o V h @s<ddlmZddlmZmZmZddlmZmZddl m Z m Z ddl m Z mZmZddlmZddlmZd d lmZmZmZd d lmZd d lmZmZd dlmZmZm Z gdZ!Gddde Z"GdddeZ#GdddeZ$eddedddfdddddd eee$efd!e%d"e%d#ed$e#f d%d&Z&dS)')partial)AnyOptionalUnion)nnTensor) DeQuantStub QuantStub)InvertedResidualMobileNet_V2_Weights MobileNetV2)Conv2dNormActivation)ImageClassification)register_modelWeights WeightsEnum)_IMAGENET_CATEGORIES)_ovewrite_named_paramhandle_legacy_interface) _fuse_modules _replace_reluquantize_model)QuantizableMobileNetV2MobileNet_V2_QuantizedWeights mobilenet_v2cTeZdZdededdffdd Zdedefdd Zd d eeddfd d Z Z S)QuantizableInvertedResidualargskwargsreturnNcs"tj|i|tj|_dSN)super__init__r quantizedFloatFunctionalskip_addselfr r! __class___/var/www/vscode/kcb/lib/python3.10/site-packages/torchvision/models/quantization/mobilenetv2.pyr%sz$QuantizableInvertedResidual.__init__xcCs$|jr |j|||S||Sr#)use_res_connectr(addconvr*r/r-r-r.forwards z#QuantizableInvertedResidual.forwardis_qatcCsNtt|jD]}t|j|tjur$t|jt|t|dg|ddqdS)NrTinplace)rangelenr2typerConv2drstr)r*r5idxr-r-r. fuse_model"s "z&QuantizableInvertedResidual.fuse_modelr# __name__ __module__ __qualname__rr%rr4rboolr> __classcell__r-r-r+r.rs rcr)rr r!r"Ncs&tj|i|t|_t|_dS)zq MobileNet V2 main class Args: Inherits args from floating point MobileNetV2 N)r$r%r quantrdequantr)r+r-r.r%)s zQuantizableMobileNetV2.__init__r/cCs"||}||}||}|Sr#)rE _forward_implrFr3r-r-r.r44s   zQuantizableMobileNetV2.forwardr5cCsH|D]}t|turt|gd|ddt|tur!||qdS)N)012Tr6)modulesr:rrrr>)r*r5mr-r-r.r>:s    z!QuantizableMobileNetV2.fuse_modelr#r?r-r-r+r.r(s rc@sHeZdZedeeddddeddejdd d d id d dd dZ e Z dS)rzOhttps://download.pytorch.org/models/quantized/mobilenet_v2_qnnpack_37f702c5.pth) crop_sizeiz5)rrqnnpackzUhttps://github.com/pytorch/vision/tree/main/references/classification#qat-mobilenetv2z ImageNet-1Kg'1Q@gV@)zacc@1zacc@5g$C?gMb @z These weights were produced by doing Quantization Aware Training (eager mode) on top of the unquantized weights listed below. ) num_paramsmin_size categoriesbackendrecipe unquantized_metrics_ops _file_size_docs)url transformsmetaN) r@rArBrrrrr IMAGENET1K_V1IMAGENET1K_QNNPACK_V1DEFAULTr-r-r-r.rBs* rquantized_mobilenet_v2)name pretrainedcCs|ddr tjStjS)NquantizeF)getrr^r r])r!r-r-r.bs re)weightsNTF)rfprogressrcrfrgrcr!r"cKs|rtnt|}|dur&t|dt|jdd|jvr&t|d|jd|dd}td dti|}t ||r@t |||durN| |j |dd|S) a Constructs a MobileNetV2 architecture from `MobileNetV2: Inverted Residuals and Linear Bottlenecks `_. .. 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_V2_QuantizedWeights` or :class:`~torchvision.models.MobileNet_V2_Weights`, optional): The pretrained weights for the model. See :class:`~torchvision.models.quantization.MobileNet_V2_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, returns a quantized version of the model. Default is False. **kwargs: parameters passed to the ``torchvision.models.quantization.QuantizableMobileNetV2`` base class. Please refer to the `source code `_ for more details about this class. .. autoclass:: torchvision.models.quantization.MobileNet_V2_QuantizedWeights :members: .. autoclass:: torchvision.models.MobileNet_V2_Weights :members: :noindex: N num_classesrRrSrOblockT)rg check_hashr-) rr verifyrr9r\poprrrrload_state_dictget_state_dict)rfrgrcr!rSmodelr-r-r.r^s,   r)' functoolsrtypingrrrtorchrrtorch.ao.quantizationrr torchvision.models.mobilenetv2r r r ops.miscrtransforms._presetsr_apirrr_metar_utilsrrutilsrrr__all__rrrrCrr-r-r-r.sF