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