"""
Modified from https://github.com/pytorch/vision/blob/main/torchvision/models/alexnet.py
"""
from typing import Any
import oneflow as flow
import oneflow.nn as nn
from .utils import load_state_dict_from_url
from .registry import ModelCreator
__all__ = ["AlexNet", "alexnet"]
model_urls = {
"alexnet": "https://oneflow-public.oss-cn-beijing.aliyuncs.com/model_zoo/flowvision/classification/AlexNet/alexnet_oneflow_model.tar.gz",
}
class AlexNet(nn.Module):
def __init__(self, num_classes: int = 1000) -> None:
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.avgpool = nn.AdaptiveAvgPool2d((6, 6))
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: flow.Tensor) -> flow.Tensor:
x = self.features(x)
x = self.avgpool(x)
x = flow.flatten(x, 1)
x = self.classifier(x)
return x
[docs]@ModelCreator.register_model
def alexnet(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> AlexNet:
"""
Constructs the AlexNet model.
.. note::
AlexNet model architecture from the `One weird trick... <https://arxiv.org/abs/1404.5997>`_ paper.
The required minimum input size of this model is 63x63.
Args:
pretrained (bool): Whether to download the pre-trained model on ImageNet. Default: ``False``
progress (bool): If True, displays a progress bar of the download to stderr. Default: ``True``
For example:
.. code-block:: python
>>> import flowvision
>>> alexnet = flowvision.models.alexnet(pretrained=False, progress=True)
"""
model = AlexNet(**kwargs)
if pretrained:
state_dict = load_state_dict_from_url(model_urls["alexnet"], progress=progress)
model.load_state_dict(state_dict)
return model