o V hA@sddlmZddlmZmZmZmZddlZddlm Z ddlm Z ddl m Z ddl mZdd lmZmZmZdd lmZdd lmZmZdd l mZmZmZmZd dlmZmZmZgdZ Gddde j!Z"Gddde j#Z$dee%dee%deede&de&dede$fddZ'deddd d!Z(Gd"d#d#eZ)Gd$d%d%eZ*Gd&d'd'eZ+Gd(d)d)eZ,ed*d+ed,d-d.fd/dd0d1d2deee)efde&de&dede$f d3d4Z-ed5d+ed,d6d.fd/dd0d1d2deee*efde&de&dede$f d7d8Z.ed9d+ed,d:d.fd/dd0d1d2deee+efde&de&dede$f d;d<Z/ed=d+ed,d>d.fd/dd0d1d2deee,efde&de&dede$f d?d@Z0dS)A)partial)AnyListOptionalUnionN)Tensor) shufflenetv2)ImageClassification)register_modelWeights WeightsEnum)_IMAGENET_CATEGORIES)_ovewrite_named_paramhandle_legacy_interface)ShuffleNet_V2_X0_5_WeightsShuffleNet_V2_X1_0_WeightsShuffleNet_V2_X1_5_WeightsShuffleNet_V2_X2_0_Weights) _fuse_modules _replace_reluquantize_model) QuantizableShuffleNetV2#ShuffleNet_V2_X0_5_QuantizedWeights#ShuffleNet_V2_X1_0_QuantizedWeights#ShuffleNet_V2_X1_5_QuantizedWeights#ShuffleNet_V2_X2_0_QuantizedWeightsshufflenet_v2_x0_5shufflenet_v2_x1_0shufflenet_v2_x1_5shufflenet_v2_x2_0cs<eZdZdededdffdd Zdedefdd ZZS) QuantizableInvertedResidualargskwargsreturnNcs"tj|i|tj|_dSN)super__init__nn quantizedFloatFunctionalcatselfr$r% __class__`/var/www/vscode/kcb/lib/python3.10/site-packages/torchvision/models/quantization/shufflenetv2.pyr)$sz$QuantizableInvertedResidual.__init__xcCsh|jdkr|jddd\}}|jj|||gdd}n|jj||||gdd}t|d}|S)Nrr )dim)stridechunkr-branch2branch1rchannel_shuffle)r/r4x1x2outr2r2r3forward(s   z#QuantizableInvertedResidual.forward)__name__ __module__ __qualname__rr)rr> __classcell__r2r2r0r3r##sr#csTeZdZdededdffdd Zdedefdd Zd d eeddfd d Z Z S)rr$r%r&Ncs6tj|dti|tjj|_tjj|_ dS)Ninverted_residual) r(r)r#torchao quantization QuantStubquant DeQuantStubdequantr.r0r2r3r)6sz QuantizableShuffleNetV2.__init__r4cCs"||}||}||}|Sr')rH _forward_implrJ)r/r4r2r2r3r>;s   zQuantizableShuffleNetV2.forwardis_qatcCs|jD]\}}|dvr|durt|gdg|ddq|D]3}t|turTt|jjdkrBt|jddggd g|ddt|jgdd d ggd g|ddq!dS) aBFuse conv/bn/relu modules in shufflenetv2 model Fuse conv+bn+relu/ conv+relu/conv+bn modules to prepare for quantization. Model is modified in place. .. note:: Note that this operation does not change numerics and the model after modification is in floating point )conv1conv5N)012T)inplacerrOrP)rQ34rSrT)567) _modulesitemsrmodulestyper#lenr9r8)r/rLnamemr2r2r3 fuse_modelAs    z"QuantizableShuffleNetV2.fuse_modelr') r?r@rArr)rr>rboolr_rBr2r2r0r3r4s rstages_repeatsstages_out_channelsweightsprogressquantizer%r&cKs|durt|dt|jdd|jvrt|d|jd|dd}t||fi|}t||r7t|||durE||j|dd|S)N num_classes categoriesbackendfbgemmT)rd check_hash) rr\metapoprrrload_state_dictget_state_dict)rarbrcrdrer%rhmodelr2r2r3 _shufflenetv2Zs    rp)rrrizdhttps://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_sizergrhrecipe_docsc @FeZdZedeeddiedejddddid d d d Z e Z d S)rzShttps://download.pytorch.org/models/quantized/shufflenetv2_x0.5_fbgemm-00845098.pth crop_sizei ImageNet-1Kg#~jL@gRS@zacc@1zacc@5g{Gz?gjt? num_params unquantized_metrics_ops _file_sizeurl transformsrkN) r?r@rAr rr _COMMON_METAr IMAGENET1K_V1IMAGENET1K_FBGEMM_V1DEFAULTr2r2r2r3r& rc @rt)rzQhttps://download.pytorch.org/models/quantized/shufflenetv2_x1_fbgemm-1e62bb32.pthrurvi"rxgףp= Q@gh|?U@ryg(\?gy&1@rzrN) r?r@rAr rr rrrrrr2r2r2r3rrrc @JeZdZedeedddieddejddd d id d d dZ e Z dS)rzShttps://download.pytorch.org/models/quantized/shufflenetv2_x1_5_fbgemm-d7401f05.pthrurw resize_size+https://github.com/pytorch/vision/pull/5906iv5rxgSR@g̬V@rygl?gK7A` @rrr{r|r}r~rrN) r?r@rAr rr rrrrrr2r2r2r3r( rc @r)rzShttps://download.pytorch.org/models/quantized/shufflenetv2_x2_0_fbgemm-5cac526c.pthrurrriprxg-R@gZd;W@ryg-?g|?5@rrN) r?r@rAr rr rrrrrr2r2r2r3rrrquantized_shufflenet_v2_x0_5)r] pretrainedcC|ddr tjStjSNreF)getrrrrr%r2r2r3 r)rcTFrcrdrecK4|rtnt|}tgdgdf|||d|S)aQ Constructs a ShuffleNetV2 with 0.5x output channels, as described in `ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design `__. .. 