U h @s6ddlmZddlmZmZmZddlmZmZddl m Z m Z ddl m Z mZmZddlmZddlmZd d lmZmZmZd d lmZd d lmZmZd dlmZmZm Z dddgZ!Gddde Z"GdddeZ#GdddeZ$eddedddfdddddeee$efe%e%ee#d d!dZ&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_modelQuantizableMobileNetV2MobileNet_V2_QuantizedWeights mobilenet_v2csLeZdZeeddfdd ZeedddZd eeddd d Z Z S) QuantizableInvertedResidualNargskwargsreturncstj||tj|_dSN)super__init__rZ quantizedZFloatFunctionalskip_addselfr r! __class__]/var/www/html/venv/lib/python3.8/site-packages/torchvision/models/quantization/mobilenetv2.pyr%sz$QuantizableInvertedResidual.__init__xr"cCs(|jr|j|||S||SdSr#)Zuse_res_connectr&addconvr(r.r+r+r,forwardsz#QuantizableInvertedResidual.forwardis_qatr"cCsNtt|jD]:}t|j|tjkrt|jt|t|dg|ddqdS)NrTZinplace)rangelenr0typerZConv2drstr)r(r4idxr+r+r, fuse_model"sz&QuantizableInvertedResidual.fuse_model)N __name__ __module__ __qualname__rr%rr2rboolr; __classcell__r+r+r)r,rsrcsLeZdZeeddfdd ZeedddZd eeddd d Z Z S) rNrcs"tj||t|_t|_dS)zq MobileNet V2 main class Args: Inherits args from floating point MobileNetV2 N)r$r%r quantrdequantr'r)r+r,r%)szQuantizableMobileNetV2.__init__r-cCs"||}||}||}|Sr#)rBZ _forward_implrCr1r+r+r,r24s   zQuantizableMobileNetV2.forwardr3cCsJ|D]<}t|tkr.t|dddg|ddt|tkr||qdS)N012Tr5)modulesr8rrrr;)r(r4mr+r+r,r;:s    z!QuantizableMobileNetV2.fuse_model)Nr<r+r+r)r,r(s c@sHeZdZedeeddddeddejdd d d id d dd dZ e Z dS)rzOhttps://download.pytorch.org/models/quantized/mobilenet_v2_qnnpack_37f702c5.pth)Z 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. ) Z num_paramsZmin_size categoriesbackendZrecipeZ unquantizedZ_metricsZ_ops _file_sizeZ_docs)urlZ transformsmetaN) r=r>r?rrrrr IMAGENET1K_V1IMAGENET1K_QNNPACK_V1DEFAULTr+r+r+r,rBs( Zquantized_mobilenet_v2)nameZ pretrainedcCs|ddrtjStjS)NquantizeF)getrrQr rP)r!r+r+r,bs rV)weightsNTF)rWprogressrT)rWrXrTr!r"cKs|rtnt|}|dk rLt|dt|jdd|jkrLt|d|jd|dd}tfdti|}t ||rt |||dk r| |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: NZ num_classesrKrLrJblockT)rXZ check_hash) rr verifyrr7rOpoprrrrZload_state_dictZget_state_dict)rWrXrTr!rLmodelr+r+r,r^s,   )' functoolsrtypingrrrZtorchrrZtorch.ao.quantizationrr Ztorchvision.models.mobilenetv2r r r Zops.miscrZtransforms._presetsrZ_apirrr_metarZ_utilsrrutilsrrr__all__rrrr@rr+r+r+r,sB