39 lines
1.2 KiB
Python
39 lines
1.2 KiB
Python
import torch.nn as nn
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CIFAR10_NUM_CLASSES = 10
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class AlexNet(nn.Module):
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def __init__(self, /, num_classes: int):
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super(AlexNet, self).__init__()
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self.features = nn.Sequential(
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nn.Conv2d(3, 64, kernel_size=3, stride=2, padding=1),
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nn.ReLU(inplace=True),
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nn.MaxPool2d(kernel_size=2),
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nn.Conv2d(64, 192, kernel_size=3, padding=1),
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nn.ReLU(inplace=True),
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nn.MaxPool2d(kernel_size=2),
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nn.Conv2d(192, 384, kernel_size=3, padding=1),
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nn.ReLU(inplace=True),
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nn.Conv2d(384, 256, kernel_size=3, padding=1),
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nn.ReLU(inplace=True),
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nn.Conv2d(256, 256, kernel_size=3, padding=1),
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nn.ReLU(inplace=True),
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nn.MaxPool2d(kernel_size=2),
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)
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self.classifier = nn.Sequential(
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nn.Dropout(),
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nn.Linear(256 * 2 * 2, 4096),
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nn.ReLU(inplace=True),
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nn.Dropout(),
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nn.Linear(4096, 4096),
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nn.ReLU(inplace=True),
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nn.Linear(4096, num_classes),
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)
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def forward(self, x):
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x = self.features(x)
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x = x.view(x.size(0), 256 * 2 * 2)
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x = self.classifier(x)
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return x
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