31 lines
1.1 KiB
Python
31 lines
1.1 KiB
Python
import torch.nn as nn
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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 = 32
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self.model = nn.Sequential(
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nn.Conv1d(1, layers, kernel_size=5, stride=2, padding=2),
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nn.BatchNorm1d(layers),
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nn.LeakyReLU(0.2, inplace=True),
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nn.Conv1d(layers, layers * 2, kernel_size=5, stride=2, padding=2),
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nn.BatchNorm1d(layers * 2),
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nn.LeakyReLU(0.2, inplace=True),
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nn.Conv1d(layers * 2, layers * 4, kernel_size=5, stride=2, padding=2),
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nn.BatchNorm1d(layers * 4),
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nn.LeakyReLU(0.2, inplace=True),
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nn.Conv1d(layers * 4, layers * 8, kernel_size=5, stride=2, padding=2),
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nn.BatchNorm1d(layers * 8),
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nn.LeakyReLU(0.2, inplace=True),
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nn.Conv1d(layers * 8, 1, kernel_size=3, padding=1),
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)
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self.global_avg_pool = nn.AdaptiveAvgPool1d(1)
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self.sigmoid = nn.Sigmoid()
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def forward(self, x):
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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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x = self.sigmoid(x)
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return x
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