a PSicD@sddlmZddlmZmZmZmZmZddlZddl m Z ddlm Z ddl m Z mZmZmZmZmZmZddlmZdd lmZmZdd lmZdd lmZmZd d lmZm Z m!Z!gdZ"GdddeZ#Gddde Z$GdddeZ%eee#e$fee&eee'e'ee%dddZ(deddddZ)GdddeZ*Gdd d eZ+Gd!d"d"eZ,Gd#d$d$eZ-ed%d&d'fd(dd)d*d+eee*efe'e'ee%d,d-d.Z.ed%d/d'fd(dd)d*d+eee+efe'e'ee%d,d0d1Z/ed%d2d'fd(dd)d*d+eee,efe'e'ee%d,d3d4Z0dd)d*d+eee-efe'e'ee%d,d5d6Z1dd7lm2Z2dd8l3m4Z4e2e*j5j6e+j5j6e,j5j6d9Z7dS):)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_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__b/var/www/html/django/DPS/env/lib/python3.9/site-packages/torchvision/models/quantization/resnet.pyr,&szQuantizableBasicBlock.__init__xr)cCs\|}||}||}||}||}||}|jdurJ||}|j||}|Sr*)conv1bn1reluconv2bn2 downsampler1r3r9identityoutr6r6r7forward*s       zQuantizableBasicBlock.forwardis_qatr)cCs<t|gdddgg|dd|jr8t|jddg|dddS)Nr:r;r<r=r>Tinplace01rr?r3rEr6r6r7 fuse_model;sz QuantizableBasicBlock.fuse_model)N __name__ __module__ __qualname__rr,rrCrboolrM __classcell__r6r6r4r7r%%sr%csLeZdZeeddfdd ZeedddZd eeddd d Z Z S) QuantizableBottleneckNr&cs>tj|i|tj|_tjdd|_tjdd|_dS)NFrG) r+r,r.r/r0 skip_add_reluReLUrelu1relu2r2r4r6r7r,Bs zQuantizableBottleneck.__init__r8cCsz|}||}||}||}||}||}||}||}||}|jdurh||}|j ||}|Sr*) r:r;rWr=r>rXconv3bn3r?rUr1r@r6r6r7rCHs          zQuantizableBottleneck.forwardrDcCsBt|gdgdddgg|dd|jr>t|jddg|dddS) N)r:r;rW)r=r>rXrYrZTrGrIrJrKrLr6r6r7rMZs z QuantizableBottleneck.fuse_model)NrNr6r6r4r7rTAsrTcsLeZdZeeddfdd ZeedddZd eeddd d Z Z S) rNr&cs2tj|i|tjj|_tjj|_dSr*) r+r,r-ao quantization QuantStubquant DeQuantStubdequantr2r4r6r7r,cszQuantizableResNet.__init__r8cCs"||}||}||}|Sr*)r^ _forward_implr`)r3r9r6r6r7rCis   zQuantizableResNet.forwardrDcCsHt|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. Model is modified in place. Note that this operation does not change numerics and the model after modification is in floating point rFTrGN)rmodulestyperTr%rM)r3rEmr6r6r7rMrs zQuantizableResNet.fuse_model)NrNr6r6r4r7rbs r)blocklayersweightsprogressquantizer(r)cKs|dur:t|dt|jdd|jvr:t|d|jd|dd}t||fi|}t||rnt|||dur||j|d|S)N num_classes categoriesbackendfbgemm)rh) rlenmetapoprrrload_state_dictget_state_dict)rerfrgrhrir(rlmodelr6r6r7_resnets   rt)rrrmzdhttps://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_sizerkrlrecipe_docsc @sBeZdZedeeddiedejddddid 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_metricsurl transformsroN) rOrPrQrrr _COMMON_METAr IMAGENET1K_V1IMAGENET1K_FBGEMM_V1DEFAULTr6r6r6r7rs  rc @sveZdZedeeddiedejddddid d Z ed eedd d iedej ddddid d Z e Z dS)rzJhttps://download.pytorch.org/models/quantized/resnet50_fbgemm_bf931d71.pthrxryi(r{g{GR@gjt4W@r|r}rzJhttps://download.pytorch.org/models/quantized/resnet50_fbgemm-23753f79.pthrz resize_sizeg5^I T@gX9vW@N) rOrPrQrrrrr rr IMAGENET1K_V2IMAGENET1K_FBGEMM_V2rr6r6r6r7rs>  rc @sveZdZedeeddiedejddddid d Z ed eedd d iedej ddddid d Z e Z dS)rzQhttps://download.pytorch.org/models/quantized/resnext101_32x8_fbgemm_09835ccf.pthrxryi(Jr{gvS@gQW@r|r}rzQhttps://download.pytorch.org/models/quantized/resnext101_32x8_fbgemm-ee16d00c.pthrrg~jT@g rX@N) rOrPrQrrrrrrrrrrr6r6r6r7rs>  rc @sFeZdZedeedddieddejddd d id d Z e Z d S)r zRhttps://download.pytorch.org/models/quantized/resnext101_64x4d_fbgemm-605a1cb3.pthrxrri(mz+https://github.com/pytorch/vision/pull/5935r{gxT@g/X@r|)r~rvrrrN) rOrPrQrrrrrrrrr6r6r6r7r s" r pretrainedcCs|ddrtjStjSNriF)getrrr rr(r6r6r7s r)rgTF)rgrhri)rgrhrir(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 verifyrtr%rgrhrir(r6r6r7r!s,r!cCs|ddrtjStjSr)rrrr rrr6r6r7rCs 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 rrtrTrr6r6r7r"@s,r"cCs|ddrtjStjSr)rrrrrrr6r6r7rts 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)rrrrrtrTrr6r6r7r#qs,  r#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)r rrrrtrTrr6r6r7r$s$  r$) _ModelURLs) model_urls)Zresnet18_fbgemmZresnet50_fbgemmZresnext101_32x8d_fbgemm)8 functoolsrtypingrrrrrr-torch.nnr.rZtorchvision.models.resnetr r r r r rrZtransforms._presetsr_apirr_metar_utilsrrutilsrrr__all__r%rTrintrRrtrrrrr r!r"r#r$rresnetrrrquant_model_urlsr6r6r6r7s   $   !  "" ) ) - ,