h ,SSKJr SSKJrJr SSKrSSKJr SSKJ r SSK J r SSK J r JrJr SS KJr SS KJrJr /S Qr"S S \R,5r"SS\5r\ "5\"S\R24S9SSS.S\\S\S\S\4Sjj55rg))partial)AnyOptionalN)ImageClassification)_log_api_usage_once)register_modelWeights WeightsEnum)_IMAGENET_CATEGORIES)_ovewrite_named_paramhandle_legacy_interface)AlexNetAlexNet_Weightsalexnetcv^\rSrSrS S\S\SS4U4SjjjrS\RS\R4Sjr S r U=r $) r num_classesdropoutreturnNc>[TU]5 [U5 [R"[R "SSSSSS9[R "SS9[R"SSS 9[R "SS S SS 9[R "SS9[R"SSS 9[R "S S SSS 9[R "SS9[R "S SSSS 9[R "SS9[R "SSSSS 9[R "SS9[R"SSS 95 Ul[R"S5Ul [R"[R"US9[R"SS5[R "SS9[R"US9[R"SS5[R "SS9[R"SU55Ul g)N@ r) kernel_sizestridepaddingT)inplace)rr)rrir )r$)pi$i)super__init__rnn SequentialConv2dReLU MaxPool2dfeaturesAdaptiveAvgPool2davgpoolDropoutLinear classifier)selfrr __class__s R/var/www/auris/envauris/lib/python3.13/site-packages/torchvision/models/alexnet.pyr'AlexNet.__init__sf D! IIaAq A GGD ! LLQq 1 IIb#1a 8 GGD ! LLQq 1 IIc3Aq 9 GGD ! IIc3Aq 9 GGD ! IIc3Aq 9 GGD ! LLQq 1  ++F3 -- JJ ! IIk4 ( GGD ! JJ ! IIdD ! GGD ! IIdK ( xcURU5nURU5n[R"US5nUR U5nU$)Nr )r-r/torchflattenr2)r3r8s r5forwardAlexNet.forward/s@ MM!  LLO MM!Q  OOA r7)r/r2r-)ig?) __name__ __module__ __qualname____firstlineno__intfloatr'r:Tensorr<__static_attributes__ __classcell__)r4s@r5rrsB C    :%,,r7rc N\rSrSr\"S\"\SS9SS\SSS S S .0S S SS.S9r\r Sr g)r7zr?r@rAr rrr IMAGENET1K_V1DEFAULTrErWr7r5rr7sR J.#>" .m##  ! M*Gr7r pretrained)weightsT)r[progressr[r\kwargsrc [RU5nUb#[US[URS55 [ S0UD6nUbUR URUSS95 U$)aAlexNet model architecture from `One weird trick for parallelizing convolutional neural networks `__. .. 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: rrNT)r\ check_hashrW)rverifyrlenrVrload_state_dictget_state_dict)r[r\r]models r5rrPsk:$$W-GfmSl9S5TU  f E g44hSW4XY Lr7) functoolsrtypingrrr:torch.nnr(transforms._presetsrutilsr_apir r r _metar _utilsrr__all__ModulerrrXboolrrWr7r5rps 5'66'B 4#bii#Lk2,0M0M!NO484%1%D%[^%cj%P%r7