U (d@sXddlmZddlmZmZmZddlmZddlmZddl m Z m Z ddl m Z mZmZddlmZdd lmZd d lmZmZd d lmZd d lmZmZddlmZmZmZdddgZ Gddde Z!GdddeZ"GdddeZ#edddfdddddeee#efe$e$ee"dd dZ%d d!lm&Z&d d"l'm(Z(e&d#e#j)j*iZ+dS)$)partial)AnyOptionalUnion)Tensor)nn) QuantStub DeQuantStub)InvertedResidual MobileNetV2MobileNet_V2_Weights)Conv2dNormActivation)ImageClassification) WeightsEnumWeights)_IMAGENET_CATEGORIES)handle_legacy_interface_ovewrite_named_param) _fuse_modules _replace_reluquantize_modelQuantizableMobileNetV2MobileNet_V2_QuantizedWeights mobilenet_v2csLeZdZeeddfdd ZeedddZd eeddd d Z Z S) QuantizableInvertedResidualNargskwargsreturncstj||tj|_dSN)super__init__rZ quantizedZFloatFunctionalskip_addselfrr  __class__O/tmp/pip-unpacked-wheel-vx7f76es/torchvision/models/quantization/mobilenetv2.pyr$sz$QuantizableInvertedResidual.__init__xr!cCs(|jr|j|||S||SdSr")Zuse_res_connectr%addconvr'r-r*r*r+forwardsz#QuantizableInvertedResidual.forwardis_qatr!cCsNtt|jD]:}t|j|tjkrt|jt|t|dg|ddqdS)NrTZinplace)rangelenr/typerZConv2drstr)r'r3idxr*r*r+ fuse_model#sz&QuantizableInvertedResidual.fuse_model)N __name__ __module__ __qualname__rr$rr1rboolr: __classcell__r*r*r(r+rsrcsLeZdZeeddfdd ZeedddZd eeddd d Z Z S) rNrcs"tj||t|_t|_dS)zq MobileNet V2 main class Args: Inherits args from floating point MobileNetV2 N)r#r$rquantr dequantr&r(r*r+r$*szQuantizableMobileNetV2.__init__r,cCs"||}||}||}|Sr")rAZ _forward_implrBr0r*r*r+r15s   zQuantizableMobileNetV2.forwardr2cCsJ|D]<}t|tkr.t|dddg|ddt|tkr||qdS)N012Tr4)modulesr7rrrr:)r'r3mr*r*r+r:;s    z!QuantizableMobileNetV2.fuse_model)Nr;r*r*r(r+r)s c @sDeZdZedeeddddeddejdd d d id d dZ e Z dS)rzOhttps://download.pytorch.org/models/quantized/mobilenet_v2_qnnpack_37f702c5.pth)Z crop_sizeiz5)rrqnnpackzUhttps://github.com/pytorch/vision/tree/main/references/classification#qat-mobilenetv2z ImageNet-1Kg'1Q@gV@)zacc@1zacc@5z These weights were produced by doing Quantization Aware Training (eager mode) on top of the unquantized weights listed below. )Z num_paramsZmin_size categoriesbackendZrecipeZ unquantizedZ_metricsZ_docs)urlZ transformsmetaN) r<r=r>rrrrr IMAGENET1K_V1IMAGENET1K_QNNPACK_V1DEFAULTr*r*r*r+rCs$ Z pretrainedcCs|ddrtjStjS)NquantizeF)getrrOr rN)r r*r*r+`s rS)weightsNTF)rTprogressrQ)rTrUrQr r!cKs|rtnt|}|dk rLt|dt|jdd|jkrLt|d|jd|dd}tfdti|}t ||rt |||dk r| |j |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: NZ num_classesrJrKrIblock)rU) rr verifyrr6rMpoprrrrZload_state_dictZget_state_dict)rTrUrQr rKmodelr*r*r+r]s+   ) _ModelURLs) model_urlsZmobilenet_v2_qnnpack), functoolsrtypingrrrZtorchrrZtorch.ao.quantizationrr Ztorchvision.models.mobilenetv2r r r Zops.miscrZtransforms._presetsrZ_apirrZ_metar_utilsrrutilsrrr__all__rrrr?rrZZ mobilenetv2r[rOrLZquant_model_urlsr*r*r*r+sP       7