Ei SSKrSSKJr SSKJrJrJr SSKrSSKJ r SSKJ r SSKJ r SSK Jr SS KJrJrJr SS KJr SS KJrJr SS KJrJrJrJrJrJr S SKJ r J!r!J"r" /SQr#"SS\5r$"SS\5r%"SS\5r&"SS\5r'"SS\5r(\"SS9\"SS4S9SSS S!.S"\\\(\4S#\)S$\)S%\S&\'4 S'jj55rg)(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 googlenetcf^\rSrSrS\S\SS4U4SjjrS\S\4SjrS S \\ SS4S jjr S r U=r $) QuantizableBasicConv2dargskwargsreturnNcZ>[TU]"U0UD6 [R"5UlgN)super__init__nnReLUreluselfr"r# __class__s f/home/mande/repo/quber/.venv/lib/python3.13/site-packages/torchvision/models/quantization/googlenet.pyr(QuantizableBasicConv2d.__init__s" $)&)GGI xclURU5nURU5nURU5nU$r&convbnr+r-r2s r/forwardQuantizableBasicConv2d.forwards. IIaL GGAJ IIaLr1is_qatc [U/SQUSS9 g)Nr4T)inplace)r)r-r:s r/ fuse_model!QuantizableBasicConv2d.fuse_model$sd2FDIr1r+r&) __name__ __module__ __qualname____firstlineno__rr(rr8rboolr=__static_attributes__ __classcell__r.s@r/r r sPcSTF J$J4JJr1r cJ^\rSrSrS\S\SS4U4SjjrS\S\4SjrS rU=r $) QuantizableInception(r"r#r$Ncx>[TU]"US[0UD6 [RR 5UlgN conv_block)r'r(r r) quantizedFloatFunctionalcatr,s r/r(QuantizableInception.__init__)s/ $L+ALVL<<//1r1r2c\URU5nURRUS5$)Nr)_forwardrP)r-r2outputss r/r8QuantizableInception.forward-s%--"xx||GQ''r1)rP r@rArBrCrr(rr8rErFrGs@r/rIrI(s52c2S2T2((F((r1rIcJ^\rSrSrS\S\SS4U4SjjrS\S\4SjrS rU=r $) QuantizableInceptionAux2r"r#r$Ncf>[TU]"US[0UD6 [R"5UlgrL)r'r(r r)r*r+r,s r/r( QuantizableInceptionAux.__init__4s( $L+ALVLGGI r1r2c[R"US5nURU5n[R"US5nUR UR U55nURU5nURU5nU$)N)r]r) Fadaptive_avg_pool2dr5torchflattenr+fc1dropoutfc2r7s r/r8QuantizableInceptionAux.forward8sh ! !!V , IIaL MM!Q  IIdhhqk " LLO HHQKr1r?rVrGs@r/rXrX2s5cSTFr1rXcf^\rSrSrS\S\SS4U4SjjrS\S\4SjrS S \ \ SS4S jjr S r U=r $) rJr"r#r$Nc>[TU]"US[[[/0UD6 [ R RR5Ul [ R RR5Ul g)Nblocks) r'r(r rIrXr`ao quantization QuantStubquant DeQuantStubdequantr,s r/r(QuantizableGoogLeNet.__init__Lsb   13GI`a ek XX**446 xx,,88: r1r2cURU5nURU5nURU5upnURU5nUR=(a UR n[ RR5(a)U(d[R"S5 [XU5$URXU5$)NzCScripted QuantizableGoogleNet always returns GoogleNetOutputs Tuple) _transform_inputrmrSrotraining aux_logitsr`jit is_scriptingwarningswarnr eager_outputs)r-r2aux1aux2 aux_defineds r/r8QuantizableGoogLeNet.forwardSs  ! !! $ JJqM a(  LLOmm7 99 ! ! # # cd#AT2 2%%at4 4r1r:c|UR5H(n[U5[LdMURU5 M* g)zFuse 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-r:ms r/r=QuantizableGoogLeNet.fuse_model`s-AAw00 V$ r1)rormr&)r@rArBrCrr(rrr8rrDr=rErFrGs@r/rrJsM;c;S;T; 5 5$4 5 %$ %4 % %r1rcf\rSrSr\"S\"\SS9SS\SS\RS S S S .0S SSS. S9r \ r Sr g)rmzKhttps://download.pytorch.org/models/quantized/googlenet_fbgemm-c81f6644.pth) crop_sizeie)rfbgemmzdhttps://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@rArBrCr rr rr IMAGENET1K_V1IMAGENET1K_FBGEMM_V1DEFAULTrErr1r/rrms_" Y.#>! .|,::##    0#Gr1rquantized_googlenet)name pretrainedcpURSS5(a[R$[R$)NquantizeF)getrrrr)r#s r/rs1zz*e,, ' ; ; 1"00 1r1)weightsTF)rprogressrrrrr#r$c U(a[O[RU5nURSS5nUbzSU;a [ USS5 [ USS5 [ USS5 [ US[ UR S55 S UR ;a[ US UR S 5 URS S 5n[S 0UD6n[U5 U(a [Xe5 UbSURURUSS 95 U(dSUl SUlSUlU$[ R""S 5 U$)a\GoogLeNet (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: rtFNtransform_inputT init_weights num_classesrrr)r check_hashz`auxiliary heads in the pretrained googlenet model are NOT pretrained, so make sure to train themr)rrverifyrrlenrpoprrrload_state_dictget_state_dictrtrzr{rwrx)rrrr#original_aux_logitsrmodels r/rrs(\.6);LTTU\]G **\59 F * !&*;T BflD9fne<fmSl9S5TU  $ !&)W\\)5L MjjH-G *6 *E%u& g44hSW4XY"$E EJEJ L MMr  Lr1)*rw functoolsrtypingrrrr`torch.nnr)rrr^transforms._presetsr _apir r r_metar_utilsrrrrrrrrrutilsrrr__all__r rIrXrrrDrr1r/rs'' $677(Cll??  J[ J(9(l0 %9 %F##8*+   OS @ e68IIJ K@@ @  @  @ ,@r1