41 lines
1.4 KiB
Python
41 lines
1.4 KiB
Python
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import torch.nn as nn
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import torch.nn.functional as F
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import numpy as np
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def conv_init(m):
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classname = m.__class__.__name__
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if classname.find('Conv') != -1:
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#nn.init.xavier_uniform(m.weight, gain=np.sqrt(2))
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nn.init.normal_(m.weight, mean=0, std=1)
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nn.init.constant(m.bias, 0)
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class AlexNet(nn.Module):
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def __init__(self, num_classes, inputs=3):
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super(AlexNet, self).__init__()
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self.features = nn.Sequential(
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nn.Conv2d(inputs, 64, kernel_size=11, stride=4, padding=5),
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nn.ReLU(inplace=True),
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nn.Dropout(p=0.5),
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nn.MaxPool2d(kernel_size=2, stride=2),
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nn.Conv2d(64, 192, kernel_size=5, padding=2),
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nn.ReLU(inplace=True),
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nn.MaxPool2d(kernel_size=2, stride=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.Dropout(p=0.5),
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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.Dropout(p=0.5),
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nn.MaxPool2d(kernel_size=2, stride=2),
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)
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self.classifier = nn.Linear(256, num_classes)
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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), -1)
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x = self.classifier(x)
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return x
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