37 lines
1.9 KiB
Python
37 lines
1.9 KiB
Python
import torch
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import torch.nn as nn
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import torch.nn.utils as utils
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def discriminator_block(in_channels, out_channels, kernel_size=3, stride=1, dilation=1):
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padding = (kernel_size // 2) * dilation
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return nn.Sequential(
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utils.spectral_norm(nn.Conv1d(in_channels, out_channels, kernel_size=kernel_size, stride=stride, dilation=dilation, padding=padding)),
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nn.LeakyReLU(0.2, inplace=True),
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nn.BatchNorm1d(out_channels)
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)
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class SISUDiscriminator(nn.Module):
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def __init__(self):
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super(SISUDiscriminator, self).__init__()
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layers = 4 # Increased base layer count
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self.model = nn.Sequential(
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discriminator_block(1, layers, kernel_size=7, stride=2), # Initial downsampling
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discriminator_block(layers, layers * 2, kernel_size=5, stride=2), # Downsampling
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discriminator_block(layers * 2, layers * 4, kernel_size=5, dilation=2), # Increased dilation
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discriminator_block(layers * 4, layers * 4, kernel_size=5, dilation=4), # Increased dilation
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discriminator_block(layers * 4, layers * 8, kernel_size=5, dilation=8), # Deeper layer!
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discriminator_block(layers * 8, layers * 8, kernel_size=5, dilation=1), # Deeper layer!
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discriminator_block(layers * 8, layers * 4, kernel_size=3, dilation=2), # Reduced dilation
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discriminator_block(layers * 4, layers * 2, kernel_size=3, dilation=1),
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discriminator_block(layers * 2, layers, kernel_size=3, stride=1), # Final convolution
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discriminator_block(layers, 1, kernel_size=3, stride=1)
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)
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self.global_avg_pool = nn.AdaptiveAvgPool1d(1)
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def forward(self, x):
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# Gaussian noise is not necessary here for discriminator as it is already implicit in the training process
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x = self.model(x)
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x = self.global_avg_pool(x)
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x = x.view(-1, 1)
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return x
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