kh?* ddlZddlmZddlmZmZmZmZddlZddl m Z ddl m cm Z ddlmZddlmZddlmZmZddlmZd d lmZmZmZd d lmZd d lmZmZd dl m!Z!m"Z"m#Z#gdZ$GddejJZ&GddejNZ(GddejRZ*GddejVZ,GddejZZ.Gddej^Z0GddejbZ2GddejfZ4Gd d!eZ5ed"#ed$d%f&dd'd(d)d*eee5efd+e6d,e6d-ed.e4f d/Z7y)0N)partial)AnyListOptionalUnion)Tensor) inception)Inception_V3_WeightsInceptionOutputs)ImageClassification)register_modelWeights WeightsEnum)_IMAGENET_CATEGORIES)_ovewrite_named_paramhandle_legacy_interface) _fuse_modules _replace_reluquantize_model)QuantizableInception3Inception_V3_QuantizedWeights inception_v3cTeZdZdededdffd ZdedefdZd deeddfd Z xZ S) QuantizableBasicConv2dargskwargsreturnNcVt||i|tj|_yN)super__init__nnReLUreluselfrr __class__s e/var/www/teggl/fontify/venv/lib/python3.12/site-packages/torchvision/models/quantization/inception.pyr$zQuantizableBasicConv2d.__init__s" $)&)GGI xcl|j|}|j|}|j|}|Sr"convbnr')r)r-s r+forwardzQuantizableBasicConv2d.forwards. 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Model is modified in place. Note that this operation does not change numerics and the model after modification is in floating point N)modulestyperr6)r)r3ms r+r6z QuantizableInception3.fuse_models2 %AAw00 V$ %r,r") r7r8r9rr$rr r2rr:r6r;r<s@r+rr}sH;c;S;T;" . .$4 . %$ %4 %r,rcleZdZedeeddddeddejd d d d id ddd Z e Z y)rzUhttps://download.pytorch.org/models/quantized/inception_v3_google_fbgemm-a2837893.pthi+iV) crop_size resize_sizeir)Krfbgemmzdhttps://github.com/pytorch/vision/tree/main/references/classification#post-training-quantized-modelsz ImageNet-1Kg%CKS@g-VW@)zacc@1zacc@5g'1@gL7A`%7@z These weights were produced by doing Post Training Quantization (eager mode) on top of the unquantized weights listed below. ) num_paramsmin_size categoriesbackendrecipe unquantized_metrics_ops _file_size_docs)url transformsmetaN) r7r8r9rrr rr IMAGENET1K_V1IMAGENET1K_FBGEMM_V1DEFAULTr,r+rrsa" c.#3O" .|/==##    0#Gr,rquantized_inception_v3)name pretrainedcf|jddrtjStjS)NquantizeF)getrrr r)rs r+rs+ ::j% (5II ! / /r,)weightsTF)rprogressrrrrrr c 6|rtntj|}|jdd}|id|vr t |ddt |ddt |dt |j dd|j vrt |d|j d|jdd }td i|}t||r t|||F|r|sd|_ d|_ |j|j|d |s|sd|_ d|_ |S) aInception v3 model architecture from `Rethinking the Inception Architecture for Computer Vision `__. .. note:: **Important**: In contrast to the other models the inception_v3 expects tensors with a size of N x 3 x 299 x 299, so ensure your images are sized accordingly. .. 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.Inception_V3_QuantizedWeights` or :class:`~torchvision.models.Inception_V3_Weights`, optional): The pretrained weights for the model. See :class:`~torchvision.models.quantization.Inception_V3_QuantizedWeights` below for more details, and possible values. By default, no pre-trained weights are used. progress (bool, optional): If True, displays a progress bar of the download to stderr. Default is True. quantize (bool, optional): If True, return a quantized version of the model. Default is False. **kwargs: parameters passed to the ``torchvision.models.quantization.QuantizableInception3`` base class. Please refer to the `source code `_ for more details about this class. .. autoclass:: torchvision.models.quantization.Inception_V3_QuantizedWeights :members: .. autoclass:: torchvision.models.Inception_V3_Weights :members: :noindex: rFNtransform_inputT num_classesrrr)r check_hashr)rr verifyrrlenrpoprrrr AuxLogitsload_state_dictget_state_dict)rrrroriginal_aux_logitsrmodels r+rrsd19,>RZZ[bcG **\59 F * !&*;T BflD9fmSl9S5TU  $ !&)W\\)5L MjjH-G ! +F +E%ug& /$E "EO g44hSW4XY 3$E "EO Lr,)8r functoolsrtypingrrrrr{torch.nnr%torch.nn.functional functionalrortorchvision.modelsr inception_moduletorchvision.models.inceptionr r transforms._presetsr _apirrr_metar_utilsrrutilsrrr__all__ BasicConv2dr InceptionAr> InceptionBrR InceptionCrV InceptionDrZ InceptionEr^ InceptionAuxrv Inception3rrr:rrr,r+rsu-- <O677(C??  J-99 J),77)),77)),77)),77)*,77*DM.;;M )%,77)%X#K#8-. 0 UY C e9;OOP QCC C  C  C/Cr,