a PSic$@sddlmZddlmZmZmZmZddlZddlmZm Z ddl m Z m Z ddl mZmZddlmZd d lmZmZd d lmZd d lmZmZd d lmZmZmZmZmZddl m!Z!m"Z"gdZ#GdddeZ$GdddeZ%GdddeZ&eee'eee(e(ee&dddZ)GdddeZ*edddfddd d!d"eee*efe(e(ee&d#d$d%Z+d d&lm,Z,d d'lm-Z-e,d(e*j.j/iZ0dS)))partial)AnyListOptionalUnionN)nnTensor) QuantStub DeQuantStub)Conv2dNormActivationSqueezeExcitation)ImageClassification) WeightsEnumWeights)_IMAGENET_CATEGORIES)handle_legacy_interface_ovewrite_named_param)InvertedResidualInvertedResidualConfig MobileNetV3_mobilenet_v3_confMobileNet_V3_Large_Weights) _fuse_modules _replace_relu)QuantizableMobileNetV3#MobileNet_V3_Large_QuantizedWeightsmobilenet_v3_largecs\eZdZdZeeddfdd ZeedddZdee dd d d Z fd d Z Z S)QuantizableSqueezeExcitationrNargskwargsreturncs,tj|d<tj|i|tj|_dS)Nscale_activation)r Hardsigmoidsuper__init__ quantizedFloatFunctionalskip_mulselfr"r# __class__g/var/www/html/django/DPS/env/lib/python3.9/site-packages/torchvision/models/quantization/mobilenetv3.pyr(!s z%QuantizableSqueezeExcitation.__init__)inputr$cCs|j|||SN)r+mul_scale)r-r2r0r0r1forward&sz$QuantizableSqueezeExcitation.forwardis_qatr$cCst|ddg|dddS)Nfc1 activationTinplace)r)r-r8r0r0r1 fuse_model)sz'QuantizableSqueezeExcitation.fuse_modelc s|dd}t|dr|dus&|dkrtdgtdgtjdgtjdtjdgtjdtdgtdgd} | D] \} } || } | |vr|| || <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>Zdefault_state_dictkvZfull_keyr.r0r1rG,s0       z2QuantizableSqueezeExcitation._load_from_state_dict)N) __name__ __module__ __qualname___versionrr(rr6rboolr=rG __classcell__r0r0r.r1r s r cs6eZdZeeddfdd ZeedddZZS)QuantizableInvertedResidualNr!cs&tj|dti|tj|_dS)Nse_layer)r'r(r rr)r*skip_addr,r.r0r1r(Usz$QuantizableInvertedResidual.__init__xr$cCs(|jr|j|||S||SdSr3)use_res_connectrYaddblockr-r[r0r0r1r6Ysz#QuantizableInvertedResidual.forward)rQrRrSrr(rr6rVr0r0r.r1rWSsrWcsLeZdZeeddfdd 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.r0r1r(aszQuantizableMobileNetV3.__init__rZcCs"||}||}||}|Sr3)r` _forward_implrar_r0r0r1r6ls   zQuantizableMobileNetV3.forwardr7cCsv|D]h}t|turZddg}t|dkrHt|dtjurH|dt|||ddqt|tur| |qdS)N01r r2Tr;) modulestyper lenrReLUappendrr r=)r-r8mmodules_to_fuser0r0r1r=rs    z!QuantizableMobileNetV3.fuse_model)N) rQrRrSrr(rr6rrUr=rVr0r0r.r1r`s r)inverted_residual_setting last_channelweightsprogressquantizer#r$cKs|dur:t|dt|jdd|jvr:t|d|jd|dd}t||fdti|}t||r|jddtj j ||_ tj j j |dd|dur||j|d |rtj j j|dd||S) N num_classes categoriesbackendqnnpackr^T)r8r;)rp)rrhmetapoprrWrr=rCao quantizationget_default_qat_qconfigr? prepare_qatload_state_dictget_state_dictconverteval)rmrnrorprqr#rtmodelr0r0r1_mobilenet_v3_model}s"   rc @sDeZdZedeeddddeddejdd d d id d dZ e Z dS)rzUhttps://download.pytorch.org/models/quantized/mobilenet_v3_large_qnnpack-5bcacf28.pth) crop_sizeiS)rrruzUhttps://github.com/pytorch/vision/tree/main/references/classification#qat-mobilenetv3z ImageNet-1KgK7A@R@gxV@)zacc@1zacc@5z These weights were produced by doing Quantization Aware Training (eager mode) on top of the unquantized weights listed below. ) num_paramsmin_sizersrtrecipe unquantized_metrics_docs)url transformsrvN) rQrRrSrrrrr IMAGENET1K_V1IMAGENET1K_QNNPACK_V1DEFAULTr0r0r0r1rs$ r pretrainedcCs|ddrtjStjS)NrqF)rArrrr)r#r0r0r1s r)roTF)rorprq)rorprqr#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)rrverifyrr)rorprqr#rmrnr0r0r1rs,r) _ModelURLs) model_urlsZmobilenet_v3_large_qnnpack)1 functoolsrtypingrrrrrCrrtorch.ao.quantizationr r Zops.miscr r Ztransforms._presetsr_apirr_metar_utilsrr mobilenetv3rrrrrutilsrr__all__r rWrintrUrrrrrrrquant_model_urlsr0r0r0r1sZ   5  $ +