o V h @sddlmZddlmZmZddlZddlmZddlm Z ddl m Z ddl m Z mZmZdd lmZdd lmZmZgd ZGd d d ejZGdddeZe edejfdddddeedededefddZdS))partial)AnyOptionalN)ImageClassification)_log_api_usage_once)register_modelWeights WeightsEnum)_IMAGENET_CATEGORIES)_ovewrite_named_paramhandle_legacy_interface)AlexNetAlexNet_WeightsalexnetcsBeZdZd dededdffdd Zd ejdejfd d ZZ S) r? num_classesdropoutreturnNcs$tt|ttjddddddtjddtjddd tjdd d dd tjddtjddd tjd d ddd tjddtjd dddd tjddtjddddd tjddtjddd  |_t d|_ ttj |dt ddtjddtj |dt ddtjddt d||_ dS)N@ r) kernel_sizestridepaddingT)inplace)rr)rrir)r")pi$i)super__init__rnn SequentialConv2dReLU MaxPool2dfeaturesAdaptiveAvgPool2davgpoolDropoutLinear classifier)selfrr __class__N/var/www/vscode/kcb/lib/python3.10/site-packages/torchvision/models/alexnet.pyr%s6                  zAlexNet.__init__xcCs.||}||}t|d}||}|S)Nr)r+r-torchflattenr0)r1r6r4r4r5forward/s    zAlexNet.forward)rr) __name__ __module__ __qualname__intfloatr%r7Tensorr9 __classcell__r4r4r2r5rsrc @sBeZdZedeeddddedddd d id d d ddZeZdS)rz`__. .. note:: AlexNet was originally introduced in the `ImageNet Classification with Deep Convolutional Neural Networks `__ paper. Our implementation is based instead on the "One weird trick" paper above. Args: weights (:class:`~torchvision.models.AlexNet_Weights`, optional): The pretrained weights to use. See :class:`~torchvision.models.AlexNet_Weights` below for more details, and possible values. By default, no pre-trained weights are used. progress (bool, optional): If True, displays a progress bar of the download to stderr. Default is True. **kwargs: parameters passed to the ``torchvision.models.squeezenet.AlexNet`` base class. Please refer to the `source code `_ for more details about this class. .. autoclass:: torchvision.models.AlexNet_Weights :members: NrrFT)rS check_hashr4)rverifyr lenrNrload_state_dictget_state_dict)rRrSrTmodelr4r4r5rPs r) functoolsrtypingrrr7torch.nnr&transforms._presetsrutilsr_apir r r _metar _utilsr r__all__ModulerrrOboolrr4r4r4r5s     &.