jG6ddlmZddlmZmZmZmZmZddlZddl m Z ddlm Z ddl m Z mZmZmZmZmZmZddlmZdd lmZmZmZdd lmZdd lmZmZd d lm Z m!Z!m"Z"gdZ#Gdde Z$GddeZ%GddeZ&deee$e%fdee'deede(de(dede&fdZ)deddd d!Z*Gd"d#eZ+Gd$d%eZ,Gd&d'eZ-Gd(d)eZ.ed*+ed,d-f.dd/d0d1deee+efde(de(dede&f d2Z/ed3+ed,d4f.dd/d0d1deee,efde(de(dede&f d5Z0ed6+ed,d7f.dd/d0d1deee-efde(de(dede&f d8Z1ed9+ed,d:f.dd/d0d1deee.efde(de(dede&f d;Z2dS)<)partial)AnyListOptionalTypeUnionN)Tensor) BasicBlock BottleneckResNetResNet18_WeightsResNet50_WeightsResNeXt101_32X8D_WeightsResNeXt101_64X4D_Weights)ImageClassification)register_modelWeights WeightsEnum)_IMAGENET_CATEGORIES)_ovewrite_named_paramhandle_legacy_interface) _fuse_modules _replace_reluquantize_model) QuantizableResNetResNet18_QuantizedWeightsResNet50_QuantizedWeights!ResNeXt101_32X8D_QuantizedWeights!ResNeXt101_64X4D_QuantizedWeightsresnet18resnet50resnext101_32x8dresnext101_64x4dcZeZdZdededdffd ZdedefdZd deeddfd Z xZ S) QuantizableBasicBlockargskwargsreturnNctj|i|tjj|_dSN)super__init__torchnn quantizedFloatFunctionaladd_reluselfr)r* __class__s WC:\Projects\Historical\venv\Lib\site-packages\torchvision/models/quantization/resnet.pyr/zQuantizableBasicBlock.__init__&s;$)&)))*::<< xcJ|}||}||}||}||}||}|j||}|j||}|Sr-)conv1bn1reluconv2bn2 downsampler4r6r:identityouts r8forwardzQuantizableBasicBlock.forward*sjjmmhhsmmiinnjjoohhsmm ? &q))Hm$$S(33 r9is_qatc~t|gdddgg|d|jrt|jddg|ddSdS)Nr<r=r>r?r@Tinplace01rrAr6rFs r8 fuse_modelz QuantizableBasicBlock.fuse_model;sed5557GH&Z^____ ? M $/C:vt L L L L L L M Mr9r- __name__ __module__ __qualname__rr/r rErboolrO __classcell__r7s@r8r(r(%s=c=S=T======F"MM$M4MMMMMMMMr9r(cZeZdZdededdffd ZdedefdZd deeddfd Z xZ S) QuantizableBottleneckr)r*r+Nctj|i|tj|_tjd|_tjd|_dS)NFrI) r.r/r1r2r3 skip_add_reluReLUrelu1relu2r5s r8r/zQuantizableBottleneck.__init__Bsa$)&)))\99;;WU+++ WU+++ r9r:c|}||}||}||}||}||}||}||}||}|j||}|j ||}|Sr-) r<r=r\r?r@r]conv3bn3rArZr4rBs r8rEzQuantizableBottleneck.forwardHsjjmmhhsmmjjoojjoohhsmmjjoojjoohhsmm ? &q))H ))#x88 r9rFct|gdgdddgg|d|jrt|jddg|ddSdS) N)r<r=r\)r?r@r]r_r`TrIrKrLrMrNs r8rOz QuantizableBottleneck.fuse_modelZs{ ,,,.G.G.G'SXIYZ\blp     ? M $/C:vt L L L L L L M Mr9r-rPrVs@r8rXrXAs,c,S,T,,,,,, F$MM$M4MMMMMMMMr9rXcZeZdZdededdffd ZdedefdZd deeddfd Z xZ S) rr)r*r+Nctj|i|tjj|_tjj|_dSr-) r.r/r0ao quantization QuantStubquant DeQuantStubdequantr5s r8r/zQuantizableResNet.__init__csS$)&)))X*4466 x,88:: r9r:c||}||}||}|Sr-)rg _forward_implri)r6r:s r8rEzQuantizableResNet.forwardis: JJqMM   q ! ! LLOOr9rFct|gd|d|D]C}t|tust|tur||DdS)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 rHTrIN)rmodulestyperXr(rO)r6rFms r8rOzQuantizableResNet.fuse_modelrsy d444fdKKKK % %AAww///477>S3S3S V$$$ % %r9r-rPrVs@r8rrbs;c;S;T;;;;;; F % %$ %4 % % % % % % % %r9rblocklayersweightsprogressquantizer*r+c |Nt|dt|jdd|jvrt|d|jd|dd}t ||fi|}t ||rt |||*|||d|S)N num_classes categoriesbackendfbgemmT)rs check_hash) rlenmetapoprrrload_state_dictget_state_dict)rprqrrrsrtr*rxmodels r8_resnetrsfmSl9S5T5TUUU  $ $ !