Fi)RddlZddlmZddlmZddlmZddlmZGddeZ y)N) LetterBox) BasePredictor)Results)opsceZdZdZdZdZy)RTDETRPredictoraHRT-DETR (Real-Time Detection Transformer) Predictor extending the BasePredictor class for making predictions. This class leverages Vision Transformers to provide real-time object detection while maintaining high accuracy. It supports key features like efficient hybrid encoding and IoU-aware query selection. Attributes: imgsz (int): Image size for inference (must be square and scale-filled). args (dict): Argument overrides for the predictor. model (torch.nn.Module): The loaded RT-DETR model. batch (list): Current batch of processed inputs. Methods: postprocess: Postprocess raw model predictions to generate bounding boxes and confidence scores. pre_transform: Pre-transform input images before feeding them into the model for inference. Examples: >>> from ultralytics.utils import ASSETS >>> from ultralytics.models.rtdetr import RTDETRPredictor >>> args = dict(model="rtdetr-l.pt", source=ASSETS) >>> predictor = RTDETRPredictor(overrides=args) >>> predictor.predict_cli() c t|ttfs|dg}|djd}|dj d|dz fd\}}t|tst j |ddddf}g}t||||jdD]q\}} } } t j|}| jdd\} } | jd|jjkD}|jjJ| tj |jj| j" k(j%d |z}tj&|| | gd|}||dddfj)d d|jj*}| jdd \}}|ddd gfxx|zcc<|dd d gfxx|zcc<|j-t/| | |j0j2|t|S)aPostprocess the raw predictions from the model to generate bounding boxes and confidence scores. The method filters detections based on confidence and class if specified in `self.args`. It converts model predictions to Results objects containing properly scaled bounding boxes. Args: preds (list | tuple): List of [predictions, extra] from the model, where predictions contain bounding boxes and scores. img (torch.Tensor): Processed input images with shape (N, 3, H, W). orig_imgs (list | torch.Tensor): Original, unprocessed images. Returns: (list[Results]): A list of Results objects containing the post-processed bounding boxes, confidence scores, and class labels. Nr)dim.T)keepdim)device) descending)pathnamesboxes) isinstancelisttupleshapesplitrconvert_torch2numpy_batchzipbatch xywh2xyxymaxsqueezeargsconfclassestorchtensorranycatargsortmax_detappendrmodelr)selfpredsimg orig_imgsndbboxesscoresresultsbboxscoreorig_imgimg_path max_scoreclsidxpredohows c/var/www/pod-logistic/pod-ai/venv/lib/python3.12/site-packages/ultralytics/models/rtdetr/predict.py postprocesszRTDETRPredictor.postprocess#s %$/DME 1X^^B qBF <)T*55i@ddKI/2669djjYZm/\ a +D%8==&D"YYr4Y8NIs##B'$))..8Cyy  ,ell499+<+ pre_transformzRTDETRPredictor.pre_transformKs/djjuF ,./q "///s0N)__name__ __module__ __qualname____doc__r?rIr@r>rr s.&P 0r@r) r$ultralytics.data.augmentrultralytics.engine.predictorrultralytics.engine.resultsrultralytics.utilsrrrNr@r>rSs$ .6.!L0mL0r@