o V hF@sddlmZddlmZmZmZmZmZddlZddl m Z ddlm Z ddl m Z mZmZmZmZmZmZddlmZdd lmZmZmZdd lmZdd lmZmZd d lm Z m!Z!m"Z"gdZ#Gddde Z$GdddeZ%GdddeZ&deee$e%fdee'deede(de(dede&fddZ)dedd d!d"Z*Gd#d$d$eZ+Gd%d&d&eZ,Gd'd(d(eZ-Gd)d*d*eZ.ed+d,ed-d.d/fd0dd1d2d3deee+efde(de(dede&f d4d5Z/ed6d,ed-d7d/fd0dd1d2d3deee,efde(de(dede&f d8d9Z0ed:d,ed-d;d/fd0dd1d2d3deee-efde(de(dede&f dd,ed-d?d/fd0dd1d2d3deee.efde(de(dede&f d@dAZ2dS)B)partial)AnyListOptionalTypeUnionN)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_64x4dcTeZdZdededdffdd Zdedefdd Zd d eeddfd d Z Z S)QuantizableBasicBlockargskwargsreturnNcs$tj|i|tjj|_dSN)super__init__torchnn quantizedFloatFunctionaladd_reluselfr(r) __class__Z/var/www/vscode/kcb/lib/python3.10/site-packages/torchvision/models/quantization/resnet.pyr-&szQuantizableBasicBlock.__init__xcCs\|}||}||}||}||}||}|jdur%||}|j||}|Sr+)conv1bn1reluconv2bn2 downsampler2r4r9identityoutr7r7r8forward*s       zQuantizableBasicBlock.forwardis_qatcCs@t|gdddgg|dd|jrt|jddg|dddSdS)Nr:r;r<r=r>Tinplace01rr?r4rDr7r7r8 fuse_model;sz QuantizableBasicBlock.fuse_modelr+ __name__ __module__ __qualname__rr-rrCrboolrL __classcell__r7r7r5r8r'%s r'cr&)QuantizableBottleneckr(r)r*Ncs>tj|i|tj|_tjdd|_tjdd|_dS)NFrF) r,r-r/r0r1 skip_add_reluReLUrelu1relu2r3r5r7r8r-Bs zQuantizableBottleneck.__init__r9cCsz|}||}||}||}||}||}||}||}||}|jdur4||}|j ||}|Sr+) r:r;rVr=r>rWconv3bn3r?rTr2r@r7r7r8rCHs          zQuantizableBottleneck.forwardrDcCsFt|gdgdddgg|dd|jr!t|jddg|dddSdS) N)r:r;rV)r=r>rWrXrYTrFrHrIrJrKr7r7r8rLZs z QuantizableBottleneck.fuse_modelr+rMr7r7r5r8rSAs rScr&)rr(r)r*Ncs2tj|i|tjj|_tjj|_dSr+) r,r-r.ao quantization QuantStubquant DeQuantStubdequantr3r5r7r8r-cszQuantizableResNet.__init__r9cCs"||}||}||}|Sr+)r] _forward_implr_)r4r9r7r7r8rCis   zQuantizableResNet.forwardrDcCsHt|gd|dd|D]}t|tust|tur!||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 rETrFN)rmodulestyperSr'rL)r4rDmr7r7r8rLrs   zQuantizableResNet.fuse_modelr+rMr7r7r5r8rbs rblocklayersweightsprogressquantizer)r*cKs|durt|dt|jdd|jvrt|d|jd|dd}t||fi|}t||r7t|||durE||j|dd|S)N num_classes categoriesbackendfbgemmT)rg check_hash) rlenmetapoprrrload_state_dictget_state_dict)rdrerfrgrhr)rkmodelr7r7r8_resnets   rt)rrrlzdhttps://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. )min_sizerjrkrecipe_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_sizeurl transformsroN) rNrOrPrrr _COMMON_METAr IMAGENET1K_V1IMAGENET1K_FBGEMM_V1DEFAULTr7r7r7r8rs& rc @~eZdZedeeddiedejddddid d d d Z ed eedddiedej ddddid dd d Z e Z dS)rzJhttps://download.pytorch.org/models/quantized/resnet50_fbgemm_bf931d71.pthrxryi(r{g{GR@gjt4W@r|gB`"[@gM8@r}rzJhttps://download.pytorch.org/models/quantized/resnet50_fbgemm-23753f79.pthrz resize_sizeg5^I T@gX9vW@g8@N) rNrOrPrrrrr rr IMAGENET1K_V2IMAGENET1K_FBGEMM_V2rr7r7r7r8rH  rc @r)r zQhttps://download.pytorch.org/models/quantized/resnext101_32x8_fbgemm_09835ccf.pthrxryi(Jr{gvS@gQW@r|gDli0@gV-U@r}rzQhttps://download.pytorch.org/models/quantized/resnext101_32x8_fbgemm-ee16d00c.pthrrg~jT@g rX@gzGU@N) rNrOrPrrrrrrrrrrr7r7r7r8r rr c @sJeZdZedeedddieddejddd d id d d dZ e Z dS)r!zRhttps://download.pytorch.org/models/quantized/resnext101_64x4d_fbgemm-605a1cb3.pthrxrri(mz+https://github.com/pytorch/vision/pull/5935r{gxT@g/X@r|gQ.@g$cT@)r~rvrrrrrN) rNrOrPrrrrrrrrr7r7r7r8r!s( r!quantized_resnet18)name pretrainedcC|ddr tjStjSNrhF)getrrr rr)r7r7r8 r)rfTF)rfrgrhcK.|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 verifyrtr'rfrgrhr)r7r7r8r"-r"quantized_resnet50cCrr)rrrr rrr7r7r8rQrcKr)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 rrtrSrr7r7r8r#Mrr#quantized_resnext101_32x8dcCrr)rr rrrrr7r7r8rrcKF|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)r rrrrtrSrr7r7r8r$-  r$quantized_resnext101_64x4dcCrr)rr!rrrrr7r7r8rrcKr)aResNeXt-101 64x4d model from `Aggregated Residual Transformation for Deep Neural Networks `_ .. note:: Note that ``quantize = True`` returns a quantized model with 8 bit weights. Quantized models only support inference and run on CPUs. GPU inference is not yet supported. Args: weights (:class:`~torchvision.models.quantization.ResNeXt101_64X4D_QuantizedWeights` or :class:`~torchvision.models.ResNeXt101_64X4D_Weights`, optional): The pretrained weights for the model. See :class:`~torchvision.models.quantization.ResNet101_64X4D_QuantizedWeights` below for more details, and possible values. By default, no pre-trained weights are used. progress (bool, optional): If True, displays a progress bar of the download to stderr. Default is True. quantize (bool, optional): If True, return a quantized version of the model. Default is False. **kwargs: parameters passed to the ``torchvision.models.quantization.QuantizableResNet`` base class. Please refer to the `source code `_ for more details about this class. .. autoclass:: torchvision.models.quantization.ResNeXt101_64X4D_QuantizedWeights :members: .. autoclass:: torchvision.models.ResNeXt101_64X4D_Weights :members: :noindex: r@rrr)r!rrrrtrSrr7r7r8r%rr%)3 functoolsrtypingrrrrrr.torch.nnr/rtorchvision.models.resnetr r r r r rrtransforms._presetsr_apirrr_metar_utilsrrutilsrrr__all__r'rSrintrQrtrrrr r!r"r#r$r%r7r7r7r8s   $   !  && ) ) +