o V h$@sddlmZddlmZmZmZmZddlZddlmZm Z ddl m Z m Z ddl mZmZddlmZd d lmZmZmZd d lmZd d lmZmZd d lmZmZmZmZm Z ddl!m"Z"m#Z#gdZ$GdddeZ%GdddeZ&Gddde Z'deede(deede)de)dede'fddZ*Gd d!d!eZ+ed"d#ed$d%d&fd'dd(d)d*deee+efde)de)dede'f d+d,Z,dS)-)partial)AnyListOptionalUnionN)nnTensor) DeQuantStub QuantStub)Conv2dNormActivationSqueezeExcitation)ImageClassification)register_modelWeights WeightsEnum)_IMAGENET_CATEGORIES)_ovewrite_named_paramhandle_legacy_interface)_mobilenet_v3_confInvertedResidualInvertedResidualConfigMobileNet_V3_Large_Weights MobileNetV3) _fuse_modules _replace_relu)QuantizableMobileNetV3#MobileNet_V3_Large_QuantizedWeightsmobilenet_v3_largecsdeZdZdZdededdffdd Zdedefd d Zdd ee ddfd d Z fddZ Z S)QuantizableSqueezeExcitationrargskwargsreturnNcs,tj|d<tj|i|tj|_dS)Nscale_activation)r Hardsigmoidsuper__init__ quantizedFloatFunctionalskip_mulselfr"r# __class___/var/www/vscode/kcb/lib/python3.10/site-packages/torchvision/models/quantization/mobilenetv3.pyr(!s z%QuantizableSqueezeExcitation.__init__inputcCs|j|||SN)r+mul_scale)r-r2r0r0r1forward&sz$QuantizableSqueezeExcitation.forwardis_qatcCst|ddg|dddS)Nfc1 activationTinplace)r)r-r7r0r0r1 fuse_model)sz'QuantizableSqueezeExcitation.fuse_modelc s|dd}t|drO|dus|dkrOtdgtdgtjdgtjdtjdgtjdtdgtdgd} | D]\} } || } | |vrN| || <q>t|||||||dS) Nversionqconfigrg?r)dtyper)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)gethasattrtorchtensorint32itemsr'_load_from_state_dict) r- state_dictprefixlocal_metadatastrict missing_keysunexpected_keys error_msgsr=default_state_dictkvfull_keyr.r0r1rF,s2      z2QuantizableSqueezeExcitation._load_from_state_dictr3) __name__ __module__ __qualname___versionrr(rr6rboolr<rF __classcell__r0r0r.r1r!s r!cs<eZdZdededdffdd Zdedefdd ZZS) QuantizableInvertedResidualr"r#r$Ncs&tj|dti|tj|_dS)Nse_layer)r'r(r!rr)r*skip_addr,r.r0r1r(Usz$QuantizableInvertedResidual.__init__xcCs$|jr |j|||S||Sr3)use_res_connectrZaddblockr-r[r0r0r1r6Ys z#QuantizableInvertedResidual.forward)rRrSrTrr(rr6rWr0r0r.r1rXSsrXcsTeZdZdededdffdd Zdedefdd Zd d eeddfd d Z Z S)rr"r#r$Ncs&tj|i|t|_t|_dS)zq MobileNet V3 main class Args: Inherits args from floating point MobileNetV3 N)r'r(r quantr dequantr,r.r0r1r(as zQuantizableMobileNetV3.__init__r[cCs"||}||}||}|Sr3)r` _forward_implrar_r0r0r1r6ls   zQuantizableMobileNetV3.forwardr7cCsv|D]4}t|tur-ddg}t|dkr$t|dtjur$|dt|||ddqt|tur8| |qdS)N01r r2Tr:) modulestyper lenrReLUappendrr!r<)r-r7mmodules_to_fuser0r0r1r<rs     z!QuantizableMobileNetV3.fuse_modelr3) rRrSrTrr(rr6rrVr<rWr0r0r.r1r`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) N num_classes categoriesbackendqnnpackr^T)r7r:)rp check_hash)rrhmetapoprrXrr<rBao quantizationget_default_qat_qconfigr> prepare_qatload_state_dictget_state_dictconverteval)rmrnrorprqr#rtmodelr0r0r1_mobilenet_v3_model}s"   rc@sHeZdZedeeddddeddejdd d d id d dd dZ e Z dS)rzUhttps://download.pytorch.org/models/quantized/mobilenet_v3_large_qnnpack-5bcacf28.pth) crop_sizeiS)rrruzUhttps://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. ) num_paramsmin_sizersrtrecipe unquantized_metrics_ops _file_size_docs)url transformsrwN) rRrSrTrrrrr IMAGENET1K_V1IMAGENET1K_QNNPACK_V1DEFAULTr0r0r0r1rs* rquantized_mobilenet_v3_large)name pretrainedcCs|ddr tjStjS)NrqF)r@rrrr)r#r0r0r1s r)roTF)rorprqcKs<|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 N)r )rrverifyrr)rorprqr#rmrnr0r0r1r s-r )- functoolsrtypingrrrrrBrrtorch.ao.quantizationr r ops.miscr r transforms._presetsr_apirrr_metar_utilsrr mobilenetv3rrrrrutilsrr__all__r!rXrintrVrrr r0r0r0r1sh   5  $