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import logging
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import torch.nn as nn
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class AlexNet(nn.Module):
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"""AlexNet backbone.
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Args:
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num_classes (int): number of classes for classification.
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"""
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def __init__(self, num_classes=-1):
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super(AlexNet, self).__init__()
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self.num_classes = num_classes
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self.features = nn.Sequential(
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nn.Conv2d(3, 64, 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(64, 192, 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(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=3, stride=2),
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)
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if self.num_classes > 0:
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self.classifier = nn.Sequential(
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nn.Dropout(),
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nn.Linear(256 * 6 * 6, 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 init_weights(self, pretrained=None):
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if isinstance(pretrained, str):
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logger = logging.getLogger()
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from ..runner import load_checkpoint
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load_checkpoint(self, pretrained, strict=False, logger=logger)
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elif pretrained is None:
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pass
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else:
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raise TypeError('pretrained must be a str or None')
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def forward(self, x):
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x = self.features(x)
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if self.num_classes > 0:
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x = x.view(x.size(0), 256 * 6 * 6)
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x = self.classifier(x)
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return x
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