kh$ddlmZddlmZmZmZmZddlZddlmZm Z ddl m Z m Z ddl mZmZddlmZd d lmZmZmZd d lmZd d lmZmZd d lmZmZmZmZm Z ddl!m"Z"m#Z#gdZ$GddeZ%GddeZ&Gdde Z'deede(deede)de)dede'fdZ*Gdd eZ+ed!"ed#d$f%dd&d'd(deee+efde)de)dede'f d)Z,y)*)partial)AnyListOptionalUnionN)nnTensor) DeQuantStub QuantStub)Conv2dNormActivationSqueezeExcitation)ImageClassification)register_modelWeights WeightsEnum)_IMAGENET_CATEGORIES)_ovewrite_named_paramhandle_legacy_interface)_mobilenet_v3_confInvertedResidualInvertedResidualConfigMobileNet_V3_Large_Weights MobileNetV3) _fuse_modules _replace_relu)QuantizableMobileNetV3#MobileNet_V3_Large_QuantizedWeightsmobilenet_v3_largecbeZdZdZdededdffd ZdedefdZd d ee ddfd Z fd Z xZ S) QuantizableSqueezeExcitationrargskwargsreturnNctj|d<t| |i|tjj |_y)Nscale_activation)r Hardsigmoidsuper__init__ quantizedFloatFunctionalskip_mulselfr$r% __class__s g/var/www/teggl/fontify/venv/lib/python3.12/site-packages/torchvision/models/quantization/mobilenetv3.pyr+z%QuantizableSqueezeExcitation.__init__!s8%'^^!" $)&) 446 inputcX|jj|j||SN)r.mul_scale)r0r4s r2forwardz$QuantizableSqueezeExcitation.forward&s"}}  U!3U;;r3is_qatc&t|ddg|dy)Nfc1 activationTinplace)r)r0r:s r2 fuse_modelz'QuantizableSqueezeExcitation.fuse_model)sdUL164Hr3c  |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)r0 state_dictprefixlocal_metadatastrict missing_keysunexpected_keys error_msgsrBdefault_state_dictkvfull_keyr1s r2rKz2QuantizableSqueezeExcitation._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( - %        r3r6) __name__ __module__ __qualname___versionrr+r r9rboolr@rK __classcell__r1s@r2r#r#sYH7c7S7T7 ) modulestyper lenrReLUappendrr#r@)r0r:mmodules_to_fuses r2r@z!QuantizableMobileNetV3.fuse_modelrs %AAw..#&*q6Q;4!:#8#**3/a&$Ga88 V$ %r3r6) rWrXrYrr+r r9rr[r@r\r]s@r2rr`sG %c %S %T %F %$%4%r3rinverted_residual_setting last_channelweightsprogressquantizer%r&c |Kt|dt|jdd|jvrt|d|jd|jdd}t ||fdt i|}t ||rk|jdtjjj||_ tjjj|d|"|j|j|d |r;tjjj!|d|j#|S) N num_classes categoriesbackendqnnpackrgT)r:r>)r} check_hash)rrumetapoprr_rr@rGao quantizationget_default_qat_qconfigrC prepare_qatload_state_dictget_state_dictconverteval)rzr{r|r}r~r%rmodels r2_mobilenet_v3_modelr}sfmSl9S5TU  $ !&)W\\)5L MjjI.G "# g44hSW4XY %%eT%: Lr3cjeZdZedeedddeddejdd d d id d dd Z e Z y)r zUhttps://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) rWrXrYrrrrr IMAGENET1K_V1IMAGENET1K_QNNPACK_V1DEFAULTr3r2r r s_# c.#>!. m5CC##    0$Gr3r quantized_mobilenet_v3_large)name pretrainedcf|jddrtjStjS)Nr~F)rEr rrr)r%s r2rs+ ::j% (;PP ' 5 5r3)r|TF)r|r}r~c x|rtntj|}tdi|\}}t |||||fi|S)a MobileNetV3 (Large) model from `Searching for MobileNetV3 `_. .. note:: Note that ``quantize = True`` returns a quantized model with 8 bit weights. Quantized models only support inference and run on CPUs. GPU inference is not yet supported. Args: weights (:class:`~torchvision.models.quantization.MobileNet_V3_Large_QuantizedWeights` or :class:`~torchvision.models.MobileNet_V3_Large_Weights`, optional): The pretrained weights for the model. See :class:`~torchvision.models.quantization.MobileNet_V3_Large_QuantizedWeights` below for more details, and possible values. By default, no pre-trained weights are used. progress (bool): If True, displays a progress bar of the download to stderr. Default is True. quantize (bool): If True, return a quantized version of the model. Default is False. **kwargs: parameters passed to the ``torchvision.models.quantization.MobileNet_V3_Large_QuantizedWeights`` base class. Please refer to the `source code `_ for more details about this class. .. autoclass:: torchvision.models.quantization.MobileNet_V3_Large_QuantizedWeights :members: .. autoclass:: torchvision.models.MobileNet_V3_Large_Weights :members: :noindex: )r!)r rverifyrr)r|r}r~r%rzr{s r2r!r!sLZ7?2D^ffgnoG.@.`Y_.`+| 8,QY[c ngm nnr3)- functoolsrtypingrrrrrGrr torch.ao.quantizationr r ops.miscr rtransforms._presetsr_apirrr_metar_utilsrr mobilenetv3rrrrrutilsrr__all__r#r_rintr[rr r!rr3r2rsN-- 8?677(C0 2 #42 j !"2 !%[%:!#$:;!!k "! !  !  !!H$+$834 6 ae 'o e?A[[\ ]'o'o 'o  'o  'o5'or3