54 lines
1.5 KiB
Python
54 lines
1.5 KiB
Python
import torch
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import torch.nn as nn
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class AlexNetCustom(nn.Module):
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def __init__(self,
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in_channels: int = 3,
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out_classes: int=1_000):
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super().__init__()
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self._out_classes = out_classes
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self.feature = nn.Sequential(
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nn.Conv2d(in_channels, 96, kernel_size=11, stride=4, padding=2),
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nn.ReLU(inplace=True),
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nn.MaxPool2d(kernel_size=3, stride=2),
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nn.Conv2d(96, 256, kernel_size=5, padding=2),
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nn.ReLU(inplace=True),
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nn.MaxPool2d(kernel_size=3, stride=2),
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nn.Conv2d(256, 384, kernel_size=3, padding=1),
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nn.ReLU(inplace=True),
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nn.Conv2d(384, 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.MaxPool2d(kernel_size=3, stride=2)
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)
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self.avgpool = nn.AdaptiveAvgPool2d((6, 6))
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self.mlp = nn.Sequential(
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nn.Dropout(p=0.5),
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nn.Linear(256*6*6, 4096),
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nn.ReLU(inplace=True),
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nn.Dropout(p=0.5),
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nn.Linear(4096, 4096),
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nn.ReLU(inplace=True),
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nn.Linear(4096, self._out_classes)
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)
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def forward(self, image):
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x = self.feature(image)
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x = self.avgpool(x)
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x = torch.flatten(x, 1)
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x = self.mlp(x)
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return x
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