o V h? @sddlmZddlmZmZmZmZmZddlZddlm Z m Z ddl m Z m ZddlmZddlmZd d lmZmZmZd d lmZd d lmZmZmZgd ZGdddZGddde jZ Gddde jZ! d2de"de#de$de$def ddZ%deede&deed e$ded!e!f d"d#Z'd$ed%Z(Gd&d'd'eZ)Gd(d)d)eZ*eed*e)j+fd+dd,d-dee)d e$ded!e!fd.d/Z,eed*e*j+fd+dd,d-dee*d e$ded!e!fd0d1Z-dS)3)partial)AnyCallableListOptionalSequenceN)nnTensor)Conv2dNormActivationSqueezeExcitation)ImageClassification)_log_api_usage_once)register_modelWeights WeightsEnum)_IMAGENET_CATEGORIES)_make_divisible_ovewrite_named_paramhandle_legacy_interface) MobileNetV3MobileNet_V3_Large_WeightsMobileNet_V3_Small_Weightsmobilenet_v3_largemobilenet_v3_smallc@sPeZdZdedededededededed efd d Zed ed efd dZ dS)InvertedResidualConfiginput_channelskernelexpanded_channels out_channelsuse_se activationstridedilation width_multc CsP||| |_||_||| |_||| |_||_|dk|_||_||_dS)NHS) adjust_channelsrrrr r!use_hsr#r$) selfrrrr r!r"r#r$r%r*R/var/www/vscode/kcb/lib/python3.10/site-packages/torchvision/models/mobilenetv3.py__init__s   zInvertedResidualConfig.__init__channelscCst||dS)N)r)r-r%r*r*r+r'/sz&InvertedResidualConfig.adjust_channelsN) __name__ __module__ __qualname__intboolstrfloatr, staticmethodr'r*r*r*r+rs,  rc s^eZdZeeejdfdededej fdedej fffdd Z de d e fd d Z Z S) InvertedResidual)scale_activationcnf norm_layer.se_layerc s*td|jkrdkstdtd|jdko"|j|jk|_g}|jr,tj ntj }|j |jkrC| t |j|j d||d|jdkrJdn|j}| t |j |j |j||j|j ||d|jrut|j dd}| ||j || t |j |jd|ddtj||_|j|_|jdk|_dS)Nrr zillegal stride value kernel_sizer:activation_layer)r=r#r$groupsr:r>r.)superr,r# ValueErrorrr use_res_connectr(r HardswishReLUrappendr r$rr!r Sequentialblock_is_cn)r)r9r:r;layersr>r#squeeze_channels __class__r*r+r,6sT     zInvertedResidual.__init__inputreturncCs||}|jr ||7}|SN)rHrC)r)rNresultr*r*r+forwardns zInvertedResidual.forward)r/r0r1rSElayerr HardsigmoidrrModuler,r rR __classcell__r*r*rLr+r74s   8r7cseZdZ    ddeedededeedej fd eedej fd e d e d dffd d Z de d e fddZde d e fddZZS)rN皙?inverted_residual_setting last_channel num_classesrH.r:dropoutkwargsrOc stt||stdt|trtdd|Ds!td|dur't}|dur3t t j ddd}g}|d j } | td | d d |t jd |D] } | || |qJ|d j} d| } | t| | d|t jdt j||_t d|_t t | |t jddt j|ddt |||_|D]L} t| t jrt jj| jdd| jdurt j| jqt| t j t jfrt j | jt j| jqt| t jrt j!| jd dt j| jqdS)a. MobileNet V3 main class Args: inverted_residual_setting (List[InvertedResidualConfig]): Network structure last_channel (int): The number of channels on the penultimate layer num_classes (int): Number of classes block (Optional[Callable[..., nn.Module]]): Module specifying inverted residual building block for mobilenet norm_layer (Optional[Callable[..., nn.Module]]): Module specifying the normalization layer to use dropout (float): The droupout probability z1The inverted_residual_setting should not be emptycSsg|]}t|tqSr*) isinstancer).0sr*r*r+ sz(MobileNetV3.__init__..zDThe inverted_residual_setting should be List[InvertedResidualConfig]NgMbP?g{Gz?)epsmomentumrr )r=r#r:r>rr<T)inplace)prgfan_out)mode)"rAr,rrBr^rall TypeErrorr7rr BatchNorm2drrFr rDr rGfeaturesAdaptiveAvgPool2davgpoolLinearDropout classifiermodulesConv2dinitkaiming_normal_weightbiaszeros_ GroupNormones_normal_)r)rYrZr[rHr:r\r]rJfirstconv_output_channelsr9lastconv_input_channelslastconv_output_channelsmrLr*r+r,vsx             zMobileNetV3.