U Tch!@sddlZddlZddlmZddlmZddlmZddlm Z m Z m Z ddl m Z ddd d d d d ddddddddZdddZdS)N) DPTDepthModel)MidasNet)MidasNet_small)ResizeNormalizeImage PrepareForNet)Composezweights/dpt_beit_large_512.ptzweights/dpt_beit_large_384.ptzweights/dpt_beit_base_384.ptzweights/dpt_swin2_large_384.ptzweights/dpt_swin2_base_384.ptzweights/dpt_swin2_tiny_256.ptzweights/dpt_swin_large_384.ptz!weights/dpt_next_vit_large_384.ptzweights/dpt_levit_224.ptzweights/dpt_large_384.ptzweights/dpt_hybrid_384.ptzweights/midas_v21_384.ptzweights/midas_v21_small_256.ptz(weights/openvino_midas_v21_small_256.xml)dpt_beit_large_512dpt_beit_large_384dpt_beit_base_384dpt_swin2_large_384dpt_swin2_base_384dpt_swin2_tiny_256dpt_swin_large_384dpt_next_vit_large_384 dpt_levit_224 dpt_large_384dpt_hybrid_384 midas_v21_384midas_v21_small_256openvino_midas_v21_small_256rTFc Csd|krddlm}| }|dkrXt|ddd}d\} } d } td d d gd d d gd } n~|d krt|d dd}d\} } d } td d d gd d d gd } n@|dkrt|ddd}d\} } d } td d d gd d d gd } n|dkrt|ddd}d\} } d}d } td d d gd d d gd } n|dkr\t|ddd}d\} } d}d } td d d gd d d gd } nz|dkrt|ddd}d\} } d}d } td d d gd d d gd } n6|dkrt|ddd}d\} } d}d } td d d gd d d gd } n|dkr$t|ddd}d\} } d } td d d gd d d gd } n|dkrlt|dddd d!}d"\} } d}d } td d d gd d d gd } nj|d#krt|d$dd}d\} } d } td d d gd d d gd } n*|d%krt|d&dd}d\} } d } td d d gd d d gd } n|d'kr&t|dd(}d\} } d)} td*d+d,gd-d.d/gd } n|d0krnt|dd1ddd2did3}d\} } d)} td*d+d,gd-d.d/gd } nh|d4kr|} | j|d5}| |d6}d\} } d)} td*d+d,gd-d.d/gd } ntd7|d8dst d|krtd9 t d:d;| Dd<ntd=d|krd}|d>k r0||} } t t| | d>|d?| tjd@| tg}d|krf||r|tdAkrd|kr|jtjdB}|}ntdCtd|kr||||| | fS)Da Load the specified network. Args: device (device): the torch device used model_path (str): path to saved model model_type (str): the type of the model to be loaded optimize (bool): optimize the model to half-integer on CUDA? height (int): inference encoder image height square (bool): resize to a square resolution? Returns: The loaded network, the transform which prepares images as input to the network and the dimensions of the network input Zopenvinor)Corer Z beitl16_512T)pathbackbone non_negative)rZminimalg?)meanstdr Z beitl16_384)rr Z beitb16_384r Z swin2l24_384Fr Z swin2b24_384rZ swin2t16_256)rrZ swinl12_384rZnext_vit_large_6mrZ levit_384@)rrrZhead_features_1Zhead_features_2)r"rZ vitl16_384rZ vitb_rn50_384r)r upper_boundg ףp= ?gv/?gCl?gZd;O?gy&1?g?rZefficientnet_lite3expand)ZfeaturesrZ exportablerblocksr)modelCPUz model_type 'z*' not implemented, use: --model_type largez,Model loaded, number of parameters = {:.0f}Mcss|]}|VqdS)N)numel).0pr+s/home/vaishanth/workspace/independent_study_project/ros_mono_depth_ws/src/ros_monocular_depth/midas/model_loader.py szload_model..g.Az%Model loaded, optimized with OpenVINON )Z resize_targetkeep_aspect_ratioZensure_multiple_ofZ resize_methodZimage_interpolation_methodcuda) memory_formatzUError: OpenVINO models are already optimized. No optimization to half-float possible.)Zopenvino.runtimerrrrrZ read_modelZ compile_modelprintAssertionErrorformatsum parametersrrcv2 INTER_CUBICrevaltorchdeviceto channels_lasthalfexit)r;Z model_path model_typeoptimizeheightsquarerr/r&net_wnet_hZ resize_mode normalizationieZuncompiled_model transformr+r+r, load_modelsX                  &         rI)rTNF)r7r:Zmidas.dpt_depthrZmidas.midas_netrZmidas.midas_net_customrZmidas.transformsrrrZtorchvision.transformsrdefault_modelsrIr+r+r+r,s,