&)W\)5L M M MjjH--G eV 6 6v 6 6E%'ug&&& g44hSW4XXYYY Lr9)rrryzdhttps://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_sizerwrxrecipe_docsc leZdZedeediedejddddid 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/$?g`"y&@ num_params unquantized_metrics_ops _file_sizeurl transformsr|N) rQrRrSrrr _COMMON_METAr IMAGENET1K_V1IMAGENET1K_FBGEMM_V1DEFAULTr9r8rrs"7 X7.#>>>    "+9##        "#GGGr9rc eZdZedeediedejddddid d d  Z ed eeddiedej ddddid dd  Z e Z dS)r zJhttps://download.pytorch.org/models/quantized/resnet50_fbgemm_bf931d71.pthrri(rg{GR@gjt4W@rgB`"[@gM8@rrzJhttps://download.pytorch.org/models/quantized/resnet50_fbgemm-23753f79.pthr resize_sizeg5^I T@gX9vW@g8@N) rQrRrSrrrrrrr IMAGENET1K_V2IMAGENET1K_FBGEMM_V2rrr9r8r r s"7 X7.#>>>    "+9##        "#7 X7.#3OOO    "+9##        "#GGGr9r c eZdZedeediedejddddid d d  Z ed eeddiedej ddddid dd  Z e Z dS)r!zQhttps://download.pytorch.org/models/quantized/resnext101_32x8_fbgemm_09835ccf.pthrri(JrgvS@gQW@rgDli0@gV-U@rrzQhttps://download.pytorch.org/models/quantized/resnext101_32x8_fbgemm-ee16d00c.pthrrg~jT@g rX@gzGU@N) rQrRrSrrrrrrrrrrrr9r8r!r!s"7 _7.#>>>    "3A##        "#7 _7.#3OOO    "3A##        "#GGGr9r!c peZdZedeeddieddejddd d id d d Z e Z dS)r"zRhttps://download.pytorch.org/models/quantized/resnext101_64x4d_fbgemm-605a1cb3.pthrrri(mz+https://github.com/pytorch/vision/pull/5935rgxT@g/X@rgQ.@g$cT@)rrrrrrrN) rQrRrSrrrrrrrrrr9r8r"r"s"7 `7.#3OOO    "C3A##        $#GGGr9r"quantized_resnet18)name pretrainedc^|ddr tjn tjSNrtF)getrrr rr*s r8r, ::j% ( (,0EE  +r9)rrTF)rrrsrtc x|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 verifyrr(rrrsrtr*s r8r#r#IZ-5J((:JRRSZ[[G (,,,8 ^ ^W] ^ ^^r9quantized_resnet50c^|ddr tjn tjSr)rr rrrrs r8rrQrr9c x|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)r rrrrXrs r8r$r$Mrr9quantized_resnext101_32x8dc^|ddr tjn tjSr)rr!rrrrs r8rr, ::j% ( (48MM % 3r9c |rtnt|}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)r!rrrrrXrs r8r%r%nZ5=Z00BZbbcjkkG&(B///&"3Q777 (---(H _ _X^ _ __r9quantized_resnext101_64x4dc^|ddr tjn tjSr)rr"rrrrs r8rrrr9c |rtnt|}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"rrrrrXrs r8r&r&rr9)3 functoolsrtypingrrrrrr0torch.nnr1r torchvision.models.resnetr r r r rrrtransforms._presetsr_apirrr_metar_utilsrrutilsrrr__all__r(rXrintrTrrrr r!r"r#r$r%r&rr9r8rs633333333333333 7666667777777777((((((CCCCCCCC??????????   MMMMMJMMM8MMMMMJMMMB%%%%%%%%: +-BBC D Ik "     4&t   ##### ###*########## ######L########## ######L##### ###,)*** , , MQ &_&_&_ e57GGH I&_&_ &_  &_  &_&_&_+*&_R)*** , , MQ &_&_&_ e57GGH I&_&_ &_  &_  &_&_&_+*&_R1222 4 4 ]a (`(`(` e=?WWX Y(`(` (`  (`  (`(`(`32(`V1222 4 4 ]a (`(`(` e=?WWX Y(`(` (`  (`  (`(`(`32(`(`(`r9