hFZddlmZddlmZmZmZddlZddlmZddlm Z ddl m Z m Z m Z mZmZmZmZddlmZdd lmZmZmZdd lmZdd lmZmZd d lmZmZm Z gdZ!Gdde Z"Gdde Z#Gdde Z$de%ee"e#fde&e'deede(de(dede$fdZ)deddd d!Z*Gd"d#eZ+Gd$d%eZ,Gd&d'eZ-Gd(d)eZ.ed*+ed,d-f.dd/d0d1deee+efde(de(dede$f d2Z/ed3+ed,d4f.dd/d0d1deee,efde(de(dede$f d5Z0ed6+ed,d7f.dd/d0d1deee-efde(de(dede$f d8Z1ed9+ed,d:f.dd/d0d1deee.efde(de(dede$f d;Z2y)<)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_64x4dcTeZdZdededdffd ZdedefdZd deeddfd Z xZ S) QuantizableBasicBlockargskwargsreturnNc~t||i|tjjj |_yN)super__init__torchnn quantizedFloatFunctionaladd_reluselfr'r( __class__s d/var/www/html/eduruby.in/venv/lib/python3.12/site-packages/torchvision/models/quantization/resnet.pyr-zQuantizableBasicBlock.__init__&s/ $)&)**::< xc&|}|j|}|j|}|j|}|j|}|j |}|j |j |}|j j ||}|Sr+)conv1bn1reluconv2bn2 downsampler2r4r8identityouts r6forwardzQuantizableBasicBlock.forward*s{jjmhhsmiinjjohhsm ?? &q)Hmm$$S(3 r7is_qatc~t|gdddgg|d|jrt|jddg|dyy)Nr:r;r<r=r>Tinplace01rr?r4rDs r6 fuse_modelz QuantizableBasicBlock.fuse_model;s>d57GH&Z^_ ?? $//C:vt L r7r+ __name__ __module__ __qualname__rr-rrCrboolrM __classcell__r5s@r6r&r&%sJ=c=S=T=F"M$M4Mr7r&cTeZdZdededdffd ZdedefdZd deeddfd Z xZ S) QuantizableBottleneckr'r(r)Nct||i|tjj |_tj d|_tj d|_y)NFrG) r,r-r/r0r1 skip_add_reluReLUrelu1relu2r3s r6r-zQuantizableBottleneck.__init__BsJ $)&)\\99;WWU+ WWU+ r7r8c|}|j|}|j|}|j|}|j|}|j |}|j |}|j |}|j|}|j|j|}|jj||}|Sr+) r:r;rZr=r>r[conv3bn3r?rXr2r@s r6rCzQuantizableBottleneck.forwardHsjjmhhsmjjojjohhsmjjojjohhsm ?? &q)H  ))#x8 r7rDct|gdgdddgg|d|jrt|jddg|dyy) N)r:r;rZ)r=r>r[r]r^TrGrIrJrKrLs r6rMz QuantizableBottleneck.fuse_modelZsH ,.G'SXIYZ\blp  ?? $//C:vt L r7r+rNrTs@r6rVrVAsJ,c,S,T, F$M$M4Mr7rVcTeZdZdededdffd ZdedefdZd deeddfd Z xZ S) rr'r(r)Nct||i|tjjj |_tjjj|_yr+) r,r-r.ao quantization QuantStubquant DeQuantStubdequantr3s r6r-zQuantizableResNet.__init__csI $)&)XX**446 xx,,88: r7r8cl|j|}|j|}|j|}|Sr+)re _forward_implrg)r4r8s r6rCzQuantizableResNet.forwardis3 JJqM   q ! LLOr7rDct|gd|d|jD]6}t|tust|tus&|j |8y)aFuse conv/bn/relu modules in resnet models Fuse conv+bn+relu/ Conv+relu/conv+Bn modules to prepare for quantization. Model is modified in place. Note that this operation does not change numerics and the model after modification is in floating point rFTrGN)rmodulestyperVr&rM)r4rDms r6rMzQuantizableResNet.fuse_modelrsM d4fdK %AAw//47>S3S V$ %r7r+rNrTs@r6rrbsG;c;S;T; F %$ %4 %r7rblocklayersweightsprogressquantizer(r)c X|Kt|dt|jdd|jvrt|d|jd|jdd}t ||fi|}t ||r t |||"|j|j|d|S)N num_classes categoriesbackendfbgemmT)rq check_hash) rlenmetapoprrrload_state_dictget_state_dict)rnrorprqrrr(rvmodels r6_resnetrsfmSl9S5TU  $ !