o ,h@sTddlmZddlmZddlmZmZddlmZm Z ddl m Z ddl mZddlmZd d lmZmZmZd d lmZd d lmZmZmZd d lmZmZmZgdZGdddej Z!Gdddej Z"dede#de!fddZ$GdddeZ%eede%j&fdej'fddddej'dd ee%d!e(dee#d"eed#ede!f d$d%Z)dS)&) OrderedDict)partial)AnyOptional)nnTensor) functional)SemanticSegmentation)_log_api_usage_once)register_modelWeights WeightsEnum)_VOC_CATEGORIES)_ovewrite_value_paramhandle_legacy_interfaceIntermediateLayerGetter)mobilenet_v3_largeMobileNet_V3_Large_Weights MobileNetV3)LRASPP!LRASPP_MobileNet_V3_Large_Weightslraspp_mobilenet_v3_largec sZeZdZdZ ddejdededededd f fd d Zd ede e effd dZ Z S)ra Implements a Lite R-ASPP Network for semantic segmentation from `"Searching for MobileNetV3" `_. Args: backbone (nn.Module): the network used to compute the features for the model. The backbone should return an OrderedDict[Tensor], with the key being "high" for the high level feature map and "low" for the low level feature map. low_channels (int): the number of channels of the low level features. high_channels (int): the number of channels of the high level features. num_classes (int, optional): number of output classes of the model (including the background). inter_channels (int, optional): the number of channels for intermediate computations. backbone low_channels high_channels num_classesinter_channelsreturnNcs,tt|||_t|||||_dS)N)super__init__r r LRASPPHead classifier)selfrrrrr __class__a/var/www/html/scripts/venv/lib/python3.10/site-packages/torchvision/models/segmentation/lraspp.pyr"#s zLRASPP.__init__inputcCsB||}||}tj||jddddd}t}||d<|S)NbilinearFsizemode align_cornersout)rr$F interpolateshaper)r%r*featuresr1resultr(r(r)forward+s  zLRASPP.forward)r) __name__ __module__ __qualname____doc__rModuleintr"rdictstrr7 __classcell__r(r(r&r)rs""rc sLeZdZdededededdf fdd Zd eeefdefd d ZZ S) r#rrrrr Nc stttj||dddt|tjdd|_ttdtj||dddt |_ t||d|_ t||d|_ dS)NF)biasT)inplace) r!r"r SequentialConv2d BatchNorm2dReLUcbrAdaptiveAvgPool2dSigmoidscalelow_classifierhigh_classifier)r%rrrrr&r(r)r"7s  zLRASPPHead.__init__r*cCs\|d}|d}||}||}||}tj||jddddd}||||S)Nlowhighr+r,Fr-)rHrKr2r3r4rLrM)r%r*rNrOxsr(r(r)r7Fs  zLRASPPHead.forward) r8r9r:r=r"r>r?rr7r@r(r(r&r)r#6s""r#rrr cCsz|j}dgddt|Dt|dg}|d}|d}||j}||j}t|t|dt|did }t||||S) NrcSs g|] \}}t|ddr|qS)_is_cnF)getattr).0ibr(r(r) Vs z'_lraspp_mobilenetv3..rArNrO) return_layers)r5 enumeratelen out_channelsrr?r)rr stage_indiceslow_poshigh_posrrr(r(r)_lraspp_mobilenetv3Rs&  rac @sBeZdZedeedddeddddd d id d d ddZeZdS)rzJhttps://download.pytorch.org/models/lraspp_mobilenet_v3_large-d234d4ea.pthi) resize_sizei"(1)rArAz]https://github.com/pytorch/vision/tree/main/references/segmentation#lraspp_mobilenet_v3_largezCOCO-val2017-VOC-labelsg33333L@gV@)miou pixel_accg㥛 @g{G(@z These weights were trained on a subset of COCO, using only the 20 categories that are present in the Pascal VOC dataset. ) num_params categoriesmin_sizerecipe_metrics_ops _file_size_docs)url transformsmetaN) r8r9r:rrr rCOCO_WITH_VOC_LABELS_V1DEFAULTr(r(r(r)r`s& r pretrainedpretrained_backbone)weightsweights_backboneNT)rtprogressrrurtrvrukwargscKs|ddr tdt|}t|}|dur&d}td|t|jd}n|dur,d}t|dd }t ||}|durE| |j |dd |S) a|Constructs a Lite R-ASPP Network model with a MobileNetV3-Large backbone from `Searching for MobileNetV3 `_ paper. .. betastatus:: segmentation module Args: weights (:class:`~torchvision.models.segmentation.LRASPP_MobileNet_V3_Large_Weights`, optional): The pretrained weights to use. See :class:`~torchvision.models.segmentation.LRASPP_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. num_classes (int, optional): number of output classes of the model (including the background). aux_loss (bool, optional): If True, it uses an auxiliary loss. weights_backbone (:class:`~torchvision.models.MobileNet_V3_Large_Weights`, optional): The pretrained weights for the backbone. **kwargs: parameters passed to the ``torchvision.models.segmentation.LRASPP`` base class. Please refer to the `source code `_ for more details about this class. .. autoclass:: torchvision.models.segmentation.LRASPP_MobileNet_V3_Large_Weights :members: aux_lossFz&This model does not use auxiliary lossNrrfT)rtdilated)rv check_hash) popNotImplementedErrorrverifyrrr\rorraload_state_dictget_state_dict)rtrvrrurwrmodelr(r(r)rzs &    r)* collectionsr functoolsrtypingrrtorchrrtorch.nnrr2transforms._presetsr utilsr _apir rr_metar_utilsrrr mobilenetv3rrr__all__r<rr#r=rarrp IMAGENET1K_V1boolrr(r(r(r)sL     #