o ,hnB@sddlmZddlmZmZmZddlZddlmZddlm Z ddl m Z ddl m Z dd lmZmZmZdd lmZdd lmZmZdd l mZmZmZmZd dlmZmZmZgdZGddde j Z!Gddde j"Z#de$e%de$e%deede&de&dede#fddZ'deddd d!Z(Gd"d#d#eZ)Gd$d%d%eZ*Gd&d'd'eZ+Gd(d)d)eZ,ed*d+ed,d-d.fd/dd0d1d2deee)efde&de&dede#f d3d4Z-ed5d+ed,d6d.fd/dd0d1d2deee*efde&de&dede#f d7d8Z.ed9d+ed,d:d.fd/dd0d1d2deee+efde&de&dede#f d;d<Z/ed=d+ed,d>d.fd/dd0d1d2deee,efde&de&dede#f d?d@Z0dS)A)partial)AnyOptionalUnionN)Tensor) shufflenetv2)ImageClassification)register_modelWeights WeightsEnum)_IMAGENET_CATEGORIES)_ovewrite_named_paramhandle_legacy_interface)ShuffleNet_V2_X0_5_WeightsShuffleNet_V2_X1_0_WeightsShuffleNet_V2_X1_5_WeightsShuffleNet_V2_X2_0_Weights) _fuse_modules _replace_reluquantize_model) QuantizableShuffleNetV2#ShuffleNet_V2_X0_5_QuantizedWeights#ShuffleNet_V2_X1_0_QuantizedWeights#ShuffleNet_V2_X1_5_QuantizedWeights#ShuffleNet_V2_X2_0_QuantizedWeightsshufflenet_v2_x0_5shufflenet_v2_x1_0shufflenet_v2_x1_5shufflenet_v2_x2_0cs<eZdZdededdffdd Zdedefdd ZZS) QuantizableInvertedResidualargskwargsreturnNcs"tj|i|tj|_dSN)super__init__nn quantizedFloatFunctionalcatselfr#r$ __class__g/var/www/html/scripts/venv/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.r3x1x2outr1r1r2forward(s   z#QuantizableInvertedResidual.forward)__name__ __module__ __qualname__rr(rr= __classcell__r1r1r/r2r"#sr"csTeZdZdededdffdd Zdedefdd Zd d eeddfd d Z Z S)rr#r$r%Ncs6tj|dti|tjj|_tjj|_ dS)Ninverted_residual) r'r(r"torchao quantization QuantStubquant DeQuantStubdequantr-r/r1r2r(6sz QuantizableShuffleNetV2.__init__r3cCs"||}||}||}|Sr&)rG _forward_implrI)r.r3r1r1r2r=;s   zQuantizableShuffleNetV2.forwardis_qatcCs|jD]\}}|dvr|durt|gdg|ddq|D]3}t|turTt|jjdkrBt|jddggd g|ddt|jgdd d ggd g|ddq!dS) aBFuse conv/bn/relu modules in shufflenetv2 model Fuse conv+bn+relu/ conv+relu/conv+bn modules to prepare for quantization. Model is modified in place. .. note:: Note that this operation does not change numerics and the model after modification is in floating point )conv1conv5N)012T)inplacerrNrO)rP34rRrS)567) _modulesitemsrmodulestyper"lenr8r7)r.rKnamemr1r1r2 fuse_modelAs    z"QuantizableShuffleNetV2.fuse_modelr&) r>r?r@rr(rr=rboolr^rAr1r1r/r2r4s rstages_repeatsstages_out_channelsweightsprogressquantizer$r%cKs|durt|dt|jdd|jvrt|d|jd|dd}t||fi|}t||r7t|||durE||j|dd|S)N num_classes categoriesbackendfbgemmT)rc check_hash) rr[metapoprrrload_state_dictget_state_dict)r`rarbrcrdr$rgmodelr1r1r2 _shufflenetv2Zs    ro)rrrhzdhttps://github.com/pytorch/vision/tree/main/references/classification#post-training-quantized-modelsz These weights were produced by doing Post Training Quantization (eager mode) on top of the unquantized weights listed below. )min_sizerfrgrecipe_docsc @FeZdZedeeddiedejddddid d d d Z e Z d S)rzShttps://download.pytorch.org/models/quantized/shufflenetv2_x0.5_fbgemm-00845098.pth crop_sizei ImageNet-1Kg#~jL@gRS@zacc@1zacc@5g{Gz?gjt? num_params unquantized_metrics_ops _file_sizeurl transformsrjN) r>r?r@r rr _COMMON_METAr IMAGENET1K_V1IMAGENET1K_FBGEMM_V1DEFAULTr1r1r1r2r& rc @rs)rzQhttps://download.pytorch.org/models/quantized/shufflenetv2_x1_fbgemm-1e62bb32.pthrtrui"rwgףp= Q@gh|?U@rxg(\?gy&1@ryrN) r>r?r@r rr rrrrrr1r1r1r2rrrc @JeZdZedeedddieddejddd d id d d dZ e Z dS)rzShttps://download.pytorch.org/models/quantized/shufflenetv2_x1_5_fbgemm-d7401f05.pthrtrv resize_size+https://github.com/pytorch/vision/pull/5906iv5rwgSR@g̬V@rxgl?gK7A` @rqrzr{r|r}r~rN) r>r?r@r rr rrrrrr1r1r1r2r( rc @r)rzShttps://download.pytorch.org/models/quantized/shufflenetv2_x2_0_fbgemm-5cac526c.pthrtrrriprwg-R@gZd;W@rxg-?g|?5@rrN) r>r?r@r rr rrrrrr1r1r1r2rrrquantized_shufflenet_v2_x0_5)r\ pretrainedcC|ddr tjStjSNrdF)getrrrrr$r1r1r2 r)rbTFrbrcrdcK4|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)rrverifyrorbrcrdr$r1r1r2r2 rquantized_shufflenet_v2_x1_0cCrr)rrrrrrr1r1r2rrcKr)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)rrrrorr1r1r2rrrquantized_shufflenet_v2_x1_5cCrr)rrrrrrr1r1r2rJrcKr)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)rrrrorr1r1r2r Frr quantized_shufflenet_v2_x2_0cCrr)rrrrrrr1r1r2rrcKr)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)rrrrorr1r1r2r!~rr!)1 functoolsrtypingrrrrCtorch.nnr)rtorchvision.modelsrtransforms._presetsr _apir r r _metar_utilsrrrrrrutilsrrr__all__InvertedResidualr" ShuffleNetV2rlistintr_rorrrrrrrr r!r1r1r1r2s       &   - - -