je ddlZddlmZddlZddlmZddlmZmZm Z m Z ddl m Z m Z mZmZmZddlmZdeddfd Zd'd Zejd e d ed ededdfdZejd e d ed ededdfdZejd e d ed ededdfdZejd e d eddfdZeje ddedz dfd ed ededdfdZejejdejejoeoe ddedz dfd ed ededdfdZ d'dZ!ejj"ejd e eje d ejed!kd"d ed ededdfd#Z#ejd$gd%d$eddfd&Z$dS)(N)Path)Image)CUDA_DEVICE_COUNTCUDA_IS_AVAILABLEMODELSTASK_MODEL_DATA)ARM64ASSETSLINUX WEIGHTS_DIRchecks) TORCH_1_11cmdreturncVtj|ddS)z)Execute a shell command using subprocess.T)checkN) subprocessrunsplit)rs S/home/longshao/multi-rider-rag/.venv/lib/python3.11/site-packages/tests/test_cli.pyrrs%N399;;d++++++ctdtdtdtdtddS)z?Test various special command-line modes for YOLO functionality.z yolo helpz yolo checksz yolo versionzyolo settings resetzyolo cfgNrrrtest_special_modesrsJ   OOOOOrztask,model,datataskmodeldatac 8td|d|d|ddS)z=Test YOLO training for different tasks, models, and datasets. yolo train  model= data=z imgsz=32 epochs=1 cache=diskNrrrrs r test_trainr$s4SdSS5SSSSSTTTTTrc HdD]}td|d|d|d|d dS)zWTest YOLO validation process for specified task, model, and data using a shell command.FTz yolo val r!r"z/ imgsz=32 save_txt save_json visualize end2end=z max_det=100 agnostic_nmsNrrrrend2ends rtest_valr)"sy!    J J JU J J$ J Jgn J J J      rc RdD]#}td|d|dtd|d $dS)zLTest YOLO prediction on provided sample assets for specified task and model.r&zyolo z predict model= source=z4 imgsz=32 save save_crop save_txt visualize end2end= max_det=100Nrr r's r test_predictr.+sy!    JD J J J J J Jt{ J J J      rc<dD]}td|d|ddS)z2Test exporting a YOLO model to TorchScript format.r&zyolo export model=z% format=torchscript imgsz=32 end2end=r,Nr)rr(s r test_exportr04sF!dd b b bW b b bccccddrzRTDETR requires torch>=1.11)reasondetectz rtdetr-l.ptz coco8.yamlc ztd|d|dtdz dtd|d|d|dd S) zdTest the RTDETR functionality within Ultralytics for detection tasks using specified model and data.z yolo predict r!r+bus.jpgz" imgsz=160 save save_crop save_txtr r"z3 --imgsz= 160 epochs =1, cache = disk fraction=0.25Nr-r#s r test_rtdetrr5;scjjjUjjFY4Fjjjkkkidii5iiiiijjjjjrz3MobileSAM with CLIP is not supported in Python 3.12zDMobileSAM with CLIP is not supported in Python 3.8 and aarch64 Linuxsegmentz FastSAM-s.ptzcoco8-seg.yamlc ztdz }td|d|d|dtd|d|dd d lm}d d lm}||}|t j|fD]S}||d d ddd}||d j j d\} } ||gdddggdgdTdS)z]Test FastSAM model for segmenting objects in images using various prompts within Ultralytics.r4zyolo segment val r!r"z imgsz=32zyolo segment predict model=r+z! imgsz=32 save save_crop save_txtr)FastSAM) PredictorcpuTi@g?g?)device retina_masksimgszconfiou)min_areaiii iza photo of a dog)bboxespointslabelstextsN) r r ultralyticsr8ultralytics.models.samr9ropenremove_small_regionsmasksr) rrrsourcer8r9 sam_modelseverything_results _new_masks_s r test_fastsamrTCs8i FEDEEEEdEEEFFF^e^^V^^^___######000000Iej(( )rr&YqTQT[^dghhh"667I!7L7R7Wbd6ee A  &!5!5!5SzlTUSV^pqqqqqrrrcddlm}|tdz }tdz }||ddgdg||ddgd d gggddgg||gd d dS)zITest MobileSAM segmentation with point and box prompts using Ultralytics.r)SAMz mobile_sam.ptz zidane.jpgiirrD)rFrGidrBT)rEsaveN)rIrVr r predict)rVrrNs rtest_mobilesamrZbs C o- . .El "F MM&#sQCM888 MM&C:c{";!rxs^ OOOOOOOOOOOOGGGGGGGGGGGGGG444444,S,T,,,, *O<<USUUCUDUUU=<U *O<< 3 s # $   =< *O<< s 3 c d   =< &))dsdtddd*)d  N+HII$K-4O]ikkckTkWZknrkkkJIkF)2ghh ,U,u Q  n(DRbrr r"%rLOr rrr ih r4BBBB0*O<<))2IJJ%)2HIIUUSUUUUUJIKJ=<U ppp4S4T444 444r