a yf@slddlZddlmZddlmZmZmZddlmZddl m Z m Z dZ GdddeZ Gd d d eZdS) N) YOLODataset)ComposeFormat v8_transforms)DetectionValidator)colorstrops)RTDETRValidatorcs>eZdZdZddfdd Zd fdd Zd d d ZZS) RTDETRDataseta  Real-Time DEtection and TRacking (RT-DETR) dataset class extending the base YOLODataset class. This specialized dataset class is designed for use with the RT-DETR object detection model and is optimized for real-time detection and tracking tasks. N)datacstj|d|i|dS)zLInitialize the RTDETRDataset class by inheriting from the YOLODataset class.r N)super__init__)selfr argskwargs __class__Y/var/www/html/django/DPS/env/lib/python3.9/site-packages/ultralytics/models/rtdetr/val.pyr szRTDETRDataset.__init__Fcstj||dS)z?Loads 1 image from dataset index 'i', returns (im, resized hw).)i rect_mode)r load_image)rrrrrrrszRTDETRDataset.load_imagec Csz|jrJ|jr|js|jnd|_|jr0|js0|jnd|_t||j|dd}ntg}|tdd|j |j d|j |j d|S)zTemporary, only for evaluation.gT)ZstretchZxywh)Z bbox_format normalizeZ return_maskZreturn_keypoint batch_idx mask_ratioZ mask_overlap) augmentrectZmosaicZmixuprimgszrappendrZ use_segmentsZ use_keypointsrZ overlap_mask)rhypZ transformsrrrbuild_transformss" zRTDETRDataset.build_transforms)F)N)__name__ __module__ __qualname____doc__r rr __classcell__rrrrr sr c@s2eZdZdZd ddZddZdd Zd d ZdS) r a RTDETRValidator extends the DetectionValidator class to provide validation capabilities specifically tailored for the RT-DETR (Real-Time DETR) object detection model. The class allows building of an RTDETR-specific dataset for validation, applies Non-maximum suppression for post-processing, and updates evaluation metrics accordingly. Example: ```python from ultralytics.models.rtdetr import RTDETRValidator args = dict(model="rtdetr-l.pt", data="coco8.yaml") validator = RTDETRValidator(args=args) validator() ``` Note: For further details on the attributes and methods, refer to the parent DetectionValidator class. valNc Cs4t||jj|d|jd|jjpdt|d|jd S)aL Build an RTDETR Dataset. Args: img_path (str): Path to the folder containing images. mode (str): `train` mode or `val` mode, users are able to customize different augmentations for each mode. batch (int, optional): Size of batches, this is for `rect`. Defaults to None. FNz: ) img_pathrZ batch_sizerrrcacheprefixr )r rrr(rr )rr'modebatchrrr build_datasetJs   zRTDETRValidator.build_datasetc Cst|ttfs|dg}|dj\}}}|djd|dfdd\}}||jj9}tjd|j dg|}t |D]X\}} t | } || d\} } tj| | d| dgdd} | | jd d } | ||<qn|S) z4Apply Non-maximum suppression to prediction outputs.Nr)dim)r)device).NT)Z descending) isinstancelisttupleshapesplitrrtorchZzerosr1 enumerater xywh2xyxymaxcatZargsort) rpredsbs_ndbboxesZscoresoutputsrbboxZscoreclspredrrr postprocess_s   zRTDETRValidator.postprocessc Cs|d|k}|d|d}|d|}|d|}|djdd}|d |}t|rt|}|d d dgf|d 9<|d d d gf|d 9<|||||dS)zGPrepares a batch for training or inference by applying transformations.rrCr.r@ ori_shapeZimgN ratio_pad.r)rCrBrFrrH)Zsqueezer5lenrr9) rsir+idxrCrBrFrrHrrr_prepare_batchts     zRTDETRValidator._prepare_batchcCs`|}|dddgf|dd|jj9<|dddgf|dd|jj9<|S)zNPrepares and returns a batch with transformed bounding boxes and class labels..rrGrFrIrJ)clonerrfloat)rrDZpbatchZprednrrr _prepare_preds((zRTDETRValidator._prepare_pred)r&N)r!r"r#r$r,rErNrQrrrrr 5s  r )r7Zultralytics.datarZultralytics.data.augmentrrrZultralytics.models.yolo.detectrZultralytics.utilsrr__all__r r rrrrs  (