a d# @szddlmZddlmZmZmZmZddlZddlmZm Z ddl m Z m Z ddl mZmZddlmZd d lmZmZmZd d lmZd d lmZmZd d lmZmZmZmZm Z ddl!m"Z"m#Z#gdZ$GdddeZ%GdddeZ&Gddde Z'eee(eee)e)ee'dddZ*GdddeZ+eddeddd fd!dd"d#d$eee+efe)e)ee'd%d&d'Z,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_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__t/var/www/html/stable-diffusion-webui/venv/lib/python3.9/site-packages/torchvision/models/quantization/mobilenetv3.pyr'!s z%QuantizableSqueezeExcitation.__init__)inputr%cCs|j|||SN)r*mulZ_scale)r,r1r/r/r0forward&sz$QuantizableSqueezeExcitation.forwardis_qatr%cCst|ddg|dddS)NZfc1Z activationTZinplace)r)r,r6r/r/r0 fuse_model)sz'QuantizableSqueezeExcitation.fuse_modelc s|dd}t|dr|dus&|dkrtdgtdgtjdgtjdtjdgtjdtdgtdgd} | D] \} } || } | |vr|| || <q|t|||||||dS) Nversionqconfigrg?r)Zdtyper)z.scale_activation.activation_post_process.scalezFscale_activation.activation_post_process.activation_post_process.scalez3scale_activation.activation_post_process.zero_pointzKscale_activation.activation_post_process.activation_post_process.zero_pointz;scale_activation.activation_post_process.fake_quant_enabledz9scale_activation.activation_post_process.observer_enabled)gethasattrtorchZtensorint32itemsr&_load_from_state_dict) r,Z state_dictprefixZlocal_metadatastrictZ missing_keysZunexpected_keysZ error_msgsr9Zdefault_state_dictkvZfull_keyr-r/r0r@,s0       z2QuantizableSqueezeExcitation._load_from_state_dict)N) __name__ __module__ __qualname___versionrr'rr4rboolr8r@ __classcell__r/r/r-r0r!s r!cs6eZdZeeddfdd ZeedddZZS)QuantizableInvertedResidualNr"cs&tj|dti|tj|_dS)NZse_layer)r&r'r!rr(r)skip_addr+r-r/r0r'Usz$QuantizableInvertedResidual.__init__xr%cCs(|jr|j|||S||SdSr2)Zuse_res_connectrLaddblockr,rNr/r/r0r4Ysz#QuantizableInvertedResidual.forward)rErFrGrr'rr4rJr/r/r-r0rKSsrKcsLeZdZeeddfdd 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 quantr dequantr+r-r/r0r'aszQuantizableMobileNetV3.__init__rMcCs"||}||}||}|Sr2)rRZ _forward_implrSrQr/r/r0r4ls   zQuantizableMobileNetV3.forwardr5cCsv|D]h}t|turZddg}t|dkrHt|dtjurH|dt|||ddqt|tur| |qdS)N01r r2Tr7) modulestyper lenrZReLUappendrr!r8)r,r6mZmodules_to_fuser/r/r0r8rs    z!QuantizableMobileNetV3.fuse_model)N) rErFrGrr'rr4rrIr8rJr/r/r-r0r`s r)inverted_residual_setting last_channelweightsprogressquantizer$r%cKs|dur:t|dt|jdd|jvr:t|d|jd|dd}t||fdti|}t||r|jddtj j ||_ tj j j |dd|dur||j|d |rtj j j|dd||S) NZ num_classes categoriesbackendqnnpackrPT)r6r7)r_)rrYmetapoprrKrr8r=ZaoZ quantizationZget_default_qat_qconfigr:Z prepare_qatZload_state_dictZget_state_dictconverteval)r\r]r^r_r`r$rbmodelr/r/r0_mobilenet_v3_model}s"   ric@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)rrrczUhttps://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_sizerarbZrecipeZ unquantizedZ_metricsZ_ops _file_sizeZ_docs)urlZ transformsrdN) rErFrGrrrrr IMAGENET1K_V1IMAGENET1K_QNNPACK_V1DEFAULTr/r/r/r0rs( rZquantized_mobilenet_v3_large)nameZ pretrainedcCs|ddrtjStjS)Nr`F)r;rrnrrm)r$r/r/r0s rq)r^TF)r^r_r`)r^r_r`r$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 )rrverifyrri)r^r_r`r$r\r]r/r/r0r s-r )- functoolsrtypingrrrrr=rrZtorch.ao.quantizationr r Zops.miscr r Ztransforms._presetsrZ_apirrrZ_metar_utilsrrZ mobilenetv3rrrrrutilsrr__all__r!rKrintrIrirr r/r/r/r0sP   5  $