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- # Modified from https://github.com/pytorch/vision
- import torch.nn as nn
- import torch.utils.model_zoo as model_zoo
-
-
- __all__ = ['AlexNet', 'alexnet']
-
-
- model_urls = {
- 'alexnet': 'https://download.pytorch.org/models/alexnet-owt-4df8aa71.pth',
- }
-
-
- class AlexNet(nn.Module):
-
- def __init__(self, num_classes=1000):
- super(AlexNet, self).__init__()
- self.features = nn.Sequential(
- nn.Conv2d(3, 64, kernel_size=11, stride=4, padding=2),
- nn.ReLU(inplace=True),
- nn.MaxPool2d(kernel_size=3, stride=2),
- nn.Conv2d(64, 192, kernel_size=5, padding=2),
- nn.ReLU(inplace=True),
- nn.MaxPool2d(kernel_size=3, stride=2),
- nn.Conv2d(192, 384, kernel_size=3, padding=1),
- nn.ReLU(inplace=True),
- nn.Conv2d(384, 256, kernel_size=3, padding=1),
- nn.ReLU(inplace=True),
- nn.Conv2d(256, 256, kernel_size=3, padding=1),
- nn.ReLU(inplace=True),
- nn.MaxPool2d(kernel_size=3, stride=2),
- )
- self.classifier = nn.Sequential(
- nn.Dropout(),
- nn.Linear(256 * 6 * 6, 4096),
- nn.ReLU(inplace=True),
- nn.Dropout(),
- nn.Linear(4096, 4096),
- nn.ReLU(inplace=True),
- nn.Linear(4096, num_classes),
- )
-
- def forward(self, x):
- x = self.features(x)
- x = x.view(x.size(0), 256 * 6 * 6)
- x = self.classifier(x)
- return x
-
-
- def alexnet(pretrained=False, **kwargs):
- r"""AlexNet model architecture from the
- `"One weird trick..." <https://arxiv.org/abs/1404.5997>`_ paper.
-
- Args:
- pretrained (bool): If True, returns a model pre-trained on ImageNet
- """
- num_classes = kwargs.pop('num_classes', None)
- model = AlexNet(**kwargs)
- if pretrained:
- model.load_state_dict(model_zoo.load_url(model_urls['alexnet']))
- if num_classes is not None and num_classes!=1000:
- model.classifier[-1] = nn.Linear(4096, num_classes)
- return model
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