67 lines
2.2 KiB
Python
67 lines
2.2 KiB
Python
import torch
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import torch.nn as nn
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from .utils import load_state_dict_from_url
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from typing import Any
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__all__ = ['AlexNet', 'alexnet']
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model_urls = {
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'alexnet': 'https://download.pytorch.org/models/alexnet-owt-4df8aa71.pth',
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}
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class AlexNet(nn.Module):
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def __init__(self, num_classes: int = 1000) -> None:
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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=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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self.avgpool = nn.AdaptiveAvgPool2d((6, 6))
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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 forward(self, x: torch.Tensor) -> torch.Tensor:
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x = self.features(x)
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x = self.avgpool(x)
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x = torch.flatten(x, 1)
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x = self.classifier(x)
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return x
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def alexnet(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> AlexNet:
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r"""AlexNet model architecture from the
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`"One weird trick..." <https://arxiv.org/abs/1404.5997>`_ paper.
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Args:
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pretrained (bool): If True, returns a model pre-trained on ImageNet
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progress (bool): If True, displays a progress bar of the download to stderr
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"""
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model = AlexNet(**kwargs)
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if pretrained:
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state_dict = load_state_dict_from_url(model_urls['alexnet'],
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progress=progress)
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model.load_state_dict(state_dict)
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return model
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