__init__xcCs.||}||}t|d}||}|S)Nr)rnrptorchflattenrsr)rr*r*r+ _forward_impls    zMobileNetV3._forward_implcCs ||SrP)rrr*r*r+rRs zMobileNetV3.forward)rWNNrX)r/r0r1rrr2rrrrUr5rr,r rrRrVr*r*rLr+rus0 [ r?Farchr% reduced_taildilatedr]c Ks|rdnd}|r dnd}tt|d}ttj|d}|dkr|dddddddd|ddd d dddd|d dd d dddd|d d d d dddd|d d dd dddd|d d dd dddd|d ddddddd|dddddddd|dddddddd|dddddddd|dddddddd|dddddddd|dd dd|ddd||d|d d|d|ddd||d|d d|d|ddd|g} |d|} | | fS|dkrZ|dddddddd|ddd d dddd|d ddd dddd|d d dd dddd|d d dd dddd|d d dd dddd|d d dddddd|dd dddddd|dd d d|ddd||d|d d!|d|ddd||d|d d!|d|ddd|g } |d"|} | | fStd#|)$Nr r)r%rrdFRE@H(TxPr&ipiiirX`0i i@izUnsupported model type )rrr'rB) rr%rrr]reduce_dividerr$ bneck_confr'rYrZr*r*r+_mobilenet_v3_confsN          rrYrZweightsprogressrOcKsP|durt|dt|jdt||fi|}|dur&||j|dd|S)Nr[ categoriesT)r check_hash)rlenmetarload_state_dictget_state_dict)rYrZrrr]modelr*r*r+ _mobilenet_v3s r)rr)min_sizerc @s~eZdZedeeddiedddddd id d d d dZedeedddiedddddd id ddd dZeZ dS)rzChttps://download.pytorch.org/models/mobilenet_v3_large-8738ca79.pth crop_sizeiS^https://github.com/pytorch/vision/tree/main/references/classification#mobilenetv3-large--small ImageNet-1Kg R@g(\V@zacc@1zacc@5g-?gw/5@zJThese weights were trained from scratch by using a simple training recipe. num_paramsrecipe_metrics_ops _file_size_docsurl transformsrzChttps://download.pytorch.org/models/mobilenet_v3_large-5c1a4163.pth)r resize_sizezHhttps://github.com/pytorch/vision/issues/3995#new-recipe-with-reg-tuninggK7R@gNbX9$W@gZd5@a/ These weights improve marginally upon the results of the original paper by using a modified version of TorchVision's `new training recipe `_. N) r/r0r1rrr _COMMON_META IMAGENET1K_V1 IMAGENET1K_V2DEFAULTr*r*r*r+r(sL  rc @sFeZdZedeeddiedddddd id d d d dZeZdS)rzChttps://download.pytorch.org/models/mobilenet_v3_small-047dcff4.pthrri&rrgnP@g}?5^U@rgv/?g r#@z} These weights improve upon the results of the original paper by using a simple training recipe. rrN) r/r0r1rrr rrrr*r*r*r+rTs( r pretrained)rT)rrcK2t|}tdi|\}}t||||fi|S)a Constructs a large MobileNetV3 architecture from `Searching for MobileNetV3 `__. Args: weights (:class:`~torchvision.models.MobileNet_V3_Large_Weights`, optional): The pretrained weights to use. See :class:`~torchvision.models.MobileNet_V3_Large_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.mobilenet.MobileNetV3`` base class. Please refer to the `source code `_ for more details about this class. .. autoclass:: torchvision.models.MobileNet_V3_Large_Weights :members: rN)r)rverifyrrrrr]rYrZr*r*r+rl rcKr)a Constructs a small MobileNetV3 architecture from `Searching for MobileNetV3 `__. Args: weights (:class:`~torchvision.models.MobileNet_V3_Small_Weights`, optional): The pretrained weights to use. See :class:`~torchvision.models.MobileNet_V3_Small_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.mobilenet.MobileNetV3`` base class. Please refer to the `source code `_ for more details about this class. .. autoclass:: torchvision.models.MobileNet_V3_Small_Weights :members: rN)r)rrrrrr*r*r+rrr)rFF). functoolsrtypingrrrrrrrr ops.miscr r rStransforms._presetsr utilsr_apirrr_metar_utilsrrr__all__rrUr7rr4r5r3rr2rrrrrrrr*r*r*r+s     Ak 1 ,