a hF @sddlmZddlmZmZmZddlZddlmZddlm Z ddl m Z m Z m Z mZmZmZmZddlmZdd lmZmZmZdd lmZdd lmZmZd d lmZmZm Z gdZ!Gddde Z"Gddde Z#Gddde Z$e%ee"e#fe&e'eee(e(ee$dddZ)deddddZ*GdddeZ+Gdd d eZ,Gd!d"d"eZ-Gd#d$d$eZ.ed%d&ed'd(d)fd*dd+d,d-eee+efe(e(ee$d.d/d0Z/ed1d&ed'd2d)fd*dd+d,d-eee,efe(e(ee$d.d3d4Z0ed5d&ed'd6d)fd*dd+d,d-eee-efe(e(ee$d.d7d8Z1ed9d&ed'd:d)fd*dd+d,d-eee.efe(e(ee$d.d;d<Z2dS)=)partial)AnyOptionalUnionN)Tensor) BasicBlock BottleneckResNetResNet18_WeightsResNet50_WeightsResNeXt101_32X8D_WeightsResNeXt101_64X4D_Weights)ImageClassification)register_modelWeights WeightsEnum)_IMAGENET_CATEGORIES)_ovewrite_named_paramhandle_legacy_interface) _fuse_modules _replace_reluquantize_model) QuantizableResNetResNet18_QuantizedWeightsResNet50_QuantizedWeights!ResNeXt101_32X8D_QuantizedWeights!ResNeXt101_64X4D_QuantizedWeightsresnet18resnet50resnext101_32x8dresnext101_64x4dcsLeZdZeeddfdd ZeedddZd eeddd d Z Z S) QuantizableBasicBlockNargskwargsreturncs$tj|i|tjj|_dSN)super__init__torchnn quantizedFloatFunctionaladd_reluselfr&r' __class__T/var/www/auris/lib/python3.9/site-packages/torchvision/models/quantization/resnet.pyr+&szQuantizableBasicBlock.__init__xr(cCs\|}||}||}||}||}||}|jdurJ||}|j||}|Sr))conv1bn1reluconv2bn2 downsampler0r2r8identityoutr5r5r6forward*s       zQuantizableBasicBlock.forwardis_qatr(cCs<t|gdddgg|dd|jr8t|jddg|dddS)Nr9r:r;r<r=TZinplace01rr>r2rDr5r5r6 fuse_model;sz QuantizableBasicBlock.fuse_model)N __name__ __module__ __qualname__rr+rrBrboolrK __classcell__r5r5r3r6r$%sr$csLeZdZeeddfdd ZeedddZd eeddd d Z Z S) QuantizableBottleneckNr%cs>tj|i|tj|_tjdd|_tjdd|_dS)NFrF) r*r+r-r.r/ skip_add_reluZReLUrelu1relu2r1r3r5r6r+Bs zQuantizableBottleneck.__init__r7cCsz|}||}||}||}||}||}||}||}||}|jdurh||}|j ||}|Sr)) r9r:rTr<r=rUconv3bn3r>rSr0r?r5r5r6rBHs          zQuantizableBottleneck.forwardrCcCsBt|gdgdddgg|dd|jr>t|jddg|dddS) N)r9r:rT)r<r=rUrVrWTrFrGrHrIrJr5r5r6rKZs z QuantizableBottleneck.fuse_model)NrLr5r5r3r6rRAsrRcsLeZdZeeddfdd ZeedddZd eeddd d Z Z S) rNr%cs2tj|i|tjj|_tjj|_dSr)) r*r+r,ZaoZ quantizationZ QuantStubquantZ DeQuantStubdequantr1r3r5r6r+cszQuantizableResNet.__init__r7cCs"||}||}||}|Sr))rXZ _forward_implrY)r2r8r5r5r6rBis   zQuantizableResNet.forwardrCcCsHt|gd|dd|D]&}t|tus8t|tur||qdS)aFuse conv/bn/relu modules in resnet models Fuse conv+bn+relu/ Conv+relu/conv+Bn modules to prepare for quantization. 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Note that this operation does not change numerics and the model after modification is in floating point rETrFN)rmodulestyperRr$rK)r2rDmr5r5r6rKrs zQuantizableResNet.fuse_model)NrLr5r5r3r6rbs r)blocklayersweightsprogressquantizer'r(cKs|dur:t|dt|jdd|jvr:t|d|jd|dd}t||fi|}t||rnt|||dur||j|dd|S)NZ num_classes categoriesbackendfbgemmT)r`Z check_hash) rlenmetapoprrrZload_state_dictZget_state_dict)r]r^r_r`rar'rcmodelr5r5r6_resnets   ri)rrrdzdhttps://github.com/pytorch/vision/tree/main/references/classification#post-training-quantized-modelsz These weights were produced by doing Post Training Quantization (eager mode) on top of the unquantized weights listed below. )Zmin_sizerbrcrecipeZ_docsc @sFeZdZedeeddiedejddddid d d d Z e Z d S)rzJhttps://download.pytorch.org/models/quantized/resnet18_fbgemm_16fa66dd.pth crop_sizei(^ ImageNet-1KgV-_Q@g r8V@zacc@1zacc@5g/$?g`"y&@ num_params unquantized_metrics_ops _file_sizeurlZ transformsrfN) rMrNrOrrr _COMMON_METAr IMAGENET1K_V1IMAGENET1K_FBGEMM_V1DEFAULTr5r5r5r6rs$ rc @s~eZdZedeeddiedejddddid d d d Z ed eedddiedej ddddid dd d Z e Z dS)rzJhttps://download.pytorch.org/models/quantized/resnet50_fbgemm_bf931d71.pthrkrli(rng{GR@gjt4W@rogB`"[@gM8@rprvzJhttps://download.pytorch.org/models/quantized/resnet50_fbgemm-23753f79.pthrmZ resize_sizeg5^I T@gX9vW@g8@N) rMrNrOrrrrxr ryrz IMAGENET1K_V2IMAGENET1K_FBGEMM_V2r{r5r5r5r6rsF  rc @s~eZdZedeeddiedejddddid d d d Z ed eedddiedej ddddid dd d Z e Z dS)rzQhttps://download.pytorch.org/models/quantized/resnext101_32x8_fbgemm_09835ccf.pthrkrli(JrngvS@gQW@rogDli0@gV-U@rprvzQhttps://download.pytorch.org/models/quantized/resnext101_32x8_fbgemm-ee16d00c.pthr|r}g~jT@g rX@gzGU@N) rMrNrOrrrrxr ryrzr~rr{r5r5r5r6rsF  rc @sJeZdZedeedddieddejddd d id d d dZ e Z dS)rzRhttps://download.pytorch.org/models/quantized/resnext101_64x4d_fbgemm-605a1cb3.pthrkr|r}i(mz+https://github.com/pytorch/vision/pull/5935rngxT@g/X@rogQ.