U (dl< @s$ddlmZddlmZmZmZmZddlZddlm Z ddlm Z ddl m Z ddl mZdd lmZmZdd lmZdd lmZmZd d dddddddg Ze ee dddZGddde jZGdd d e jZeeeeeedddZdedd ZGd!d d eZ Gd"ddeZ!Gd#ddeZ"Gd$ddeZ#ed%e j$fd&dd'd(ee eeed)d*dZ%ed%e!j$fd&dd'd(ee!eeed)d+dZ&ed%e"j$fd&dd'd(ee"eeed)d,dZ'ed%e#j$fd&dd'd(ee#eeed)d-dZ(dd.lm)Z)e)e j$j*e!j$j*ddd/Z+dS)0)partial)CallableAnyListOptionalN)Tensor)ImageClassification)_log_api_usage_once) WeightsEnumWeights)_IMAGENET_CATEGORIES)handle_legacy_interface_ovewrite_named_param ShuffleNetV2ShuffleNet_V2_X0_5_WeightsShuffleNet_V2_X1_0_WeightsShuffleNet_V2_X1_5_WeightsShuffleNet_V2_X2_0_Weightsshufflenet_v2_x0_5shufflenet_v2_x1_0shufflenet_v2_x1_5shufflenet_v2_x2_0)xgroupsreturncCsP|\}}}}||}||||||}t|dd}||d||}|S)Nr r)sizeviewtorchZ transpose contiguous)rrZ batchsizeZ num_channelsheightwidthZchannels_per_groupr$C/tmp/pip-unpacked-wheel-vx7f76es/torchvision/models/shufflenetv2.pychannel_shuffles r&c sZeZdZeeeddfdd Zedeeeeeeejdd d Z e e d d d Z Z S)InvertedResidualN)inpoupstridercsNtd|krdks(ntd||_|d}|jdkrh||d>krhtd|d|d|d|jdkrt|j||d|jdd t|tj||ddd d d t|tj d d|_ n t|_ ttj|jdkr|n||ddd d d t|tj d d|j||d|jdd t|tj||ddd d d t|tj d d|_ dS)Nr zillegal stride valuerzInvalid combination of stride z, inp z and oup zB values. If stride == 1 then inp should be equal to oup // 2 << 1. kernel_sizer*paddingrF)r-r*r.biasTZinplace) super__init__ ValueErrorr*nn Sequentialdepthwise_conv BatchNorm2dConv2dReLUbranch1branch2)selfr(r)r*Zbranch_features __class__r$r%r2,sF      zInvertedResidual.__init__r rF)ior-r*r.r/rc Cstj|||||||dS)N)r/r)r4r8)r?r@r-r*r.r/r$r$r%r6VszInvertedResidual.depthwise_convrrcCsb|jdkr6|jddd\}}tj|||fdd}ntj||||fdd}t|d}|S)Nr r)Zdim)r*chunkr catr;r:r&)r<rx1Zx2outr$r$r%forward\s   zInvertedResidual.forward)r rF) __name__ __module__ __qualname__intr2 staticmethodboolr4r8r6rrF __classcell__r$r$r=r%r'+s*r'csbeZdZdefeeeeeedejfddfdd Z e e ddd Z e e dd d Z Z S) ri.N)stages_repeatsstages_out_channels num_classesinverted_residualrc sdtt|t|dkr&tdt|dkr:td||_d}|jd}ttj||ddddd t |tj d d |_ |}tj dddd |_ |||d ddD}t|||jddD]R\}} }|||dg} t| dD]} | |||dqt||tj| |}q|jd}ttj||ddddd t |tj d d |_t|||_dS)Nr+z2expected stages_repeats as list of 3 positive intsz7expected stages_out_channels as list of 5 positive intsrrr F)r/Tr0r,cSsg|]}d|qS)Zstager$).0r?r$r$r% sz)ShuffleNetV2.__init__..)rr+r)r1r2r lenr3Z_stage_out_channelsr4r5r8r7r9conv1Z MaxPool2dmaxpoolziprangeappendsetattrconv5ZLinearfc) r<rNrOrPrQZinput_channelsZoutput_channelsZ stage_namesnameZrepeatsseqr?r=r$r%r2isB        zShuffleNetV2.__init__rAcCsX||}||}||}||}||}||}|ddg}||}|S)Nrr+)rWrXZstage2Zstage3Zstage4r]Zmeanr^r<rr$r$r% _forward_impls       zShuffleNetV2._forward_implcCs ||S)N)rbrar$r$r%rFszShuffleNetV2.forward)rGrHrIr'rrJrr4Moduler2rrbrFrMr$r$r=r%rhs 0 )weightsprogressargskwargsrcOsF|dk rt|dt|jdt||}|dk rB||j|d|S)NrP categories)re)rrVmetarZload_state_dictZget_state_dict)rdrerfrgmodelr$r$r% _shufflenetv2s  rk)r r z2https://github.com/ericsun99/Shufflenet-v2-Pytorch)Zmin_sizerhrecipec @s<eZdZedeeddedddddid d d ZeZd S) rzDhttps://download.pytorch.org/models/shufflenetv2_x0.5-f707e7126e.pth crop_sizei ImageNet-1Kg-FN@g9voT@zacc@1zacc@5VThese weights were trained from scratch to reproduce closely the results of the paper. num_params_metrics_docsurlZ transformsriN rGrHrIr rr _COMMON_META IMAGENET1K_V1DEFAULTr$r$r$r%rs c @s<eZdZedeeddedddddid d d ZeZd S) rzBhttps://download.pytorch.org/models/shufflenetv2_x1-5666bf0f80.pthrmrni"rpgI +WQ@gNbX9V@rqrrrsrwNryr$r$r$r%rs c @s@eZdZedeedddeddddd d id d d ZeZdS)rzBhttps://download.pytorch.org/models/shufflenetv2_x1_5-3c479a10.pthrmroZ resize_size+https://github.com/pytorch/vision/pull/5906iv5rpg9v?R@g/$V@rq These weights were trained from scratch by using TorchVision's `new training recipe `_. rlrtrurvrwNryr$r$r$r%rs c @s@eZdZedeedddeddddd d id d d ZeZdS)rzBhttps://download.pytorch.org/models/shufflenetv2_x2_0-8be3c8ee.pthrmr}r~riprpgQS@gMb@W@rqrrrwNryr$r$r$r%rs Z pretrained)rdT)rdre)rdrergrc Ks,t|}t||dddgdddddgf|S)a  Constructs a ShuffleNetV2 architecture with 0.5x output channels, as described in `ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design `__. Args: weights (:class:`~torchvision.models.ShuffleNet_V2_X0_5_Weights`, optional): The pretrained weights to use. See :class:`~torchvision.models.ShuffleNet_V2_X0_5_Weights` below for more details, and possible values. By default, no pre-trained weights are used. progress (bool, optional): If True, displays a progress bar of the download to stderr. Default is True. **kwargs: parameters passed to the ``torchvision.models.shufflenetv2.ShuffleNetV2`` base class. Please refer to the `source code `_ for more details about this class. .. autoclass:: torchvision.models.ShuffleNet_V2_X0_5_Weights :members: rU0`)rverifyrkrdrergr$r$r%rs c Ks,t|}t||dddgdddddgf|S)a  Constructs a ShuffleNetV2 architecture with 1.0x output channels, as described in `ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design `__. Args: weights (:class:`~torchvision.models.ShuffleNet_V2_X1_0_Weights`, optional): The pretrained weights to use. See :class:`~torchvision.models.ShuffleNet_V2_X1_0_Weights` below for more details, and possible values. By default, no pre-trained weights are used. progress (bool, optional): If True, displays a progress bar of the download to stderr. Default is True. **kwargs: parameters passed to the ``torchvision.models.shufflenetv2.ShuffleNetV2`` base class. Please refer to the `source code `_ for more details about this class. .. autoclass:: torchvision.models.ShuffleNet_V2_X1_0_Weights :members: rUrrtr}ir)rrrkrr$r$r%r5s c Ks,t|}t||dddgdddddgf|S)a  Constructs a ShuffleNetV2 architecture with 1.5x output channels, as described in `ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design `__. Args: weights (:class:`~torchvision.models.ShuffleNet_V2_X1_5_Weights`, optional): The pretrained weights to use. See :class:`~torchvision.models.ShuffleNet_V2_X1_5_Weights` below for more details, and possible values. By default, no pre-trained weights are used. progress (bool, optional): If True, displays a progress bar of the download to stderr. Default is True. **kwargs: parameters passed to the ``torchvision.models.shufflenetv2.ShuffleNetV2`` base class. Please refer to the `source code `_ for more details about this class. .. autoclass:: torchvision.models.ShuffleNet_V2_X1_5_Weights :members: rUrri`ir)rrrkrr$r$r%rSs c Ks,t|}t||dddgdddddgf|S)a  Constructs a ShuffleNetV2 architecture with 2.0x output channels, as described in `ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design `__. Args: weights (:class:`~torchvision.models.ShuffleNet_V2_X2_0_Weights`, optional): The pretrained weights to use. See :class:`~torchvision.models.ShuffleNet_V2_X2_0_Weights` below for more details, and possible values. By default, no pre-trained weights are used. progress (bool, optional): If True, displays a progress bar of the download to stderr. Default is True. **kwargs: parameters passed to the ``torchvision.models.shufflenetv2.ShuffleNetV2`` base class. Please refer to the `source code `_ for more details about this class. .. autoclass:: torchvision.models.ShuffleNet_V2_X2_0_Weights :members: rUrriii)rrrkrr$r$r%rqs ) _ModelURLs)zshufflenetv2_x0.5zshufflenetv2_x1.0zshufflenetv2_x1.5zshufflenetv2_x2.0), functoolsrtypingrrrrr Ztorch.nnr4rZtransforms._presetsr utilsr Z_apir r Z_metar_utilsrr__all__rJr&rcr'rrLrkrzrrrrr{rrrrrrxZ model_urlsr$r$r$r%s       =B