&)W\\)5L MjjH-G eV 6v 6E%ug& g44hSW4XY Lr7)rrrwzdhttps://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_sizerurvrecipe_docsc heZdZedeediedejddddid d d  Z e Z y )rzJhttps://download.pytorch.org/models/quantized/resnet18_fbgemm_16fa66dd.pth crop_sizei(^ ImageNet-1KgV-_Q@g r8V@zacc@1zacc@5g/$?g`"y&@ num_params unquantized_metrics_ops _file_sizeurl transformsrzN) rOrPrQrrr _COMMON_METAr IMAGENET1K_V1IMAGENET1K_FBGEMM_V1DEFAULTr7r6rrs[" X.#>    "+99##     "#Gr7rc eZdZedeediedejddddid d d  Z ed eeddiedejddddid dd  Z e Z y)rzJhttps://download.pytorch.org/models/quantized/resnet50_fbgemm_bf931d71.pthrri(rg{GR@gjt4W@rgB`"[@gM8@rrzJhttps://download.pytorch.org/models/quantized/resnet50_fbgemm-23753f79.pthr resize_sizeg5^I T@gX9vW@g8@N) rOrPrQrrrrr rr IMAGENET1K_V2IMAGENET1K_FBGEMM_V2rrr7r6rrs" X.#>    "+99##     "# X.#3O    "+99##     "#Gr7rc eZdZedeediedejddddid d d  Z ed eeddiedejddddid dd  Z e Z y)rzQhttps://download.pytorch.org/models/quantized/resnext101_32x8_fbgemm_09835ccf.pthrri(JrgvS@gQW@rgDli0@gV-U@rrzQhttps://download.pytorch.org/models/quantized/resnext101_32x8_fbgemm-ee16d00c.pthrrg~jT@g rX@gzGU@N) rOrPrQrrrrr rrrrrrr7r6rrs" _.#>    "3AA##     "# _.#3O    "3AA##     "#Gr7rc leZdZedeeddieddejddd d id d d Z e Z y)r zRhttps://download.pytorch.org/models/quantized/resnext101_64x4d_fbgemm-605a1cb3.pthrrri(mz+https://github.com/pytorch/vision/pull/5935rgxT@g/X@rgQ.@g$cT@)rrrrrrrN) rOrPrQrrrrrrrrrr7r6r r s`" `.#3O    "C3AA##     $#Gr7r quantized_resnet18)name pretrainedcf|jddrtjStjSNrrF)getrrr rr(s r6r0zz*e, & : :"//r7)rpTF)rprqrrc h|rtntj|}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 verifyrr&rprqrrr(s r6r!r!6^-5(:JRRSZ[G (,8 ^W] ^^r7quantized_resnet50cf|jddrtjStjSr)rrrr rrs r6rrSrr7c h|rtntj|}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 rrrVrs r6r"r"Orr7quantized_resnext101_32x8dcf|jddrtjStjSr)rrrr rrs r6rr0zz*e, . B B*77r7c |rtntj|}t|ddt|ddt t gd|||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 rrrrVrs r6r#r#O^5=0BZbbcjkG&(B/&"3Q7 (-(H _X^ __r7quantized_resnext101_64x4dcf|jddrtjStjSr)rr rrrrs r6rrrr7c |rtntj|}t|ddt|ddt t gd|||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 rrrrrVrs r6r$r$rr7)3 functoolsrtypingrrrr.torch.nnr/rtorchvision.models.resnetrr r r r r rtransforms._presetsr_apirrr_metar_utilsrrutilsrrr__all__r&rVrrllistintrRrrrrrr r!r"r#r$rr7r6rsA'' 777(C?? MJM8MJMB%%: +-BBC D Ik "     4&t  # #*## ##L## ##L# #,)*   MQ &_ e57GGH I&_&_ &_  &_  &_ +&_R)*   MQ &_ e57GGH I&_&_ &_  &_  &_ +&_R12   ]a (` e=?WWX Y(`(` (`  (`  (` 3(`V12   ]a (` e=?WWX Y(`(` (`  (`  (` 3(`r7