U h@sddlmZddlmZmZddlZddlmZddlm Z ddl m Z ddl m Z mZmZdd lmZdd lmZmZd d d gZGdd d ejZGdd d eZe edejfddddeeeeeddd ZdS))partial)AnyOptionalN)ImageClassification)_log_api_usage_once)register_modelWeights WeightsEnum)_IMAGENET_CATEGORIES)_ovewrite_named_paramhandle_legacy_interfaceAlexNetAlexNet_Weightsalexnetcs<eZdZd eeddfdd Zejejddd ZZ S) r?N) num_classesdropoutreturncs$tt|ttjddddddtjddtjddd tjdd d dd tjddtjddd tjd d ddd tjddtjd dddd tjddtjddddd tjddtjddd  |_t d|_ ttj |dt ddtjddtj |dt ddtjddt d||_ dS)N@ r) kernel_sizestridepaddingT)Zinplace)rr)rrir)r!)pi$i)super__init__rnnZ SequentialZConv2dZReLUZ MaxPool2dfeaturesZAdaptiveAvgPool2davgpoolZDropoutZLinear classifier)selfrr __class__L/var/www/html/venv/lib/python3.8/site-packages/torchvision/models/alexnet.pyr$s6                 zAlexNet.__init__)xrcCs.||}||}t|d}||}|S)Nr)r&r'torchflattenr()r)r.r,r,r-forward/s     zAlexNet.forward)rr) __name__ __module__ __qualname__intfloatr$r/ZTensorr1 __classcell__r,r,r*r-rsc @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: Nrr:T)rAZ check_hash)rverifyr lenr=rZload_state_dictZget_state_dict)r@rArBmodelr,r,r-rPs  ) functoolsrtypingrrr/Ztorch.nnr%Ztransforms._presetsrutilsrZ_apir r r _metar Z_utilsr r__all__Modulerrr>boolrr,r,r,r-s      &