U ʹj$ @s|ddlmZddlmZmZmZmZddlZddlmZm Z ddl m Z m Z ddl mZmZddlmZd d lmZmZmZd d lmZd d lmZmZd d lmZmZmZmZm Z ddl!m"Z"m#Z#dddgZ$GdddeZ%GdddeZ&Gddde Z'eee(eee)e)ee'dddZ*GdddeZ+eddeddd fd!dd"d#d$eee+efe)e)ee'd%d&dZ,dS)')partial)AnyListOptionalUnionN)nnTensor) DeQuantStub QuantStub)Conv2dNormActivationSqueezeExcitation)ImageClassification)register_modelWeights WeightsEnum)_IMAGENET_CATEGORIES)_ovewrite_named_paramhandle_legacy_interface)_mobilenet_v3_confInvertedResidualInvertedResidualConfigMobileNet_V3_Large_Weights MobileNetV3) _fuse_modules _replace_reluQuantizableMobileNetV3#MobileNet_V3_Large_QuantizedWeightsmobilenet_v3_largecs\eZdZdZeeddfdd ZeedddZdee dd d d Z fd d Z Z S)QuantizableSqueezeExcitationrNargskwargsreturncs(tj|d<tj||tj|_dS)NZscale_activation)rZ Hardsigmoidsuper__init__ quantizedFloatFunctionalskip_mulselfr#r$ __class__j/var/www/html/bwave/python/venv/lib/python3.8/site-packages/torchvision/models/quantization/mobilenetv3.pyr'!s z%QuantizableSqueezeExcitation.__init__)inputr%cCs|j|||SN)r*mulZ_scale)r,r1r/r/r0forward&sz$QuantizableSqueezeExcitation.forwardis_qatr%cCst|ddg|dddS)NZfc1Z activationTZinplace)r)r,r6r/r/r0 fuse_model)sz'QuantizableSqueezeExcitation.fuse_modelc s|dd}t|dr|dks&|dkrtdgtdgtjdgtjdtjdgtjdtdgtdgd} | D] \} } || } | |kr|| || <q|t|||||||dS) Nversionqconfigrg?r)Zdtyper)z.scale_activation.activation_post_process.scalezFscale_activation.activation_post_process.activation_post_process.scalez3scale_activation.activation_post_process.zero_pointzKscale_activation.activation_post_process.activation_post_process.zero_pointz;scale_activation.activation_post_process.fake_quant_enabledz9scale_activation.activation_post_process.observer_enabled)gethasattrtorchZtensorZint32itemsr&_load_from_state_dict) r,Z state_dictprefixZlocal_metadatastrictZ missing_keysZunexpected_keysZ error_msgsr9Zdefault_state_dictkvZfull_keyr-r/r0r?,s2       z2QuantizableSqueezeExcitation._load_from_state_dict)N) __name__ __module__ __qualname___versionrr'rr4rboolr8r? __classcell__r/r/r-r0r!s r!cs6eZdZeeddfdd ZeedddZZS)QuantizableInvertedResidualNr"cs&tj|dti|tj|_dS)NZse_layer)r&r'r!rr(r)skip_addr+r-r/r0r'Usz$QuantizableInvertedResidual.__init__xr%cCs(|jr|j|||S||SdSr2)Zuse_res_connectrKaddblockr,rMr/r/r0r4Ysz#QuantizableInvertedResidual.forward)rDrErFrr'rr4rIr/r/r-r0rJSsrJcsLeZdZeeddfdd ZeedddZd eeddd d Z Z S) rNr"cs"tj||t|_t|_dS)zq MobileNet V3 main class Args: Inherits args from floating point MobileNetV3 N)r&r'r quantr dequantr+r-r/r0r'aszQuantizableMobileNetV3.__init__rLcCs"||}||}||}|Sr2)rQZ _forward_implrRrPr/r/r0r4ls   zQuantizableMobileNetV3.forwardr5cCsv|D]h}t|tkrZddg}t|dkrHt|dtjkrH|dt|||ddqt|tkr| |qdS)N01r r2Tr7) modulestyper lenrZReLUappendrr!r8)r,r6mZmodules_to_fuser/r/r0r8rs    z!QuantizableMobileNetV3.fuse_model)N) rDrErFrr'rr4rrHr8rIr/r/r-r0r`s )inverted_residual_setting last_channelweightsprogressquantizer$r%cKs|dk r:t|dt|jdd|jkr:t|d|jd|dd}t||fdti|}t||r|jddtj j ||_ tj j j |dd|dk r||j|dd |rtj j j|dd||S) NZ num_classes categoriesbackendqnnpackrOT)r6r7)r^Z check_hash)rrXmetapoprrJrr8r=ZaoZ quantizationZget_default_qat_qconfigr:Z prepare_qatZload_state_dictZget_state_dictconverteval)r[r\r]r^r_r$ramodelr/r/r0_mobilenet_v3_model}s"   rhc@sHeZdZedeeddddeddejdd d d id d dd dZ e Z dS)rzUhttps://download.pytorch.org/models/quantized/mobilenet_v3_large_qnnpack-5bcacf28.pth)Z crop_sizeiS)rrrbzUhttps://github.com/pytorch/vision/tree/main/references/classification#qat-mobilenetv3z ImageNet-1KgK7A@R@gxV@)zacc@1zacc@5g-?gҍ5@z These weights were produced by doing Quantization Aware Training (eager mode) on top of the unquantized weights listed below. ) Z num_paramsZmin_sizer`raZrecipeZ unquantizedZ_metricsZ_ops _file_sizeZ_docs)urlZ transformsrcN) rDrErFrrrrr IMAGENET1K_V1IMAGENET1K_QNNPACK_V1DEFAULTr/r/r/r0rs( Zquantized_mobilenet_v3_large)nameZ pretrainedcCs|ddrtjStjS)Nr_F)r;rrmrrl)r$r/r/r0s rp)r]TF)r]r^r_)r]r^r_r$r%cKs4|rtnt|}td|\}}t|||||f|S)a MobileNetV3 (Large) model from `Searching for MobileNetV3 `_. .. 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_V3_Large_QuantizedWeights` or :class:`~torchvision.models.MobileNet_V3_Large_Weights`, optional): The pretrained weights for the model. See :class:`~torchvision.models.quantization.MobileNet_V3_Large_QuantizedWeights` below for more details, and possible values. By default, no pre-trained weights are used. progress (bool): If True, displays a progress bar of the download to stderr. Default is True. quantize (bool): If True, return a quantized version of the model. Default is False. **kwargs: parameters passed to the ``torchvision.models.quantization.MobileNet_V3_Large_QuantizedWeights`` base class. Please refer to the `source code `_ for more details about this class. .. autoclass:: torchvision.models.quantization.MobileNet_V3_Large_QuantizedWeights :members: .. autoclass:: torchvision.models.MobileNet_V3_Large_Weights :members: :noindex: r )r )rrverifyrrh)r]r^r_r$r[r\r/r/r0r s-)- functoolsrtypingrrrrr=rrZtorch.ao.quantizationr r Zops.miscr r Ztransforms._presetsrZ_apirrr_metarZ_utilsrrZ mobilenetv3rrrrrutilsrr__all__r!rJrintrHrhrr r/r/r/r0sV   5  $