ڧg 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'ZdS)(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 googlenetcZeZdZdededdffd ZdedefdZd deeddfd Z xZ S) QuantizableBasicConv2dargskwargsreturnNcjtj|i|tj|_dSN)super__init__nnReLUreluselfr!r" __class__s e/var/www/html/ai-engine/env/lib/python3.11/site-packages/torchvision/models/quantization/googlenet.pyr'zQuantizableBasicConv2d.__init__s0$)&)))GII xc||}||}||}|Sr%convbnr*r,r0s r.forwardzQuantizableBasicConv2d.forwards4 IIaLL GGAJJ IIaLLr/is_qatc0t|gd|ddS)Nr2T)inplace)r)r,r7s r. fuse_modelz!QuantizableBasicConv2d.fuse_model$s&d222FDIIIIIIr/r%) __name__ __module__ __qualname__rr'rr6rboolr: __classcell__r-s@r.r r scSTF JJ$J4JJJJJJJJr/r c<eZdZdededdffd ZdedefdZxZS)QuantizableInceptionr!r"r#Nctj|dti|tj|_dSN conv_block)r&r'r r( quantizedFloatFunctionalcatr+s r.r'zQuantizableInception.__init__)s>$L+ALVLLL<//11r/r0cb||}|j|dS)Nr)_forwardrH)r,r0outputss r.r6zQuantizableInception.forward-s)--""x||GQ'''r/r;r<r=rr'rr6r?r@s@r.rBrB(st2c2S2T222222((F((((((((r/rBc<eZdZdededdffd ZdedefdZxZS)QuantizableInceptionAuxr!r"r#Ncxtj|dti|tj|_dSrD)r&r'r r(r)r*r+s r.r'z QuantizableInceptionAux.__init__4s6$L+ALVLLLGII r/r0c(tj|d}||}tj|d}|||}||}||}|S)N)rQr) Fadaptive_avg_pool2dr3torchflattenr*fc1dropoutfc2r5s r.r6zQuantizableInceptionAux.forward8st !!V , , IIaLL M!Q   IIdhhqkk " " LLOO HHQKKr/rLr@s@r.rNrN2stcSTFr/rNcZeZdZdededdffd ZdedefdZd dee ddfd Z xZ S) rr!r"r#Nctj|dtttgi|t jj|_ t jj |_ dS)Nblocks) r&r'r rBrNrTao quantization QuantStubquant DeQuantStubdequantr+s r.r'zQuantizableGoogLeNet.__init__Lsp  13GI`a ek   X*4466 x,88:: r/r0c||}||}||\}}}||}|jo|j}t jr'|stj dt|||S| |||S)NzCScripted QuantizableGoogleNet always returns GoogleNetOutputs Tuple) _transform_inputr_rJratraining aux_logitsrTjit is_scriptingwarningswarnr eager_outputs)r,r0aux1aux2 aux_defineds r.r6zQuantizableGoogLeNet.forwardSs  ! !! $ $ JJqMM a(( 4 LLOOm7 9 ! ! # # 5 e cddd#AtT22 2%%at44 4r/r7c|D]-}t|tur||.dS)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`sJ % %AAww000 V$$$ % %r/r%) r;r<r=rr'rrr6rr>r:r?r@s@r.rrJs;c;S;T;;;;;; 5 5$4 5 5 5 5 % %$ %4 % % % % % % % %r/rcneZdZedeedddeddejdd d d id d dd Z e Z dS)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"7 Y7.#>>>! .|,:##      0#GGGr/rquantized_googlenet)name pretrainedc^|ddr tjn tjS)NquantizeF)getrrrr)r"s r.rs, ::j% ( (-1FF  ,r/)weightsTF)rprogressrrrrr"r#c |rtnt|}|dd}|d|vrt |ddt |ddt |ddt |dt |jdd |jvrt |d |jd |d d }td i|}t||rt|||V| | |d |sd|_ d|_d|_nt!jd |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.rrsX.6L));LTTU\]]G **\599 F * * !&*;T B B BflD999fne<<<fmSl9S5T5TUUU  $ $ !&)W\)5L M M MjjH--G * *6 * *E%'ug&&& g44hSW4XXYYY" $E EJEJJ Mr    Lr/)*rh functoolsrtypingrrrrTtorch.nnr(rrrRtransforms._presetsr _apir r r_metar_utilsrrrrrrrrrutilsrrr__all__r rBrNrrr>rr/r.rs'''''''''' $$$$$$6666667777777777((((((CCCCCCCCllllllllllllllll??????????    J J J J J[ J J J(((((9(((l0 % % % % %9 % % %F########8*+++ - - OS @@@ e68IIJ K@@ @  @  @@@,+@@@r/