a PSic?@s\ddlmZddlmZmZmZmZddlZddlm Z ddlm Z ddl m Z ddl mZdd lmZmZdd lmZdd lmZmZdd l mZmZmZmZd dlmZmZmZgdZGddde j Z!Gddde j"Z#ee$ee$eee%e%ee#dddZ&deddddZ'GdddeZ(GdddeZ)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+d/d0Z-dd(d)d*eee*efe%e%ee#d+d1d2Z.dd(d)d*eee+efe%e%ee#d+d3d4Z/dd5lm0Z0dd6l m1Z1e0e(j2j3e)j2j3d7Z4dS)8)partial)AnyListOptionalUnionN)Tensor) shufflenetv2)ImageClassification) WeightsEnumWeights)_IMAGENET_CATEGORIES)handle_legacy_interface_ovewrite_named_param)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_0cs6eZdZeeddfdd ZeedddZZS)QuantizableInvertedResidualNargskwargsreturncs"tj|i|tj|_dSN)super__init__nn quantizedFloatFunctionalcatselfr$r% __class__h/var/www/html/django/DPS/env/lib/python3.9/site-packages/torchvision/models/quantization/shufflenetv2.pyr)$sz$QuantizableInvertedResidual.__init__xr&cCsh|jdkr8|jddd\}}|jj|||gdd}n |jj||||gdd}t|d}|S)Nrr )dim)stridechunkr-branch2branch1rchannel_shuffle)r/r5x1x2outr2r2r3forward(s    z#QuantizableInvertedResidual.forward)__name__ __module__ __qualname__rr)rr? __classcell__r2r2r0r3r"#sr"csLeZdZeeddfdd ZeedddZd eeddd d Z Z S) rNr#cs6tj|dti|tjj|_tjj|_ dS)Ninverted_residual) r(r)r"torchao quantization QuantStubquant DeQuantStubdequantr.r0r2r3r)6sz QuantizableShuffleNetV2.__init__r4cCs"||}||}||}|Sr')rI _forward_implrK)r/r5r2r2r3r?;s   zQuantizableShuffleNetV2.forward)is_qatr&cCs|jD].\}}|dvr |dur t|gdg|ddq |D]f}t|turBt|jjdkrt|jddggd g|ddt|jgdd d ggd g|ddqBdS) 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)inplacerrPrQ)rR34rTrU)567) _modulesitemsrmodulestyper"lenr:r9)r/rMnamemr2r2r3 fuse_modelAs   z"QuantizableShuffleNetV2.fuse_model)N) r@rArBrr)rr?rboolr`rCr2r2r0r3r4sr)stages_repeatsstages_out_channelsweightsprogressquantizer%r&cKs|dur:t|dt|jdd|jvr:t|d|jd|dd}t||fi|}t||rnt|||dur||j|d|S)N num_classes categoriesbackendfbgemm)re) rr]metapoprrrload_state_dictget_state_dict)rbrcrdrerfr%rimodelr2r2r3 _shufflenetv2Zs    rp)rrrjzdhttps://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_sizerhrirecipe_docsc @sBeZdZedeeddiedejddddid 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@5 num_params unquantized_metricsurl transformsrkN) r@rArBr rr _COMMON_METAr IMAGENET1K_V1IMAGENET1K_FBGEMM_V1DEFAULTr2r2r2r3rs  rc @sBeZdZedeeddiedejddddid d Z e Z d S) rzQhttps://download.pytorch.org/models/quantized/shufflenetv2_x1_fbgemm-db332c57.pthrtrui"rwgףp= Q@gh|?U@rxryr}N) r@rArBr rr rrrrrr2r2r2r3rs  rc @sFeZdZedeedddieddejddd d id d Z e Z d S)rzShttps://download.pytorch.org/models/quantized/shufflenetv2_x1_5_fbgemm-d7401f05.pthrtrv resize_size+https://github.com/pytorch/vision/pull/5906iv5rwgSR@g̬V@rxrrrzr{r|r}N) r@rArBr rr rrrrrr2r2r2r3rs" rc @sFeZdZedeedddieddejddd d id d Z e Z d S)rzShttps://download.pytorch.org/models/quantized/shufflenetv2_x2_0_fbgemm-5cac526c.pthrtrrriprwg-R@gZd;W@rxrr}N) r@rArBr rr rrrrrr2r2r2r3rs" r pretrainedcCs|ddrtjStjSNrfF)getrrrrr%r2r2r3s r)rdTFrdrerf)rdrerfr%r&cKs4|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)rrverifyrprdrerfr%r2r2r3rs/ rcCs|ddrtjStjSr)rrrrrrr2r2r3rs cKs4|rtnt|}tgdgdf|||d|S)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)rrrrprr2r2r3rs/ rcKs4|rtnt|}tgdgdf|||d|S)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 8s' r cKs4|rtnt|}tgdgdf|||d|S)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!es' r!) _ModelURLs) model_urls)zshufflenetv2_x0.5_fbgemmzshufflenetv2_x1.0_fbgemm)5 functoolsrtypingrrrrrEtorch.nnr*rZtorchvision.modelsrZtransforms._presetsr _apir r _metar_utilsrrrrrrutilsrrr__all__InvertedResidualr" ShuffleNetV2rintrarprrrrrrrr r!rrrr~quant_model_urlsr2r2r2r3s       '   - / / .