j ddlmZddlmZmZmZddlmZmZddl m Z m Z ddl m Z mZmZddlmZddlmZd d lmZmZmZd d lmZd d lmZmZd dlmZmZm Z gdZ!Gdde Z"GddeZ#GddeZ$ededdfdddddeee$efd e%d!e%d"ed#e#f d$Z&dS)%)partial)AnyOptionalUnion)nnTensor) DeQuantStub QuantStub)InvertedResidualMobileNet_V2_Weights MobileNetV2)Conv2dNormActivation)ImageClassification)register_modelWeights WeightsEnum)_IMAGENET_CATEGORIES)_ovewrite_named_paramhandle_legacy_interface) _fuse_modules _replace_reluquantize_model)QuantizableMobileNetV2MobileNet_V2_QuantizedWeights mobilenet_v2cZeZdZdededdffd ZdedefdZd deeddfd Z xZ S) QuantizableInvertedResidualargskwargsreturnNctj|i|tj|_dSN)super__init__r quantizedFloatFunctionalskip_addselfr!r" __class__s \C:\Projects\Historical\venv\Lib\site-packages\torchvision/models/quantization/mobilenetv2.pyr'z$QuantizableInvertedResidual.__init__s8$)&))) 4466 xc|jr.|j|||S||Sr%)use_res_connectr*addconvr,r0s r.forwardz#QuantizableInvertedResidual.forwards?   =$$Q ! 55 599Q<< r/is_qatc tt|jD]_}t|j|tjur7t |jt|t|dzg|d`dS)NrTinplace)rangelenr4typerConv2drstr)r,r7idxs r. fuse_modelz&QuantizableInvertedResidual.fuse_model"szTY(( Y YCDIcN##ry00di#c((CaLL)A6SWXXXX Y Yr/r% __name__ __module__ __qualname__rr'rr6rboolrA __classcell__r-s@r.r r s7c7S7T777777  F    YY$Y4YYYYYYYYr/r cZeZdZdededdffd ZdedefdZd deeddfd Z xZ S) rr!r"r#Nctj|i|t|_t |_dS)zq MobileNet V2 main class Args: Inherits args from floating point MobileNetV2 N)r&r'r quantr dequantr+s r.r'zQuantizableMobileNetV2.__init__)s; $)&)))[[ "}} r/r0c||}||}||}|Sr%)rK _forward_implrLr5s r.r6zQuantizableMobileNetV2.forward4s8 JJqMM   q ! ! LLOOr/r7c|D]X}t|turt|gd|dt|tur||YdS)N)012Tr9)modulesr=rrr rA)r,r7ms r.rAz!QuantizableMobileNetV2.fuse_model:su % %AAww...a&$GGGGAww555 V$$$  % %r/r%rBrHs@r.rr(s %c %S %T % % % % % %F %%$%4%%%%%%%%r/rcneZdZedeedddeddejdd d d id d dd Z e Z dS)rzOhttps://download.pytorch.org/models/quantized/mobilenet_v2_qnnpack_37f702c5.pth) crop_sizeiz5)rrqnnpackzUhttps://github.com/pytorch/vision/tree/main/references/classification#qat-mobilenetv2z ImageNet-1Kg'1Q@gV@)zacc@1zacc@5g$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) rCrDrErrrrr IMAGENET1K_V1IMAGENET1K_QNNPACK_V1DEFAULTr/r.rrBs#G ]7.#>>>!. m/=##     0$GGGr/rquantized_mobilenet_v2)name pretrainedc^|ddr tjn tjS)NquantizeF)getrrgr rf)r"s r.rpbs, ::j% ( (04JJ ! /r/)weightsNTF)rqprogressrnrqrrrnr"r#c |rtnt|}|Nt|dt |jdd|jvrt|d|jd|dd}td dti|}t||rt|||*| | |d|S) a 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: N num_classesr[r\rXblockT)rr check_hashri) rr verifyrr<repoprr rrload_state_dictget_state_dict)rqrrrnr"r\models r.rr^sX19R,,>RZZ[bccGfmSl9S5T5TUUU  $ $ !&)W\)5L M M MjjI..G " O O)D O O OE%'ug&&& g44hSW4XXYYY Lr/)' functoolsrtypingrrrtorchrrtorch.ao.quantizationr r torchvision.models.mobilenetv2r r r ops.miscrtransforms._presetsr_apirrr_metar_utilsrrutilsrrr__all__r rrrFrrir/r.rsg''''''''''88888888^^^^^^^^^^,,,,,,6666667777777777((((((CCCCCCCC??????????   YYYYY"2YYY"%%%%%[%%%4$$$$$K$$$8-... 0 0 UY 333 e9;OOP Q33 3  3  333/.333r/