U (d? @sjddlmZddlmZmZmZmZddlZddlm Z ddlm Z ddl m Z ddl mZdd lmZmZdd lmZdd lmZmZdd l mZmZmZmZd dlmZmZmZdddddddddg ZGddde j Z!Gddde j"Z#ee$ee$eee%e%ee#dddZ&dedd d!d"Z'Gd#ddeZ(Gd$ddeZ)Gd%ddeZ*Gd&ddeZ+ed'd(d)fd*dd+d,d-eee(efe%e%ee#d.d/dZ,ed'd0d)fd*dd+d,d-eee)efe%e%ee#d.d1dZ-dd+d,d-eee*efe%e%ee#d.d2dZ.dd+d,d-eee+efe%e%ee#d.d3dZ/dd4lm0Z0dd5l m1Z1e0e(j2j3e)j2j3d6Z4dS)7)partial)AnyListOptionalUnionN)Tensor) shufflenetv2)ImageClassification) WeightsEnumWeights)_IMAGENET_CATEGORIES)handle_legacy_interface_ovewrite_named_param)ShuffleNet_V2_X0_5_WeightsShuffleNet_V2_X1_0_WeightsShuffleNet_V2_X1_5_WeightsShuffleNet_V2_X2_0_Weights) _fuse_modules _replace_reluquantize_modelQuantizableShuffleNetV2#ShuffleNet_V2_X0_5_QuantizedWeights#ShuffleNet_V2_X1_0_QuantizedWeights#ShuffleNet_V2_X1_5_QuantizedWeights#ShuffleNet_V2_X2_0_QuantizedWeightsshufflenet_v2_x0_5shufflenet_v2_x1_0shufflenet_v2_x1_5shufflenet_v2_x2_0cs6eZdZeeddfdd ZeedddZZS)QuantizableInvertedResidualNargskwargsreturncstj||tj|_dSN)super__init__nnZ quantizedZFloatFunctionalcatselfr$r% __class__P/tmp/pip-unpacked-wheel-vx7f76es/torchvision/models/quantization/shufflenetv2.pyr)$sz$QuantizableInvertedResidual.__init__xr&cCsh|jdkr8|jddd\}}|jj|||gdd}n |jj||||gdd}t|d}|S)Nrr )Zdim)Zstridechunkr+branch2branch1rZchannel_shuffle)r-r3x1Zx2outr0r0r1forward(s    z#QuantizableInvertedResidual.forward)__name__ __module__ __qualname__rr)rr9 __classcell__r0r0r.r1r"#sr"csLeZdZeeddfdd ZeedddZd eeddd d Z Z S) rNr#cs6tj|dti|tjj|_tjj|_ dS)NZinverted_residual) r(r)r"torchZaoZ quantizationZ QuantStubquantZ DeQuantStubdequantr,r.r0r1r)6sz QuantizableShuffleNetV2.__init__r2cCs"||}||}||}|Sr')r?Z _forward_implr@)r-r3r0r0r1r9;s   zQuantizableShuffleNetV2.forward)is_qatr&cCs|jD]0\}}|dkr |dk r t|dddgg|ddq |D]l}t|tkrDt|jjdkrt|jddgdd d gg|ddt|jdddgd d gd d d gg|ddqDdS)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 )Zconv1Zconv5N012T)Zinplacer34567) Z_modulesitemsrmodulestyper"lenr6r5)r-rAnamemr0r0r1 fuse_modelAs    z"QuantizableShuffleNetV2.fuse_model)N) r:r;r<rr)rr9rboolrPr=r0r0r.r1r4s)stages_repeatsstages_out_channelsweightsprogressquantizer%r&cKs|dk r:t|dt|jdd|jkr:t|d|jd|dd}t||f|}t||rjt|||dk r||j|d|S)NZ num_classes categoriesbackendfbgemm)rU) rrMmetapoprrrZload_state_dictZget_state_dict)rRrSrTrUrVr%rXmodelr0r0r1 _shufflenetv2Zs    r])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. )Zmin_sizerWrXrecipeZ_docsc @s>eZdZedeeddedejddddid 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_metricsurlZ transformsrZN) r:r;r<r rr _COMMON_METAr IMAGENET1K_V1IMAGENET1K_FBGEMM_V1DEFAULTr0r0r0r1rs c @s>eZdZedeeddedejddddid d Z e Z d S) rzQhttps://download.pytorch.org/models/quantized/shufflenetv2_x1_fbgemm-db332c57.pthr_r`i"rbgףp= Q@gh|?U@rcrdrhN) r:r;r<r rr rjrrkrlrmr0r0r0r1rs c @sBeZdZedeedddeddejddd d id d Z e Z d S)rzShttps://download.pytorch.org/models/quantized/shufflenetv2_x1_5_fbgemm-d7401f05.pthr_raZ resize_size+https://github.com/pytorch/vision/pull/5906iv5rbgSR@g̬V@rcr^rerfrgrhN) r:r;r<r rr rjrrkrlrmr0r0r0r1rs c @sBeZdZedeedddeddejddd d id d Z e Z d S)rzShttps://download.pytorch.org/models/quantized/shufflenetv2_x2_0_fbgemm-5cac526c.pthr_rnrorpiprbg-R@gZd;W@rcrqrhN) r:r;r<r rr rjrrkrlrmr0r0r0r1rs Z pretrainedcCs|ddrtjStjSNrVF)getrrlrrkr%r0r0r1s ru)rTTFrTrUrV)rTrUrVr%r&cKs<|rtnt|}tdddgdddddgf|||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: 0`rv)rrverifyr]rTrUrVr%r0r0r1rs/ cCs|ddrtjStjSrr)rsrrlrrkrtr0r0r1rus cKs<|rtnt|}tdddgdddddgf|||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: rwrxrytrnir}rv)rrr~r]rr0r0r1rs/ cKs<|rtnt|}tdddgdddddgf|||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: rwrxryi`ir}rv)rrr~r]rr0r0r1r 8s' cKs<|rtnt|}tdddgdddddgf|||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: rwrxryiiirv)rrr~r]rr0r0r1r!es' ) _ModelURLs) model_urls)zshufflenetv2_x0.5_fbgemmzshufflenetv2_x1.0_fbgemm)5 functoolsrtypingrrrrr>Ztorch.nnr*rZtorchvision.modelsrZtransforms._presetsr Z_apir r Z_metar_utilsrrrrrrutilsrrr__all__ZInvertedResidualr"Z ShuffleNetV2rintrQr]rjrrrrrrr r!rrrlriZquant_model_urlsr0r0r0r1s       '   - / / .