@g$cT@)rqrjrrrsrtrurvN) rMrNrOrrrrxr ryrzr{r5r5r5r6rs& rZquantized_resnet18)nameZ pretrainedcCs|ddrtjStjSNraF)getrrzr ryr'r5r5r6s r)r_TF)r_r`ra)r_r`rar'r(cKs.|rtnt|}ttgd|||fi|S)aResNet-18 model from `Deep Residual Learning for Image Recognition `_ .. note:: Note that ``quantize = True`` returns a quantized model with 8 bit weights. Quantized models only support inference and run on CPUs. GPU inference is not yet supported. Args: weights (:class:`~torchvision.models.quantization.ResNet18_QuantizedWeights` or :class:`~torchvision.models.ResNet18_Weights`, optional): The pretrained weights for the model. See :class:`~torchvision.models.quantization.ResNet18_QuantizedWeights` below for more details, and possible values. By default, no pre-trained weights are used. progress (bool, optional): If True, displays a progress bar of the download to stderr. Default is True. quantize (bool, optional): If True, return a quantized version of the model. Default is False. **kwargs: parameters passed to the ``torchvision.models.quantization.QuantizableResNet`` base class. Please refer to the `source code `_ for more details about this class. .. autoclass:: torchvision.models.quantization.ResNet18_QuantizedWeights :members: .. autoclass:: torchvision.models.ResNet18_Weights :members: :noindex: )rrrr)rr verifyrir$r_r`rar'r5r5r6r s/r Zquantized_resnet50cCs|ddrtjStjSr)rrrzr ryrr5r5r6rSs cKs.|rtnt|}ttgd|||fi|S)aResNet-50 model from `Deep Residual Learning for Image Recognition `_ .. 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.ResNet50_QuantizedWeights` or :class:`~torchvision.models.ResNet50_Weights`, optional): The pretrained weights for the model. See :class:`~torchvision.models.quantization.ResNet50_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.QuantizableResNet`` base class. Please refer to the `source code `_ for more details about this class. .. autoclass:: torchvision.models.quantization.ResNet50_QuantizedWeights :members: .. autoclass:: torchvision.models.ResNet50_Weights :members: :noindex: )rr)rr rrirRrr5r5r6r!Os/r!Zquantized_resnext101_32x8dcCs|ddrtjStjSr)rrrzr ryrr5r5r6rs cKsF|rtnt|}t|ddt|ddttgd|||fi|S)aResNeXt-101 32x8d model from `Aggregated Residual Transformation for Deep Neural Networks `_ .. 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.ResNeXt101_32X8D_QuantizedWeights` or :class:`~torchvision.models.ResNeXt101_32X8D_Weights`, optional): The pretrained weights for the model. See :class:`~torchvision.models.quantization.ResNet101_32X8D_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.QuantizableResNet`` base class. Please refer to the `source code `_ for more details about this class. .. autoclass:: torchvision.models.quantization.ResNeXt101_32X8D_QuantizedWeights :members: .. autoclass:: torchvision.models.ResNeXt101_32X8D_Weights :members: :noindex: groups width_per_grouprrr)rr rrrirRrr5r5r6r"s/  r"Zquantized_resnext101_64x4dcCs|ddrtjStjSr)rrrzr ryrr5r5r6rs cKsF|rtnt|}t|ddt|ddttgd|||fi|S)aResNeXt-101 64x4d model from `Aggregated Residual Transformation for Deep Neural Networks `_ .. 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.ResNeXt101_64X4D_QuantizedWeights` or :class:`~torchvision.models.ResNeXt101_64X4D_Weights`, optional): The pretrained weights for the model. See :class:`~torchvision.models.quantization.ResNet101_64X4D_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.QuantizableResNet`` base class. Please refer to the `source code `_ for more details about this class. .. autoclass:: torchvision.models.quantization.ResNeXt101_64X4D_QuantizedWeights :members: .. autoclass:: torchvision.models.ResNeXt101_64X4D_Weights :members: :noindex: r@rrr)rr rrrirRrr5r5r6r#s/  r#)3 functoolsrtypingrrrr,Ztorch.nnr-rZtorchvision.models.resnetrrr r r r r Ztransforms._presetsrZ_apirrrZ_metar_utilsrrutilsrrr__all__r$rRrr[listintrPrirxrrrrr r!r"r#r5r5r5r6s   $   !  && ) ) +