o h($@sddlmZddlmZmZmZddlZddlmZmZddl m Z m Z ddl m Z mZddlmZd d lmZmZmZd d lmZd d lmZmZd d lmZmZmZmZmZddl m!Z!m"Z"gdZ#GdddeZ$GdddeZ%GdddeZ&de'ede(deede)de)dede&fddZ*Gd d!d!eZ+ed"d#ed$d%d&fd'dd(d)d*deee+efde)de)dede&f d+d,Z,dS)-)partial)AnyOptionalUnionN)nnTensor) DeQuantStub QuantStub)Conv2dNormActivationSqueezeExcitation)ImageClassification)register_modelWeights WeightsEnum)_IMAGENET_CATEGORIES)_ovewrite_named_paramhandle_legacy_interface)_mobilenet_v3_confInvertedResidualInvertedResidualConfigMobileNet_V3_Large_Weights MobileNetV3) _fuse_modules _replace_relu)QuantizableMobileNetV3#MobileNet_V3_Large_QuantizedWeightsmobilenet_v3_largecsdeZdZdZdededdffdd Zdedefd d Zdd ee ddfd d Z fddZ Z S)QuantizableSqueezeExcitationrargskwargsreturnNcs,tj|d<tj|i|tj|_dS)NZscale_activation)rZ Hardsigmoidsuper__init__ quantizedFloatFunctionalskip_mulselfr!r" __class__q/home/www/facesmatcher.com/frenv_anti/lib/python3.10/site-packages/torchvision/models/quantization/mobilenetv3.pyr%!s z%QuantizableSqueezeExcitation.__init__inputcCs|j|||SN)r(mulZ_scale)r*r/r-r-r.forward&sz$QuantizableSqueezeExcitation.forwardis_qatcCst|ddg|dddS)NZfc1Z activationTZinplace)r)r*r3r-r-r. fuse_model)sz'QuantizableSqueezeExcitation.fuse_modelc s|dd}t|drO|dus|dkrOtdgtdgtjdgtjdtjdgtjdtdgtdgd} | D]\} } || } | |vrN| || <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$_load_from_state_dict) r*Z state_dictprefixZlocal_metadatastrictZ missing_keysZunexpected_keys error_msgsr6Zdefault_state_dictkvZfull_keyr+r-r.r<,s2      z2QuantizableSqueezeExcitation._load_from_state_dictr0) __name__ __module__ __qualname___versionrr%rr2rboolr5r< __classcell__r-r-r+r.r s r cs<eZdZdededdffdd Zdedefdd ZZS) QuantizableInvertedResidualr!r"r#Ncs&tj|dti|tj|_dS)NZse_layer)r$r%r rr&r'skip_addr)r+r-r.r%Usz$QuantizableInvertedResidual.__init__xcCs$|jr |j|||S||Sr0)Zuse_res_connectrIaddblockr*rJr-r-r.r2Ys z#QuantizableInvertedResidual.forward)rBrCrDrr%rr2rGr-r-r+r.rHSsrHcsTeZdZdededdffdd Zdedefdd Zd d eeddfd d Z Z S)rr!r"r#Ncs&tj|i|t|_t|_dS)zq MobileNet V3 main class Args: Inherits args from floating point MobileNetV3 N)r$r%r quantrdequantr)r+r-r.r%as zQuantizableMobileNetV3.__init__rJcCs"||}||}||}|Sr0)rNZ _forward_implrOrMr-r-r.r2ls   zQuantizableMobileNetV3.forwardr3cCsv|D]4}t|tur-ddg}t|dkr$t|dtjur$|dt|||ddqt|tur8| |qdS)N01r r2Tr4) modulestyper lenrZReLUappendrr r5)r*r3mZmodules_to_fuser-r-r.r5rs     z!QuantizableMobileNetV3.fuse_modelr0) rBrCrDrr%rr2rrFr5rGr-r-r+r.r`s rinverted_residual_setting last_channelweightsprogressquantizer"r#cKs|durt|dt|jdd|jvrt|d|jd|dd}t||fdti|}t||rK|jddtj j ||_ tj j j |dd|durY||j|dd |rhtj j j|dd||S) NZ num_classes categoriesbackendqnnpackrLT)r3r4)r[Z check_hash)rrUmetapoprrHrr5r:ZaoZ quantizationZget_default_qat_qconfigr7Z prepare_qatZload_state_dictZget_state_dictconverteval)rXrYrZr[r\r"r^modelr-r-r._mobilenet_v3_model}s"   rec@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)rrr_zUhttps://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]r^ZrecipeZ unquantizedZ_metricsZ_ops _file_sizeZ_docs)urlZ transformsr`N) rBrCrDrrr rr IMAGENET1K_V1IMAGENET1K_QNNPACK_V1DEFAULTr-r-r-r.rs* rZquantized_mobilenet_v3_large)nameZ pretrainedcCs|ddr tjStjS)Nr\F)r8rrjrri)r"r-r-r.s rm)rZTF)rZr[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: rN)r)rrverifyrre)rZr[r\r"rXrYr-r-r.rs/r)- functoolsrtypingrrrr:rrZtorch.ao.quantizationrr Zops.miscr r Ztransforms._presetsr Z_apirrr_metarZ_utilsrrZ mobilenetv3rrrrrutilsrr__all__r rHrlistintrFrerrr-r-r-r.sh   5  $