+ jR^RIHt^RIHtHtHt^RIHtHt^RI H t H t ^RI H t HtHt^RIHt^RIHt^RIHtHtHt^R IHt^R IHtHt^R IHtHtH t .ROt!!RR] 4t"!RR ]4t#!RR ]4t$]!RR7]!RR3R7RRRRRR/RRll44t%R#) )partial)AnyOptionalUnion)nnTensor) DeQuantStub QuantStub)InvertedResidualMobileNet_V2_Weights MobileNetV2)Conv2dNormActivation)ImageClassification)register_modelWeights WeightsEnum)_IMAGENET_CATEGORIES)_ovewrite_named_paramhandle_legacy_interface) _fuse_modules _replace_reluquantize_modelQuantizableMobileNetV2MobileNet_V2_QuantizedWeightscfaa]tRt^toV3RlV3RlltV3RlRltRV3RlRlltRtVtV;t #) QuantizableInvertedResidualc*<V^8dQhRS[RS[RR/#argskwargsreturnNr)format __classdict__s"l/home/luca/screen-checking/.venv/lib/python3.14/site-packages/torchvision/models/quantization/mobilenetv2.py __annotate__(QuantizableInvertedResidual.__annotate__s"77c7S7T7cn<\SV`!V/VB\PP 4VnR#N)super__init__r quantizedFloatFunctionalskip_addselfr r! __class__s&*,r&r-$QuantizableInvertedResidual.__init__s) $)&) 446 r)c&<V^8dQhRS[RS[/#rxr"r)r$r%s"r&r'r(s   F r)cVP'd+VPPWPV44#VPV4#r+)use_res_connectr0addconvr2r7s&&r&forward#QuantizableInvertedResidual.forwards9    ==$$Q ! 5 599Q< r)c4<V^8dQhRS[S[,RR/#ris_qatr"Nrbool)r$r%s"r&r'r("s#YY$Y4Yr)c \\VP44Fgp\VPV,4\P JgK3\ VP\V4\V^,4.VRR7Ki R#)TinplaceN)rangelenr<typerConv2drstr)r2rBidxs&& r& fuse_model&QuantizableInvertedResidual.fuse_model"sVTYY(CDIIcN#ryy0dii#c(CaL)A6SWX)r))r0r+ __name__ __module__ __qualname____firstlineno__r-r>rO__static_attributes____classdictcell__ __classcell__r3r%s@@r&rrs+77  YYYr)rcfaa]tRt^(toV3RlV3RlltV3RlRltRV3RlRlltRtVtV;t #) rc*<V^8dQhRS[RS[RR/#rr#)r$r%s"r&r'#QuantizableMobileNetV2.__annotate__)s" % %c %S %T %r)c d<\SV`!V/VB\4Vn\ 4VnR#)zQ MobileNet V2 main class Args: Inherits args from floating point MobileNetV2 N)r,r-r quantr dequantr1s&*,r&r-QuantizableMobileNetV2.__init__)s) $)&)[ "} r)c&<V^8dQhRS[RS[/#r6r8)r$r%s"r&r'r\4sFr)clVPV4pVPV4pVPV4pV#r+)r^ _forward_implr_r=s&&r&r>QuantizableMobileNetV2.forward4s1 JJqM   q ! LLOr)c4<V^8dQhRS[S[,RR/#rArC)r$r%s"r&r'r\:s%%$%4%r)cVP4FMp\V4\Jd\V.ROVRR7\V4\JgK<VP V4KO R#)0TrGN)rg12)modulesrKrrrrO)r2rBms&& r&rO!QuantizableMobileNetV2.fuse_model:sFAAw..a&$GAw55 V$  r))r_r^r+rQrYs@@r&rr(s( % % %%%r)c]tRt^Bt]!R]!]^R7RRRRR]RRR R R ]PR R RRRR//RRRRRR/ R7t ] t Rt R#)rzOhttps://download.pytorch.org/models/quantized/mobilenet_v2_qnnpack_37f702c5.pth) crop_size num_paramsiz5min_size categoriesbackendqnnpackrecipezUhttps://github.com/pytorch/vision/tree/main/references/classification#qat-mobilenetv2 unquantized_metricsz ImageNet-1Kzacc@1g'1Q@zacc@5gV@_opsg$C? _file_sizegMb @_docsz These weights were produced by doing Quantization Aware Training (eager mode) on top of the unquantized weights listed below. )url transformsmetaN)rFrF) rRrSrTrUrrrrr IMAGENET1K_V1IMAGENET1K_QNNPACK_V1DEFAULTrVr}r)r&rrBs|# ].#> '  . y m /== VV  E %  0$Gr)quantized_mobilenet_v2)name pretrainedcrVPRR4'd\P#\P#)quantizeF)getrrr r~)r!s&r&rbs1zz*e,, * ? ? 4%33 4r))weightsrNprogressTrFc V^8dQhR\\\\3,,R\R\R\ R\ /#)rrrrr!r")rrrr rDrr)r$s"r&r'r'isM33 e9;OOP Q33 3  3  3r)c V'd\M\PV4pVeW\VR\ VP R,44RVP 9d\VRVP R,4VP RR4p\RR\/VBp\V4V'd \WT4Ve#VPVPVRR74V#) 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_classesrqrrrsblockT)r check_hashr}) rr verifyrrJr|poprrrrload_state_dictget_state_dict)rrrr!rrmodels$$$, r& mobilenet_v2r^s\19,>RZZ[bcGfmSl9S5TU  $ !&)W\\)5L MjjI.G " O)D O OE%u& g44hSW4XY Lr))rrr)& functoolsrtypingrrrtorchrrtorch.ao.quantizationr r torchvision.models.mobilenetv2r r r ops.miscrtransforms._presetsr_apirrr_metar_utilsrrutilsrrr__all__rrrrr}r)r&rs''8^^,677(C?? Y"2Y"%[%4$K$8-.   3TX33 3 /3r)