o V h @spddlZddlmZddlmZmZmZddlZddlm Z ddlm Z ddlm Z ddl mZdd lmZmZmZdd lmZdd lmZmZdd lmZmZmZmZmZmZd dlm Z m!Z!m"Z"gdZ#GdddeZ$GdddeZ%GdddeZ&GdddeZ'GdddeZ(eddedddfddd d!d"d#eee(efd$e)d%e)d&ed'e'f d(d)ZdS)*N)partial)AnyOptionalUnion)Tensor) functional)ImageClassification)register_modelWeights WeightsEnum)_IMAGENET_CATEGORIES)_ovewrite_named_paramhandle_legacy_interface) BasicConv2d GoogLeNetGoogLeNet_WeightsGoogLeNetOutputs Inception InceptionAux) _fuse_modules _replace_reluquantize_model)QuantizableGoogLeNetGoogLeNet_QuantizedWeights googlenetcsTeZdZdededdffdd Zdedefdd Zd d eeddfd d Z Z S)QuantizableBasicConv2dargskwargsreturnNcs tj|i|t|_dSN)super__init__nnReLUreluselfrr  __class__]/var/www/vscode/kcb/lib/python3.10/site-packages/torchvision/models/quantization/googlenet.pyr$szQuantizableBasicConv2d.__init__xcCs"||}||}||}|Sr"convbnr'r)r.r,r,r-forwards   zQuantizableBasicConv2d.forwardis_qatcCst|gd|dddS)Nr/T)inplace)r)r)r4r,r,r- fuse_model$sz!QuantizableBasicConv2d.fuse_modelr") __name__ __module__ __qualname__rr$rr3rboolr6 __classcell__r,r,r*r-rs rc<eZdZdededdffdd Zdedefdd ZZS) QuantizableInceptionrr r!Ncs&tj|dti|tj|_dSN conv_block)r#r$rr% quantizedFloatFunctionalcatr(r*r,r-r$)szQuantizableInception.__init__r.cCs||}|j|dS)Nr)_forwardrB)r)r.outputsr,r,r-r3-s zQuantizableInception.forwardr7r8r9rr$rr3r;r,r,r*r-r=(sr=cr<) QuantizableInceptionAuxrr r!Ncs$tj|dti|t|_dSr>)r#r$rr%r&r'r(r*r,r-r$4sz QuantizableInceptionAux.__init__r.cCsJt|d}||}t|d}|||}||}||}|S)N)rGr) Fadaptive_avg_pool2dr0torchflattenr'fc1dropoutfc2r2r,r,r-r38s     zQuantizableInceptionAux.forwardrEr,r,r*r-rF2srFcsTeZdZdededdffdd Zdedefdd Zd d ee ddfd d Z Z S)rrr r!Ncs<tj|dtttgi|tjj|_ tjj |_ dS)Nblocks) r#r$rr=rFrJao quantization QuantStubquant DeQuantStubdequantr(r*r,r-r$LszQuantizableGoogLeNet.__init__r.cCsl||}||}||\}}}||}|jo|j}tjr/|s)t dt |||S| |||S)NzCScripted QuantizableGoogleNet always returns GoogleNetOutputs Tuple) _transform_inputrSrCrUtraining aux_logitsrJjit is_scriptingwarningswarnr eager_outputs)r)r.aux1aux2 aux_definedr,r,r-r3Ss       zQuantizableGoogLeNet.forwardr4cCs(|D] }t|tur||qdS)aFuse conv/bn/relu modules in googlenet model Fuse conv+bn+relu/ conv+relu/conv+bn modules to prepare for quantization. Model is modified in place. Note that this operation does not change numerics and the model after modification is in floating point N)modulestyperr6)r)r4mr,r,r-r6`s   zQuantizableGoogLeNet.fuse_modelr") r7r8r9rr$rrr3rr:r6r;r,r,r*r-rJs rc@sHeZdZedeeddddeddejdd d d id d dd dZ e Z dS)rzKhttps://download.pytorch.org/models/quantized/googlenet_fbgemm-c81f6644.pth) crop_sizeie)rffbgemmzdhttps://github.com/pytorch/vision/tree/main/references/classification#post-training-quantized-modelsz ImageNet-1Kg/tQ@g`"YV@)zacc@1zacc@5g+?g#~j<)@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) r7r8r9r rr rr IMAGENET1K_V1IMAGENET1K_FBGEMM_V1DEFAULTr,r,r,r-rms* rquantized_googlenet)name pretrainedcCs|ddr tjStjS)NquantizeF)getrrvrru)r r,r,r-s r})weightsTF)r~progressr{r~rr{r r!cKs|rtnt|}|dd}|durBd|vrt|ddt|ddt|ddt|dt|jdd |jvrBt|d |jd |d d }td i|}t ||rZt |||durz| |j |dd |sud|_ d|_d|_|Std |S)aGoogLeNet (Inception v1) model architecture from `Going Deeper with Convolutions `__. .. 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.GoogLeNet_QuantizedWeights` or :class:`~torchvision.models.GoogLeNet_Weights`, optional): The pretrained weights for the model. See :class:`~torchvision.models.quantization.GoogLeNet_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.QuantizableGoogLeNet`` base class. Please refer to the `source code `_ for more details about this class. .. autoclass:: torchvision.models.quantization.GoogLeNet_QuantizedWeights :members: .. autoclass:: torchvision.models.GoogLeNet_Weights :members: :noindex: rXFNtransform_inputT init_weights num_classesrjrkrg)r check_hashz`auxiliary heads in the pretrained googlenet model are NOT pretrained, so make sure to train themr,)rrverifyr|rlenrtpoprrrload_state_dictget_state_dictrXr^r_r[r\)r~rr{r original_aux_logitsrkmodelr,r,r-rs4,       r)*r[ functoolsrtypingrrrrJtorch.nnr%rrrHtransforms._presetsr _apir r r _metar_utilsrrrrrrrrrutilsrrr__all__rr=rFrrr:r,r,r,r-sN        #