o ,hY* @sddlZddlmZddlmZmZmZddlZddlm Z ddl m m Z ddlm Z ddlmZddlmZmZddlmZd d lmZmZmZd d lmZd d lmZmZd dlm Z m!Z!m"Z"gdZ#Gdddej$Z%Gdddej&Z'Gdddej(Z)Gdddej*Z+Gdddej,Z-Gdddej.Z/Gdddej0Z1Gdddej2Z3Gd d!d!eZ4ed"d#ed$d%d&fd'dd(d)d*d+eee4efd,e5d-e5d.ed/e3f d0d1Z6dS)2N)partial)AnyOptionalUnion)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_v3csTeZdZdededdffdd Zdedefdd Zd d eeddfd d Z Z S)QuantizableBasicConv2dargskwargsreturnNcs tj|i|t|_dSN)super__init__nnReLUreluselfrr __class__d/var/www/html/scripts/venv/lib/python3.10/site-packages/torchvision/models/quantization/inception.pyr szQuantizableBasicConv2d.__init__xcCs"||}||}||}|Srconvbnr#)r%r*r(r(r)forwards   zQuantizableBasicConv2d.forwardis_qatcCst|gd|dddS)Nr+T)inplace)r)r%r/r(r(r) fuse_model%sz!QuantizableBasicConv2d.fuse_modelr) __name__ __module__ __qualname__rr rr.rboolr1 __classcell__r(r(r&r)rs rc<eZdZdededdffdd Zdedefdd ZZS) QuantizableInceptionArrrNc&tj|dti|tj|_dSN conv_blockrr rr! quantizedFloatFunctionalmyopr$r&r(r)r +zQuantizableInceptionA.__init__r*cC||}|j|dSNr_forwardr?catr%r*outputsr(r(r)r./ zQuantizableInceptionA.forwardr2r3r4rr rr.r6r(r(r&r)r8)r8cr7) QuantizableInceptionBrrrNcr9r:r<r$r&r(r)r 6r@zQuantizableInceptionB.__init__r*cCrArBrCrFr(r(r)r.:rHzQuantizableInceptionB.forwardrIr(r(r&r)rK4rJrKcr7) QuantizableInceptionCrrrNcr9r:r<r$r&r(r)r Ar@zQuantizableInceptionC.__init__r*cCrArBrCrFr(r(r)r.ErHzQuantizableInceptionC.forwardrIr(r(r&r)rL?rJrLcr7) QuantizableInceptionDrrrNcr9r:r<r$r&r(r)r Lr@zQuantizableInceptionD.__init__r*cCrArBrCrFr(r(r)r.PrHzQuantizableInceptionD.forwardrIr(r(r&r)rMJrJrMcsReZdZdededdffdd Zdedeefdd Zdedefd d ZZ S) QuantizableInceptionErrrNcs>tj|dti|tj|_tj|_tj|_dSr:) rr rr!r=r>myop1myop2myop3r$r&r(r)r Ws  zQuantizableInceptionE.__init__r*cCs||}||}||||g}|j|d}||}||}||| |g}|j |d}t j |dddd}| |}||||g}|S)Nrr ) kernel_sizestridepadding) branch1x1 branch3x3_1 branch3x3_2a branch3x3_2brOrEbranch3x3dbl_1branch3x3dbl_2branch3x3dbl_3abranch3x3dbl_3brPF avg_pool2d branch_pool)r%r*rU branch3x3 branch3x3dblr_rGr(r(r)rD]s      zQuantizableInceptionE._forwardcCrArB)rDrQrErFr(r(r)r.rrHzQuantizableInceptionE.forward) r2r3r4rr rlistrDr.r6r(r(r&r)rNUsrNcs*eZdZdededdffdd ZZS)QuantizableInceptionAuxrrrNcstj|dti|dSr:)rr rr$r&r(r)r ysz QuantizableInceptionAux.__init__)r2r3r4rr r6r(r(r&r)rcws"rccsTeZdZdededdffdd Zdedefdd Zd d ee ddfd d Z Z S)rrrrNc sDtj|dtttttttgi|t j j |_ t j j |_dS)Ninception_blocks)rr rr8rKrLrMrNrctorchao quantization QuantStubquant DeQuantStubdequantr$r&r(r)r ~s   zQuantizableInception3.__init__r*cCsf||}||}||\}}||}|jo|j}tjr-|s(t dt ||S| ||S)NzIScripted QuantizableInception3 always returns QuantizableInception3 Tuple) _transform_inputrirDrktraining aux_logitsrejit is_scriptingwarningswarnr eager_outputs)r%r*aux aux_definedr(r(r)r.s        zQuantizableInception3.forwardr/cCs(|D] }t|tur||qdS)aFuse conv/bn/relu modules in inception 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)modulestyperr1)r%r/mr(r(r)r1s   z QuantizableInception3.fuse_modelr) r2r3r4rr rr r.rr5r1r6r(r(r&r)r}s rc@sJeZdZedeedddddeddejd d d d id ddd dZ e Z dS)rzUhttps://download.pytorch.org/models/quantized/inception_v3_google_fbgemm-a2837893.pthi+iV) crop_size resize_sizeir)Kr{fbgemmzdhttps://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) r2r3r4rrr rr IMAGENET1K_V1IMAGENET1K_FBGEMM_V1DEFAULTr(r(r(r)rs* rquantized_inception_v3)name pretrainedcCs|ddr tjStjS)NquantizeF)getrrrr)rr(r(r)s r)weightsTF)rprogressrrrrrrcKs|rtnt|}|dd}|dur`__. .. 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: rnFNtransform_inputT num_classesrrr|)r check_hashr()rrverifyrrlenrpoprrrrn AuxLogitsload_state_dictget_state_dict)rrrroriginal_aux_logitsrmodelr(r(r)rs.4      r)7rq functoolsrtypingrrrretorch.nnr!torch.nn.functional functionalr]rtorchvision.modelsrinception_moduletorchvision.models.inceptionrr transforms._presetsr _apir rr_metar_utilsrrutilsrrr__all__ BasicConv2dr InceptionAr8 InceptionBrK InceptionCrL InceptionDrM InceptionErN InceptionAuxrc Inception3rrr5rr(r(r(r)sX          ",