U h @sLddlmZddlmZddlmZmZmZddlm Z m Z ddl m Z ddlmZddlmZd d lmZmZmZd d lmZd d lmZmZmZd d lmZmZmZdddgZ Gddde j!Z"Gddde j!Z#ee$e"dddZ%GdddeZ&eede&j'fdej(fddddej(dee&e)ee$eeee"dddZ*dS) ) OrderedDict)partial)AnyDictOptional)nnTensor) functional)SemanticSegmentation)_log_api_usage_once)register_modelWeights WeightsEnum)_VOC_CATEGORIES)_ovewrite_value_paramhandle_legacy_interfaceIntermediateLayerGetter)mobilenet_v3_largeMobileNet_V3_Large_Weights MobileNetV3LRASPP!LRASPP_MobileNet_V3_Large_Weightslraspp_mobilenet_v3_largecsLeZdZdZd ejeeeeddfdd Zee e efddd Z 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. N)backbone low_channels high_channels num_classesinter_channelsreturncs,tt|||_t|||||_dS)N)super__init__r r LRASPPHead classifier)selfrrrrr  __class__X/var/www/html/venv/lib/python3.8/site-packages/torchvision/models/segmentation/lraspp.pyr##s zLRASPP.__init__inputr!cCsB||}||}tj||jddddd}t}||d<|S)NbilinearFsizemodeZ align_cornersout)rr%F interpolateshaper)r&r,featuresr2resultr)r)r*forward+s   zLRASPP.forward)r) __name__ __module__ __qualname____doc__rModuleintr#rrstrr8 __classcell__r)r)r'r*rscsBeZdZeeeeddfdd ZeeefedddZZ S)r$N)rrrr r!c stttj||dddt|tjdd|_ttdtj||dddt |_ t||d|_ t||d|_ dS)NF)ZbiasT)Zinplace) r"r#rZ SequentialZConv2dZ BatchNorm2dZReLUcbrZAdaptiveAvgPool2dZSigmoidscalelow_classifierhigh_classifier)r&rrrr r'r)r*r#7s  zLRASPPHead.__init__r+cCs\|d}|d}||}||}||}tj||jddddd}||||S)Nlowhighr-r.Fr/)rBrCr3r4r5rDrE)r&r,rFrGxsr)r)r*r8Fs  zLRASPPHead.forward) r9r:r;r>r#rr?rr8r@r)r)r'r*r$6sr$)rrr!cCsz|j}dgddt|Dt|dg}|d}|d}||j}||j}t|t|dt|did }t||||S) NrcSs g|]\}}t|ddr|qS)Z_is_cnF)getattr).0ibr)r)r* Vs z'_lraspp_mobilenetv3..rArFrG)Z return_layers)r6 enumeratelenZ out_channelsrr?r)rrZ stage_indicesZlow_posZhigh_posrrr)r)r*_lraspp_mobilenetv3Rs&  rSc @sBeZdZedeedddeddddd d id d d ddZeZdS)rzJhttps://download.pytorch.org/models/lraspp_mobilenet_v3_large-d234d4ea.pthi)Z resize_sizei"(1)rArAz]https://github.com/pytorch/vision/tree/main/references/segmentation#lraspp_mobilenet_v3_largezCOCO-val2017-VOC-labelsg33333L@gV@)ZmiouZ 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. )Z num_params categoriesZmin_sizeZrecipeZ_metricsZ_ops _file_sizeZ_docs)urlZ transformsmetaN) r9r:r;rrr rCOCO_WITH_VOC_LABELS_V1DEFAULTr)r)r)r*r`s$ Z pretrainedZpretrained_backbone)weightsweights_backboneNT)rZprogressrr[)rZr\rr[kwargsr!cKs|ddrtdt|}t|}|dk rLd}td|t|jd}n |dkrXd}t|dd }t ||}|dk r| |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: Zaux_lossFz&This model does not use auxiliary lossNrrTT)rZZdilated)r\Z check_hash) popNotImplementedErrorrverifyrrrRrWrrSZload_state_dictZget_state_dict)rZr\rr[r]rmodelr)r)r*rzs&     )+ collectionsr functoolsrtypingrrrZtorchrrZtorch.nnr r3Ztransforms._presetsr utilsr Z_apirrr_metarZ_utilsrrrZ mobilenetv3rrr__all__r=rr$r>rSrrXZ IMAGENET1K_V1boolrr)r)r)r*s@       #