
|
|
self.conv1 = nn.Sequential(
|
|
nn.Conv1d(1, layer, kernel_size=7, padding=3),
|
|
nn.BatchNorm1d(layer),
|
|
nn.PReLU(),
|
|
)
|
|
self.rir_blocks = nn.Sequential(*[ResidualInResidualBlock(layer) for _ in range(num_rirb)])
|
|
self.final_layer = nn.Conv1d(layer, 1, kernel_size=3, padding=1)
|
|
|
|
def forward(self, x):
|
|
residual = x
|
|
x = self.conv1(x)
|
|
x = self.rir_blocks(x)
|
|
x = self.final_layer(x)
|
|
return x + residual
|