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77 lines
2.4 KiB
77 lines
2.4 KiB
from torch import nn
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import torch.nn.functional as F
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class Encoder(nn.Module):
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def __init__(self, in_channels=1, h=256, dropout=0.5):
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super(Encoder, self).__init__()
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self.conv1 = nn.Conv2d(in_channels, 8, kernel_size=5, stride=1)
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self.conv2 = nn.Conv2d(8, 16, kernel_size=5, stride=1)
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self.conv3 = nn.Conv2d(16, 120, kernel_size=4, stride=1)
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self.pool = nn.MaxPool2d(kernel_size=2, stride=2)
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self.relu = nn.ReLU()
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self.dropout1 = nn.Dropout2d(dropout)
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self.dropout2 = nn.Dropout(dropout)
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self.fc = nn.Linear(480, 500)
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for m in self.modules():
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if isinstance(m, nn.Conv2d) or isinstance(m, nn.Linear):
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nn.init.kaiming_normal_(m.weight)
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def forward(self, x):
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bs = x.size(0)
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x = self.pool(self.relu(self.conv1(x)))
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x = self.pool(self.relu(self.conv2(x)))
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# x = self.dropout1(self.relu(self.conv3(x)))
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x = self.relu(self.conv3(x))
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x = x.view(bs, -1)
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x = self.dropout2(self.fc(x))
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return x
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class Classifier(nn.Module):
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def __init__(self, n_classes, dropout=0.5):
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super(Classifier, self).__init__()
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self.l1 = nn.Linear(500, n_classes)
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for m in self.modules():
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if isinstance(m, nn.Conv2d) or isinstance(m, nn.Linear):
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nn.init.kaiming_normal_(m.weight)
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def forward(self, x):
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x = self.l1(x)
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return x
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class CNN(nn.Module):
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def __init__(self, in_channels=1, n_classes=10, target=False):
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super(CNN, self).__init__()
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self.encoder = Encoder(in_channels=in_channels)
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self.classifier = Classifier(n_classes)
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if target:
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for param in self.classifier.parameters():
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param.requires_grad = False
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def forward(self, x):
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x = self.encoder(x)
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x = self.classifier(x)
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return x
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class Discriminator(nn.Module):
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def __init__(self, h=500, args=None):
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super(Discriminator, self).__init__()
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self.l1 = nn.Linear(500, h)
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self.l2 = nn.Linear(h, h)
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self.l3 = nn.Linear(h, 2)
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self.slope = args.slope
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for m in self.modules():
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if isinstance(m, nn.Conv2d) or isinstance(m, nn.Linear):
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nn.init.kaiming_normal_(m.weight)
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def forward(self, x):
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x = F.leaky_relu(self.l1(x), self.slope)
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x = F.leaky_relu(self.l2(x), self.slope)
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x = self.l3(x)
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return x
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