a PSic@sddlmZddlmZmZddlZddlmZddlm Z ddl m Z ddl m Z mZdd lmZdd lmZmZgd ZGd d d ejZGddde ZedejfddddeeeeedddZddlmZedejjiZdS))partial)AnyOptionalN)ImageClassification)_log_api_usage_once) WeightsEnumWeights)_IMAGENET_CATEGORIES)handle_legacy_interface_ovewrite_named_param)AlexNetAlexNet_Weightsalexnetcs<eZdZd eeddfdd Zejejddd ZZ S) r?N) num_classesdropoutreturncs$tt|ttjddddddtjddtjddd tjdd d dd tjddtjddd tjd d ddd tjddtjd dddd tjddtjddddd tjddtjddd  |_t d|_ ttj |dt ddtjddtj |dt ddtjddt d||_ dS)N@ r) kernel_sizestridepaddingT)inplace)rr)rrir)r!)pi$i)super__init__rnn SequentialConv2dReLU MaxPool2dfeaturesAdaptiveAvgPool2davgpoolDropoutLinear classifier)selfrr __class__V/var/www/html/django/DPS/env/lib/python3.9/site-packages/torchvision/models/alexnet.pyr$s6                 zAlexNet.__init__)xrcCs.||}||}t|d}||}|S)Nr)r*r,torchflattenr/)r0r5r3r3r4forward/s     zAlexNet.forward)rr) __name__ __module__ __qualname__intfloatr$r6Tensorr8 __classcell__r3r3r1r4rsrc @s>eZdZedeeddddedddd d id d d ZeZdS)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: NrrE)rO)rverifyr lenrJrload_state_dictZget_state_dict)rNrOrPmodelr3r3r4rNs r) _ModelURLs) functoolsrtypingrrr6torch.nnr%Ztransforms._presetsrutilsr_apir r _metar _utilsr r __all__ModulerrrKboolrrUrHZ model_urlsr3r3r3r4s$     &")