U (dD @sddlmZddlmZmZmZmZmZddlZddl m Z ddlm Z ddl m Z mZmZmZmZmZmZddlmZdd lmZmZdd lmZdd lmZmZd d lmZm Z m!Z!dddddddddg Z"GdddeZ#Gddde Z$GdddeZ%eee#e$fee&eee'e'ee%dddZ(ded d!d"d#Z)Gd$ddeZ*Gd%ddeZ+Gd&ddeZ,Gd'ddeZ-ed(d)d*fd+dd,d-d.eee*efe'e'ee%d/d0dZ.ed(d1d*fd+dd,d-d.eee+efe'e'ee%d/d2dZ/ed(d3d*fd+dd,d-d.eee,efe'e'ee%d/d4dZ0dd,d-d.eee-efe'e'ee%d/d5dZ1dd6lm2Z2dd7l3m4Z4e2e*j5j6e+j5j6e,j5j6d8Z7dS)9)partial)AnyTypeUnionListOptionalN)Tensor) Bottleneck BasicBlockResNetResNet18_WeightsResNet50_WeightsResNeXt101_32X8D_WeightsResNeXt101_64X4D_Weights)ImageClassification) WeightsEnumWeights)_IMAGENET_CATEGORIES)handle_legacy_interface_ovewrite_named_param) _fuse_modules _replace_reluquantize_modelQuantizableResNetResNet18_QuantizedWeightsResNet50_QuantizedWeights!ResNeXt101_32X8D_QuantizedWeights!ResNeXt101_64X4D_QuantizedWeightsresnet18resnet50resnext101_32x8dresnext101_64x4dcsLeZdZeeddfdd ZeedddZd eeddd d Z Z S) QuantizableBasicBlockNargskwargsreturncs tj||tjj|_dSN)super__init__torchnn quantizedFloatFunctionaladd_reluselfr'r( __class__J/tmp/pip-unpacked-wheel-vx7f76es/torchvision/models/quantization/resnet.pyr,&szQuantizableBasicBlock.__init__xr)cCs\|}||}||}||}||}||}|jdk rJ||}|j||}|Sr*)conv1bn1reluconv2bn2 downsampler1r3r9identityoutr6r6r7forward*s       zQuantizableBasicBlock.forwardis_qatr)cCs>t|dddgddgg|dd|jr:t|jdd g|dddS) Nr:r;r<r=r>TZinplace01rr?r3rEr6r6r7 fuse_model;sz QuantizableBasicBlock.fuse_model)N __name__ __module__ __qualname__rr,rrCrboolrK __classcell__r6r6r4r7r%%sr%csLeZdZeeddfdd ZeedddZd eeddd d Z Z S) QuantizableBottleneckNr&cs:tj||tj|_tjdd|_tjdd|_dS)NFrF) r+r,r.r/r0 skip_add_reluZReLUrelu1relu2r2r4r6r7r,Bs zQuantizableBottleneck.__init__r8cCsz|}||}||}||}||}||}||}||}||}|jdk rh||}|j ||}|Sr*) r:r;rTr=r>rUconv3bn3r?rSr1r@r6r6r7rCHs          zQuantizableBottleneck.forwardrDcCsFt|dddgdddgddgg|d d |jrBt|jd d g|d d dS) Nr:r;rTr=r>rUrVrWTrFrGrHrIrJr6r6r7rKZsz QuantizableBottleneck.fuse_model)NrLr6r6r4r7rRAsrRcsLeZdZeeddfdd ZeedddZd eeddd d Z Z S) rNr&cs.tj||tjj|_tjj|_dSr*) r+r,r-ZaoZ quantizationZ QuantStubquantZ DeQuantStubdequantr2r4r6r7r,cszQuantizableResNet.__init__r8cCs"||}||}||}|Sr*)rXZ _forward_implrY)r3r9r6r6r7rCis   zQuantizableResNet.forwardrDcCsJt|dddg|dd|D]&}t|tks:t|tkr||qdS)aFuse conv/bn/relu modules in resnet models 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 r:r;r<TrFN)rmodulestyperRr%rK)r3rEmr6r6r7rKrs zQuantizableResNet.fuse_model)NrLr6r6r4r7rbs )blocklayersweightsprogressquantizer(r)cKs|dk r:t|dt|jdd|jkr:t|d|jd|dd}t||f|}t||rjt|||dk r||j|d|S)NZ num_classes categoriesbackendfbgemm)r`) rlenmetapoprrrZload_state_dictZget_state_dict)r]r^r_r`rar(rcmodelr6r6r7_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 @s>eZdZedeeddedejddddid 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@5 num_params unquantized_metricsurlZ transformsrfN) rMrNrOrrr _COMMON_METAr IMAGENET1K_V1IMAGENET1K_FBGEMM_V1DEFAULTr6r6r6r7rs c @sneZdZedeeddedejddddid d Z ed eedd d edej ddddid d Z e Z dS)rzJhttps://download.pytorch.org/models/quantized/resnet50_fbgemm_bf931d71.pthrkrli(rng{GR@gjt4W@rorprtzJhttps://download.pytorch.org/models/quantized/resnet50_fbgemm-23753f79.pthrmZ resize_sizeg5^I T@gX9vW@N) rMrNrOrrrrvr rwrx IMAGENET1K_V2IMAGENET1K_FBGEMM_V2ryr6r6r6r7rs6  c @sneZdZedeeddedejddddid d Z ed eedd d edej ddddid d Z e Z dS)rzQhttps://download.pytorch.org/models/quantized/resnext101_32x8_fbgemm_09835ccf.pthrkrli(JrngvS@gQW@rorprtzQhttps://download.pytorch.org/models/quantized/resnext101_32x8_fbgemm-ee16d00c.pthrzr{g~jT@g rX@N) rMrNrOrrrrvrrwrxr|r}ryr6r6r6r7rs6  c @sBeZdZedeedddeddejddd d id d Z e Z d S)r zRhttps://download.pytorch.org/models/quantized/resnext101_64x4d_fbgemm-605a1cb3.pthrkrzr{i(mz+https://github.com/pytorch/vision/pull/5935rngxT@g/X@ro)rqrjrrrsrtN) rMrNrOrrrrvrrwrxryr6r6r6r7r s Z pretrainedcCs|ddrtjStjSNraF)getrrxr rwr(r6r6r7s r)r_TF)r_r`ra)r_r`rar(r)cKs.|rtnt|}ttddddg|||f|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: r)rr verifyrir%r_r`rar(r6r6r7r!s,cCs|ddrtjStjSr~)rrrxr rwrr6r6r7rCs cKs.|rtnt|}ttddddg|||f|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: r)rr rrirRrr6r6r7r"@s,cCs|ddrtjStjSr~)rrrxrrwrr6r6r7rts cKsF|rtnt|}t|ddt|ddttddddg|||f|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_grouprr)rrrrrirRrr6r6r7r#qs,  cKsF|rtnt|}t|ddt|ddttddddg|||f|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@rrrr)r rrrrirRrr6r6r7r$s$  ) _ModelURLs) model_urls)Zresnet18_fbgemmZresnet50_fbgemmZresnext101_32x8d_fbgemm)8 functoolsrtypingrrrrrr-Ztorch.nnr.rZtorchvision.models.resnetr r r r r rrZtransforms._presetsrZ_apirrZ_metar_utilsrrutilsrrr__all__r%rRrintrPrirvrrrr r!r"r#r$rZresnetrrxruZquant_model_urlsr6r6r6r7s   $   !  "" ) ) - ,