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.ShuffleNet_V2_X0_5_QuantizedWeights` or :class:`~torchvision.models.ShuffleNet_V2_X0_5_Weights`, optional): The pretrained weights for the model. See :class:`~torchvision.models.quantization.ShuffleNet_V2_X0_5_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.ShuffleNet_V2_X0_5_QuantizedWeights`` base class. Please refer to the `source code `_ for more details about this class. .. autoclass:: torchvision.models.quantization.ShuffleNet_V2_X0_5_QuantizedWeights :members: .. autoclass:: torchvision.models.ShuffleNet_V2_X0_5_Weights :members: :noindex: r)0`r)rrverifyrprcrdrer%r2r2r3r0 rquantized_shufflenet_v2_x1_0cCrr)rrrrrrr2r2r3rrcKr)aQ Constructs a ShuffleNetV2 with 1.0x output channels, as described in `ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design `__. .. 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.ShuffleNet_V2_X1_0_QuantizedWeights` or :class:`~torchvision.models.ShuffleNet_V2_X1_0_Weights`, optional): The pretrained weights for the model. See :class:`~torchvision.models.quantization.ShuffleNet_V2_X1_0_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.ShuffleNet_V2_X1_0_QuantizedWeights`` base class. Please refer to the `source code `_ for more details about this class. .. autoclass:: torchvision.models.quantization.ShuffleNet_V2_X1_0_QuantizedWeights :members: .. autoclass:: torchvision.models.ShuffleNet_V2_X1_0_Weights :members: :noindex: r)rtrirr)rrrrprr2r2r3r rr quantized_shufflenet_v2_x1_5cCrr)rrrrrrr2r2r3rFrcKr)aQ Constructs a ShuffleNetV2 with 1.5x output channels, as described in `ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design `__. .. 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.ShuffleNet_V2_X1_5_QuantizedWeights` or :class:`~torchvision.models.ShuffleNet_V2_X1_5_Weights`, optional): The pretrained weights for the model. See :class:`~torchvision.models.quantization.ShuffleNet_V2_X1_5_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.ShuffleNet_V2_X1_5_QuantizedWeights`` base class. Please refer to the `source code `_ for more details about this class. .. autoclass:: torchvision.models.quantization.ShuffleNet_V2_X1_5_QuantizedWeights :members: .. autoclass:: torchvision.models.ShuffleNet_V2_X1_5_Weights :members: :noindex: r)ri`irr)rrrrprr2r2r3r!Brr!quantized_shufflenet_v2_x2_0cCrr)rrrrrrr2r2r3r|rcKr)aQ Constructs a ShuffleNetV2 with 2.0x output channels, as described in `ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design `__. .. 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.ShuffleNet_V2_X2_0_QuantizedWeights` or :class:`~torchvision.models.ShuffleNet_V2_X2_0_Weights`, optional): The pretrained weights for the model. See :class:`~torchvision.models.quantization.ShuffleNet_V2_X2_0_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.ShuffleNet_V2_X2_0_QuantizedWeights`` base class. Please refer to the `source code `_ for more details about this class. .. autoclass:: torchvision.models.quantization.ShuffleNet_V2_X2_0_QuantizedWeights :members: .. autoclass:: torchvision.models.ShuffleNet_V2_X2_0_Weights :members: :noindex: r)riiir)rrrrprr2r2r3r"xrr")1 functoolsrtypingrrrrrDtorch.nnr*rtorchvision.modelsrtransforms._presetsr _apir r r_metar_utilsrrrrrrutilsrrr__all__InvertedResidualr# ShuffleNetV2rintr`rprrrrrrr r!r"r2r2r2r3s       &   - - -