Ei bSSKJr SSKJrJrJr SSKJrJr SSK J r J r SSK J r JrJr SSKJr SSKJr S S KJrJrJr S S KJr S S KJrJr S SKJrJrJ r /SQr!"SS\ 5r""SS\5r#"SS\5r$\"SS9\"SS4S9SSSS.S\\\$\4S \%S!\%S"\S#\#4 S$jj55r&g)%)partial)AnyOptionalUnion)nnTensor) DeQuantStub QuantStub)InvertedResidualMobileNet_V2_Weights MobileNetV2)Conv2dNormActivation)ImageClassification)register_modelWeights WeightsEnum)_IMAGENET_CATEGORIES)_ovewrite_named_paramhandle_legacy_interface) _fuse_modules _replace_reluquantize_model)QuantizableMobileNetV2MobileNet_V2_QuantizedWeights mobilenet_v2cf^\rSrSrS\S\SS4U4SjjrS\S\4SjrS S \\ SS4S jjr S r U=r $) QuantizableInvertedResidualargskwargsreturnNcl>[TU]"U0UD6 [RR 5UlgN)super__init__r quantizedFloatFunctionalskip_addselfr"r# __class__s h/home/mande/repo/quber/.venv/lib/python3.13/site-packages/torchvision/models/quantization/mobilenetv2.pyr($QuantizableInvertedResidual.__init__s) $)&) 446 xcUR(a*URRXRU55$URU5$r&)use_res_connectr+addconvr-r2s r/forward#QuantizableInvertedResidual.forwards6   ==$$Q ! 5 599Q< r1is_qatc [[UR55H[n[URU5[R LdM.[ UR[U5[US-5/USS9 M] g)NrTinplace)rangelenr6typerConv2drstr)r-r:idxs r/ fuse_model&QuantizableInvertedResidual.fuse_model"sVTYY(CDIIcN#ryy0dii#c(CaL)A6SWX)r1)r+r& __name__ __module__ __qualname____firstlineno__rr(rr8rboolrD__static_attributes__ __classcell__r.s@r/r r sP7c7S7T7  F Y$Y4YYr1r cf^\rSrSrS\S\SS4U4SjjrS\S\4SjrS S \\ SS4S jjr S r U=r $) r(r"r#r$Ncb>[TU]"U0UD6 [5Ul[ 5Ulg)zQ MobileNet V2 main class Args: Inherits args from floating point MobileNetV2 N)r'r(r quantr dequantr,s r/r(QuantizableMobileNetV2.__init__)s) $)&)[ "} r1r2clURU5nURU5nURU5nU$r&)rR _forward_implrSr7s r/r8QuantizableMobileNetV2.forward4s1 JJqM   q ! LLOr1r:cUR5HHn[U5[La[U/SQUSS9 [U5[LdM7UR U5 MJ g)N)012Tr<)modulesr@rrr rD)r-r:ms r/rD!QuantizableMobileNetV2.fuse_model:sFAAw..a&$GAw55 V$  r1)rSrRr&rFrNs@r/rr(sL %c %S %T %F %$%4%%r1rcf\rSrSr\"S\"\SS9SS\SS\RS S S S .0S SSS. S9r \ r Sr g)rBzOhttps://download.pytorch.org/models/quantized/mobilenet_v2_qnnpack_37f702c5.pth) crop_sizeiz5)rrqnnpackzUhttps://github.com/pytorch/vision/tree/main/references/classification#qat-mobilenetv2z ImageNet-1Kg'1Q@gV@)zacc@1zacc@5g$C?gMb @z These weights were produced by doing Quantization Aware Training (eager mode) on top of the unquantized weights listed below. ) num_paramsmin_size categoriesbackendrecipe unquantized_metrics_ops _file_size_docs)url transformsmetaN) rGrHrIrJrrrrr IMAGENET1K_V1IMAGENET1K_QNNPACK_V1DEFAULTrLrqr1r/rrBs_# ].#>!. m/==##   0$Gr1rquantized_mobilenet_v2)name pretrainedcpURSS5(a[R$[R$)NquantizeF)getrrsr rr)r#s r/r{bs1zz*e,, * ? ? 4%33 4r1)weightsNTF)r|progressryr|r}ryr#r$c U(a[O[RU5nUbM[US[ UR S55 SUR ;a[USUR S5 UR SS5n[SS[0UD6n[U5 U(a [XT5 UbURURUSS95 U$) az Constructs a MobileNetV2 architecture from `MobileNetV2: Inverted Residuals and Linear Bottlenecks `_. .. 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.MobileNet_V2_QuantizedWeights` or :class:`~torchvision.models.MobileNet_V2_Weights`, optional): The pretrained weights for the model. See :class:`~torchvision.models.quantization.MobileNet_V2_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, returns a quantized version of the model. Default is False. **kwargs: parameters passed to the ``torchvision.models.quantization.QuantizableMobileNetV2`` base class. Please refer to the `source code `_ for more details about this class. .. autoclass:: torchvision.models.quantization.MobileNet_V2_QuantizedWeights :members: .. autoclass:: torchvision.models.MobileNet_V2_Weights :members: :noindex: num_classesrfrgrcblockT)r} check_hashrq) rr verifyrr?rppoprr rrload_state_dictget_state_dict)r|r}ryr#rgmodels r/rr^s\19,>RZZ[bcGfmSl9S5TU  $ !&)W\\)5L MjjI.G " O)D O OE%u& g44hSW4XY Lr1)' functoolsrtypingrrrtorchrrtorch.ao.quantizationr r torchvision.models.mobilenetv2r r r ops.miscrtransforms._presetsr_apirrr_metar_utilsrrutilsrrr__all__r rrrKrrqr1r/rs''8^^,677(C?? Y"2Y"%[%4$K$8-.   UY 3 e9;OOP Q33 3  3  3 /3r1