Di($ddlmZddlmZmZmZddlZddlmZmZddl m Z m Z ddl m Z mZddlmZd d lmZmZmZd d lmZd d lmZmZd d lmZmZmZmZmZddl m!Z!m"Z"gdZ#GddeZ$GddeZ%GddeZ&de'ede(deede)de)dede&fdZ*Gdd eZ+ed!"ed#d$f%dd&d'd(deee+efde)de)dede&f d)Z,y)*)partial)AnyOptionalUnionN)nnTensor) DeQuantStub QuantStub)Conv2dNormActivationSqueezeExcitation)ImageClassification)register_modelWeights WeightsEnum)_IMAGENET_CATEGORIES)_ovewrite_named_paramhandle_legacy_interface)_mobilenet_v3_confInvertedResidualInvertedResidualConfigMobileNet_V3_Large_Weights MobileNetV3) _fuse_modules _replace_relu)QuantizableMobileNetV3#MobileNet_V3_Large_QuantizedWeightsmobilenet_v3_largecbeZdZdZdededdffd ZdedefdZd d ee ddfd Z fd Z xZ S) QuantizableSqueezeExcitationrargskwargsreturnNctj|d<t| |i|tjj |_y)Nscale_activation)r Hardsigmoidsuper__init__ quantizedFloatFunctionalskip_mulselfr#r$ __class__s m/var/www/pod-logistic/pod-ai/venv/lib/python3.12/site-packages/torchvision/models/quantization/mobilenetv3.pyr*z%QuantizableSqueezeExcitation.__init__!s8%'^^!" $)&) 446 inputcX|jj|j||SN)r-mul_scale)r/r3s r1forwardz$QuantizableSqueezeExcitation.forward&s"}}  U!3U;;r2is_qatc&t|ddg|dy)Nfc1 activationTinplace)r)r/r9s r1 fuse_modelz'QuantizableSqueezeExcitation.fuse_model)sdUL164Hr2c  |jdd}t|dr||dkrtjdgtjdgtjdgtjtjdgtjtjdgtjdgd} | j D]\} } || z} | |vs| || <t ||||||||y) 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_msgsrAdefault_state_dictkvfull_keyr0s r1rJz2QuantizableSqueezeExcitation._load_from_state_dict,s!$$Y5 4 #GaKBG,,PSuBUZ_ZfZfhkglZmGL||UVTW_d_j_jGk_d_k_kCu{{`PU||]^\_O`MR\\[\Z]M^ " +002 -1!A::-+,Jx( - %        r2r5) __name__ __module__ __qualname___versionrr*rr8rboolr?rJ __classcell__r0s@r1r"r"sYH7c7S7T7  g44hSW4XY %%eT%: Lr2cjeZdZedeedddeddejdd d d id d dd Z e Z y)rzUhttps://download.pytorch.org/models/quantized/mobilenet_v3_large_qnnpack-5bcacf28.pth) crop_sizeiS)rrrzUhttps://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_sizerrrecipe unquantized_metrics_ops _file_size_docs)url transformsrN) rVrWrXrrrrr IMAGENET1K_V1IMAGENET1K_QNNPACK_V1DEFAULTr2r1rrs_# c.#>!. m5CC##    0$Gr2rquantized_mobilenet_v3_large)name pretrainedcf|jddrtjStjS)Nr}F)rDrrrr)r$s r1rs0zz*e, 0 E E,99r2)r{TF)r{r|r}c x|rtntj|}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 )rrverifyrr)r{r|r}r$ryrzs r1r r sL^7?2D^ffgnoG.@.`Y_.`+| 8,QY[c ngm nnr2)- functoolsrtypingrrrrFrrtorch.ao.quantizationr r ops.miscr r transforms._presetsr_apirrr_metar_utilsrr mobilenetv3rrrrrutilsrr__all__r"r^rlistintrZrrr rr2r1rsN'' 8?677(C0 2 #42 j !"2 !%[%:!#$:;!!k "! !  !  !!H$+$834   ae 'o e?A[[\ ]'o'o 'o  'o  'o 5'or2