U (d2@sddlZddlmZddlmZddlmZmZmZm Z m Z ddl Z ddl m Z ddlm mZddl mZddlmZddlmZd d lmZmZd d lmZd d lmZmZd ddddgZeddddgZeeeeede_ eZ!Gdd d e j"Z#Gddde j"Z$Gddde j"Z%Gddde j"Z&GdddeZ'ede'j(fddd d!ee'e)e e#d"d#dZ*d d$lm+Z+e+de'j(j,iZ-dS)%N) namedtuple)partial)OptionalTupleListCallableAny)Tensor)ImageClassification)_log_api_usage_once) WeightsEnumWeights)_IMAGENET_CATEGORIES)handle_legacy_interface_ovewrite_named_param GoogLeNetGoogLeNetOutputs_GoogLeNetOutputsGoogLeNet_Weights googlenetlogits aux_logits2 aux_logits1)rrrc seZdZddgZdeeeeeeeed e j fe e dd fd d Z e e d ddZe ee ee ee fd ddZejje e ee edddZe ed ddZZS)r aux_logitstransform_inputTFN皙?ffffff?.) num_classesrr init_weightsblocksdropout dropout_auxreturnc stt||dkr$tttg}|dkrtdt |||S| |||SdS)Nz8Scripted GoogleNet always returns GoogleNetOutputs Tuple) ruryrwrrgjitZ is_scriptingrGrHrr{)rkrsr^r_Z aux_definedrprprqforwards     zGoogLeNet.forward)rTFNNrr)__name__ __module__ __qualname__Z __constants__intboolrrrrMModulefloatrCr rurryrgr|Zunusedrr{r} __classcell__rprprnrqrs.F"7c sfeZdZd eeeeeeeeedejfdd fdd Ze e e dddZ e e dd d Z Z S) rEN.) in_channelsch1x1ch3x3redch3x3ch5x5redch5x5 pool_projrlr%c st|dkrt}|||dd|_t|||dd|||ddd|_t|||dd|||ddd|_ttjddddd|||dd|_ dS)Nr r-r&r/T)r)r*r+r,) rBrCrDbranch1rMZ Sequentialbranch2branch3rNbranch4) rkrrrrrrrrlrnrprqrCs    zInception.__init__rrcCs8||}||}||}||}||||g}|Srz)rrrr)rkrsrrrroutputsrprprqrys      zInception._forwardcCs||}t|dSrv)ryrgrt)rkrsrrprprqr}s zInception.forward)N)r~rrrrrrMrrCr rryr}rrprprnrqrEs   rEcsJeZdZd eeeedejfeddfdd Z e e ddd Z Z S) rFNr.)rr rlr#r%csTt|dkrt}||ddd|_tdd|_td||_tj|d|_ dS)Nr1r r-ir>r<) rBrCrDconvrMrbfc1fc2rar#)rkrr rlr#rnrprqrCs zInceptionAux.__init__rrcCsNt|d}||}t|d}tj||dd}||}||}|S)N)rr TZinplace) FZadaptive_avg_pool2drrgrxrelurr#rrkrsrprprqr}s     zInceptionAux.forward)Nr) r~rrrrrrMrrrCr r}rrprprnrqrFsrFcs8eZdZeeeddfdd ZeedddZZS)rDN)r out_channelskwargsr%c s8ttj||fddi||_tj|dd|_dS)NrjFgMbP?)Zeps)rBrCrMrfrribn)rkrrrrnrprqrC s zBasicConv2d.__init__rrcCs"||}||}tj|ddS)NTr)rrrrrrprprqr}s  zBasicConv2d.forward) r~rrrrrCr r}rrprprnrqrD srDc @s>eZdZedeeddddedddd d id d d ZeZdS)rz:https://download.pytorch.org/models/googlenet-1378be20.pthr9)Z crop_sizeie)rzOhttps://github.com/pytorch/vision/tree/main/references/classification#googlenetz ImageNet-1KgoqQ@gRaV@)zacc@1zacc@5z1These weights are ported from the original paper.)Z num_paramsZmin_size categoriesZrecipeZ_metricsZ_docs)urlZ transformsmetaN) r~rrrrr r IMAGENET1K_V1DEFAULTrprprprqrs  Z pretrained)weightsT)rprogress)rrrr%cKst|}|dd}|dk r`d|kr2t|ddt|ddt|ddt|dt|jdtf|}|dk r||j|d |sd|_ d|_ d|_ n t d |S) aGoogLeNet (Inception v1) model architecture from `Going Deeper with Convolutions `_. Args: weights (:class:`~torchvision.models.GoogLeNet_Weights`, optional): The pretrained weights for the model. See :class:`~torchvision.models.GoogLeNet_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.GoogLeNet`` base class. Please refer to the `source code `_ for more details about this class. .. autoclass:: torchvision.models.GoogLeNet_Weights :members: rFNrTr!r r)rz`auxiliary heads in the pretrained googlenet model are NOT pretrained, so make sure to train them)rverifygetrrJrrZload_state_dictZget_state_dictrr^r_rGrH)rrrZoriginal_aux_logitsmodelrprprqr+s&      ) _ModelURLs).rG collectionsr functoolsrtypingrrrrrrgZtorch.nnrMZtorch.nn.functionalZ functionalrr Ztransforms._presetsr utilsr Z_apirrZ_metar_utilsrr__all__r__annotations__rrrrErFrDrrrrrrZ model_urlsrprprprqs<       /# ".