a h($ @svddlmZddlmZmZmZddlZddlmZmZddl m Z m Z ddl m Z mZddlmZd d lmZmZmZd d lmZd d lmZmZd d lmZmZmZmZmZddl m!Z!m"Z"gdZ#GdddeZ$GdddeZ%GdddeZ&e'ee(eee)e)ee&dddZ*GdddeZ+eddeddd fd!dd"d#d$eee+efe)e)ee&d%d&d'Z,dS)()partial)AnyOptionalUnionN)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_relu)QuantizableMobileNetV3#MobileNet_V3_Large_QuantizedWeightsmobilenet_v3_largecs\eZdZdZeeddfdd ZeedddZdee dd d d Z fd d Z Z S)QuantizableSqueezeExcitationrNargskwargsreturncs,tj|d<tj|i|tj|_dS)NZscale_activation)rZ Hardsigmoidsuper__init__ quantizedFloatFunctionalskip_mulselfr"r# __class__Y/var/www/auris/lib/python3.9/site-packages/torchvision/models/quantization/mobilenetv3.pyr&!s z%QuantizableSqueezeExcitation.__init__)inputr$cCs|j|||SN)r)mulZ_scale)r+r0r.r.r/forward&sz$QuantizableSqueezeExcitation.forwardis_qatr$cCst|ddg|dddS)NZfc1Z activationTZinplace)r)r+r5r.r.r/ fuse_model)sz'QuantizableSqueezeExcitation.fuse_modelc s|dd}t|dr|dus&|dkrtdgtdgtjdgtjdtjdgtjdtdgtdgd} | D] \} } || } | |vr|| || <q|t|||||||dS) Nversionqconfigrg?r)Zdtyper)z.scale_activation.activation_post_process.scalezFscale_activation.activation_post_process.activation_post_process.scalez3scale_activation.activation_post_process.zero_pointzKscale_activation.activation_post_process.activation_post_process.zero_pointz;scale_activation.activation_post_process.fake_quant_enabledz9scale_activation.activation_post_process.observer_enabled)gethasattrtorchZtensorint32itemsr%_load_from_state_dict) r+Z state_dictprefixZlocal_metadatastrictZ missing_keysZunexpected_keysZ error_msgsr8Zdefault_state_dictkvZfull_keyr,r.r/r?,s0       z2QuantizableSqueezeExcitation._load_from_state_dict)N) __name__ __module__ __qualname___versionrr&rr3rboolr7r? __classcell__r.r.r,r/r s r cs6eZdZeeddfdd ZeedddZZS)QuantizableInvertedResidualNr!cs&tj|dti|tj|_dS)NZse_layer)r%r&r rr'r(skip_addr*r,r.r/r&Usz$QuantizableInvertedResidual.__init__xr$cCs(|jr|j|||S||SdSr1)Zuse_res_connectrKaddblockr+rMr.r.r/r3Ysz#QuantizableInvertedResidual.forward)rDrErFrr&rr3rIr.r.r,r/rJSsrJcsLeZdZeeddfdd ZeedddZd eeddd d Z Z S) rNr!cs&tj|i|t|_t|_dS)zq MobileNet V3 main class Args: Inherits args from floating point MobileNetV3 N)r%r&r quantrdequantr*r,r.r/r&aszQuantizableMobileNetV3.__init__rLcCs"||}||}||}|Sr1)rQZ _forward_implrRrPr.r.r/r3ls   zQuantizableMobileNetV3.forwardr4cCsv|D]h}t|turZddg}t|dkrHt|dtjurH|dt|||ddqt|tur| |qdS)N01r r2Tr6) modulestyper lenrZReLUappendrr r7)r+r5mZmodules_to_fuser.r.r/r7rs    z!QuantizableMobileNetV3.fuse_model)N) rDrErFrr&rr3rrHr7rIr.r.r,r/r`s r)inverted_residual_setting last_channelweightsprogressquantizer#r$cKs|dur:t|dt|jdd|jvr:t|d|jd|dd}t||fdti|}t||r|jddtj j ||_ tj j j |dd|dur||j|dd |rtj j j|dd||S) NZ num_classes categoriesbackendqnnpackrOT)r5r6)r^Z check_hash)rrXmetapoprrJrr7r<ZaoZ quantizationZget_default_qat_qconfigr9Z prepare_qatZload_state_dictZget_state_dictconverteval)r[r\r]r^r_r#ramodelr.r.r/_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) rDrErFrrr rr IMAGENET1K_V1IMAGENET1K_QNNPACK_V1DEFAULTr.r.r.r/rs( rZquantized_mobilenet_v3_large)nameZ pretrainedcCs|ddrtjStjS)Nr_F)r:rrmrrl)r#r.r.r/s rp)r]TF)r]r^r_)r]r^r_r#r$cKs<|rtnt|}tdi|\}}t|||||fi|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.r/rs/r)- functoolsrtypingrrrr<rrZtorch.ao.quantizationrr Zops.miscr r Ztransforms._presetsr Z_apirrrZ_metar_utilsrrZ mobilenetv3rrrrrutilsrr__all__r rJrlistintrHrhrrr.r.r.r/sP   5  $