kh ddlZddlmZddlmZmZmZddlZddlm Z ddlm Z ddlm Z ddl mZdd lmZmZmZdd lmZdd lmZmZdd lmZmZmZmZmZmZd dlm Z m!Z!m"Z"gdZ#GddeZ$GddeZ%GddeZ&GddeZ'GddeZ(ededdfddd d!d"eee(efd#e)d$e)d%ed&e'f d'Zy)(N)partial)AnyOptionalUnion)Tensor) functional)ImageClassification)register_modelWeights WeightsEnum)_IMAGENET_CATEGORIES)_ovewrite_named_paramhandle_legacy_interface) BasicConv2d GoogLeNetGoogLeNet_WeightsGoogLeNetOutputs Inception InceptionAux) _fuse_modules _replace_reluquantize_model)QuantizableGoogLeNetGoogLeNet_QuantizedWeights googlenetcTeZdZdededdffd ZdedefdZd deeddfd Z xZ S) QuantizableBasicConv2dargskwargsreturnNcVt||i|tj|_yN)super__init__nnReLUreluselfr!r" __class__s e/var/www/teggl/fontify/venv/lib/python3.12/site-packages/torchvision/models/quantization/googlenet.pyr'zQuantizableBasicConv2d.__init__s" $)&)GGI xcl|j|}|j|}|j|}|Sr%convbnr*r,r0s r.forwardzQuantizableBasicConv2d.forwards. IIaL GGAJ IIaLr/is_qatc&t|gd|dy)Nr2T)inplace)r)r,r7s r. fuse_modelz!QuantizableBasicConv2d.fuse_model$sd2FDIr/r%) __name__ __module__ __qualname__rr'rr6rboolr: __classcell__r-s@r.r r sJcSTF J$J4Jr/r c<eZdZdededdffd ZdedefdZxZS)QuantizableInceptionr!r"r#Ncvt||dti|tjj |_yN conv_block)r&r'r r( quantizedFloatFunctionalcatr+s r.r'zQuantizableInception.__init__)s/ $L+ALVL<<//1r/r0c\|j|}|jj|dS)Nr)_forwardrH)r,r0outputss r.r6zQuantizableInception.forward-s%--"xx||GQ''r/r;r<r=rr'rr6r?r@s@r.rBrB(s02c2S2T2((F(r/rBc<eZdZdededdffd ZdedefdZxZS)QuantizableInceptionAuxr!r"r#Ncbt||dti|tj|_yrD)r&r'r r(r)r*r+s r.r'z QuantizableInceptionAux.__init__4s( $L+ALVLGGI r/r0ctj|d}|j|}tj|d}|j |j |}|j|}|j|}|S)N)rQr) Fadaptive_avg_pool2dr3torchflattenr*fc1dropoutfc2r5s r.r6zQuantizableInceptionAux.forward8sh ! !!V , IIaL MM!Q  IIdhhqk " LLO HHQKr/rLr@s@r.rNrN2s0cSTFr/rNcTeZdZdededdffd ZdedefdZd dee ddfd Z xZ S) rr!r"r#Nct||dtttgi|t j jj|_ t j jj|_ y)Nblocks) r&r'r rBrNrTao quantization QuantStubquant DeQuantStubdequantr+s r.r'zQuantizableGoogLeNet.__init__Lsb   13GI`a ek XX**446 xx,,88: r/r0cp|j|}|j|}|j|\}}}|j|}|jxr |j }t jjr$|stjdt|||S|j|||S)NzCScripted QuantizableGoogleNet always returns GoogleNetOutputs Tuple) _transform_inputr_rJratraining aux_logitsrTjit is_scriptingwarningswarnr eager_outputs)r,r0aux1aux2 aux_defineds r.r6zQuantizableGoogLeNet.forwardSs  ! !! $ JJqM a( 4 LLOmm7 99 ! ! # cd#AtT2 2%%at4 4r/r7ct|jD]%}t|tus|j|'y)aFuse conv/bn/relu modules in googlenet model Fuse conv+bn+relu/ conv+relu/conv+bn modules to prepare for quantization. Model is modified in place. Note that this operation does not change numerics and the model after modification is in floating point N)modulestyper r:)r,r7ms r.r:zQuantizableGoogLeNet.fuse_model`s2 %AAw00 V$ %r/r%) r;r<r=rr'rrr6rr>r:r?r@s@r.rrJsH;c;S;T; 5 5$4 5 %$ %4 %r/rcjeZdZedeedddeddejdd d d id d dd Z e Z y)rzKhttps://download.pytorch.org/models/quantized/googlenet_fbgemm-c81f6644.pth) crop_sizeie)rufbgemmzdhttps://github.com/pytorch/vision/tree/main/references/classification#post-training-quantized-modelsz ImageNet-1Kg/tQ@g`"YV@)zacc@1zacc@5g+?g#~j<)@z These weights were produced by doing Post Training Quantization (eager mode) on top of the unquantized weights listed below. ) num_paramsmin_size categoriesbackendrecipe unquantized_metrics_ops _file_size_docs)url transformsmetaN) r;r<r=r rr rr IMAGENET1K_V1IMAGENET1K_FBGEMM_V1DEFAULTr/r.rrms_" Y.#>! .|,::##    0#Gr/rquantized_googlenet)name pretrainedcf|jddrtjStjS)NquantizeF)getrrrr)r"s r.rs+ ::j% (2FF  , ,r/)weightsTF)rprogressrrrrr"r#c d|rtntj|}|jdd}|vd|vr t |ddt |ddt |ddt |dt |j dd |j vrt |d |j d |jd d }td i|}t||r t|||P|j|j|d |sd|_ d|_d|_|St!j"d |S)aGoogLeNet (Inception v1) model architecture from `Going Deeper with Convolutions `__. .. note:: Note that ``quantize = True`` returns a quantized model with 8 bit weights. Quantized models only support inference and run on CPUs. GPU inference is not yet supported. Args: weights (:class:`~torchvision.models.quantization.GoogLeNet_QuantizedWeights` or :class:`~torchvision.models.GoogLeNet_Weights`, optional): The pretrained weights for the model. See :class:`~torchvision.models.quantization.GoogLeNet_QuantizedWeights` 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. quantize (bool, optional): If True, return a quantized version of the model. Default is False. **kwargs: parameters passed to the ``torchvision.models.quantization.QuantizableGoogLeNet`` base class. Please refer to the `source code `_ for more details about this class. .. autoclass:: torchvision.models.quantization.GoogLeNet_QuantizedWeights :members: .. autoclass:: torchvision.models.GoogLeNet_Weights :members: :noindex: reFNtransform_inputT init_weights num_classesryrzrv)r check_hashz`auxiliary heads in the pretrained googlenet model are NOT pretrained, so make sure to train themr)rrverifyrrlenrpoprrrload_state_dictget_state_dictrerkrlrhri)rrrr"original_aux_logitsrzmodels r.rrs*X.6);LTTU\]G **\59 F * !&*;T BflD9fne<fmSl9S5TU  $ !&)W\\)5L MjjH-G *6 *E%ug& g44hSW4XY"$E EJEJ L MMr  Lr/)*rh functoolsrtypingrrrrTtorch.nnr(rrrRtransforms._presetsr _apir r r_metar_utilsrrrrrrrrrutilsrrr__all__r rBrNrrr>rr/r.rs'' $677(Cll??  J[ J(9(l0 %9 %F##8*+ - OS @ e68IIJ K@@ @  @  @,@r/