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$d%d&fd'dd(d)d*deee+efde)de)dede&f d+d,Z,dS)-)partial)AnyOptionalUnionN)nnTensor) DeQuantStub QuantStub)Conv2dNormActivationSqueezeExcitation)ImageClassification)register_modelWeights WeightsEnum)_IMAGENET_CATEGORIES)_ovewrite_named_paramhandle_legacy_interface)_mobilenet_v3_confInvertedResidualInvertedResidualConfigMobileNet_V3_Large_Weights MobileNetV3) _fuse_modules _replace_relu)QuantizableMobileNetV3#MobileNet_V3_Large_QuantizedWeightsmobilenet_v3_largecsdeZdZdZdededdffdd Zdedefd d Zdd ee ddfd d Z fddZ Z S)QuantizableSqueezeExcitationrargskwargsreturnNcs,tj|d<tj|i|tj|_dS)Nscale_activation)r Hardsigmoidsuper__init__ quantizedFloatFunctionalskip_mulselfr!r" __class__f/var/www/html/scripts/venv/lib/python3.10/site-packages/torchvision/models/quantization/mobilenetv3.pyr'!s z%QuantizableSqueezeExcitation.__init__inputcCs|j|||SN)r*mul_scale)r,r1r/r/r0forward&sz$QuantizableSqueezeExcitation.forwardis_qatcCst|ddg|dddS)Nfc1 activationTinplace)r)r,r6r/r/r0 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-r/r0rE,s2      z2QuantizableSqueezeExcitation._load_from_state_dictr2) __name__ __module__ __qualname___versionrr'rr5rboolr;rE __classcell__r/r/r-r0r s r cs<eZdZdededdffdd Zdedefdd ZZS) QuantizableInvertedResidualr!r"r#Ncs&tj|dti|tj|_dS)Nse_layer)r&r'r rr(r)skip_addr+r-r/r0r'Usz$QuantizableInvertedResidual.__init__xcCs$|jr |j|||S||Sr2)use_res_connectrYaddblockr,rZr/r/r0r5Ys z#QuantizableInvertedResidual.forward)rQrRrSrr'rr5rVr/r/r-r0rWSsrWcsTeZdZdededdffdd 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/r0r'as zQuantizableMobileNetV3.__init__rZcCs"||}||}||}|Sr2)r_ _forward_implr`r^r/r/r0r5ls   zQuantizableMobileNetV3.forwardr6cCsv|D]4}t|tur-ddg}t|dkr$t|dtjur$|dt|||ddqt|tur8| |qdS)N01r r2Tr9) modulestyper lenrReLUappendrr r;)r,r6mmodules_to_fuser/r/r0r;rs     z!QuantizableMobileNetV3.fuse_modelr2) rQrRrSrr'rr5rrUr;rVr/r/r-r0r`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)r6r9)ro check_hash)rrgmetapoprrWrr;rAao quantizationget_default_qat_qconfigr= prepare_qatload_state_dictget_state_dictconverteval)rlrmrnrorpr"rsmodelr/r/r0_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)rrrtzUhttps://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_sizerrrsrecipe unquantized_metrics_ops _file_size_docs)url transformsrvN) rQrRrSrrr rr IMAGENET1K_V1IMAGENET1K_QNNPACK_V1DEFAULTr/r/r/r0rs* rquantized_mobilenet_v3_large)name pretrainedcCs|ddr tjStjS)NrpF)r?rrrr)r"r/r/r0s r)rnTF)rnrorpcKs<|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)rrverifyrr)rnrorpr"rlrmr/r/r0rs/r)- functoolsrtypingrrrrArrtorch.ao.quantizationrr ops.miscr r transforms._presetsr _apirrr_metar_utilsrr mobilenetv3rrrrrutilsrr__all__r rWrlistintrUrrrr/r/r/r0sh   5  $