§ —/j„ ãóB—ddlZddlmZddlmZGd„de¦«ZdS)éN)ÚDetectionPredictor)Úopscó"‡—eZdZdZˆfd„ZˆxZS)Ú NASPredictoraBUltralytics YOLO NAS Predictor for object detection. This class extends the DetectionPredictor from Ultralytics engine and is responsible for post-processing the raw predictions generated by the YOLO NAS models. It applies operations like non-maximum suppression and scaling the bounding boxes to fit the original image dimensions. Attributes: args (Namespace): Namespace containing various configurations for post-processing including confidence threshold, IoU threshold, agnostic NMS flag, maximum detections, and class filtering options. model (torch.nn.Module): The YOLO NAS model used for inference. batch (list): Batch of inputs for processing. Examples: >>> from ultralytics import NAS >>> model = NAS("yolo_nas_s") >>> predictor = model.predictor Assume that raw_preds, img, orig_imgs are available >>> results = predictor.postprocess(raw_preds, img, orig_imgs) Notes: Typically, this class is not instantiated directly. It is used internally within the NAS class. cóú•—tj|dd¦«}tj||ddfd¦« ddd¦«}t ¦« |||¦«S)aPostprocess NAS model predictions to generate final detection results. This method takes raw predictions from a YOLO NAS model, converts bounding box formats, and applies post-processing operations to generate the final detection results compatible with Ultralytics result visualization and analysis tools. Args: preds_in (list): Raw predictions from the NAS model, typically containing bounding boxes and class scores. img (torch.Tensor): Input image tensor that was fed to the model, with shape (B, C, H, W). orig_imgs (list | torch.Tensor | np.ndarray): Original images before preprocessing, used for scaling coordinates back to original dimensions. Returns: (list): List of Results objects containing the processed predictions for each image in the batch. Examples: >>> predictor = NAS("yolo_nas_s").predictor >>> results = predictor.postprocess(raw_preds, img, orig_imgs) rééÿÿÿÿé)rÚ xyxy2xywhÚtorchÚcatÚpermuteÚsuperÚ postprocess)ÚselfÚpreds_inÚimgÚ orig_imgsÚboxesÚpredsÚ __class__s €úc/home/longshao/multi-rider-rag/.venv/lib/python3.11/site-packages/ultralytics/models/nas/predict.pyrzNASPredictor.postprocess"shø€õ(” ˜h qœk¨!œnÑ-Ô-ˆÝ” ˜5 (¨1¤+¨a¤.Ð1°2Ñ6Ô6×>Ò>¸qÀ!ÀQÑGÔGˆÝ‰wŒw×"Ò" 5¨#¨yÑ9Ô9Ð9ó)Ú__name__Ú __module__Ú __qualname__Ú__doc__rÚ __classcell__)rs@rrr sBø€€€€€ððð0:ð:ð:ð:ð:ð:ð:ð:ð:rr)r Ú&ultralytics.models.yolo.detect.predictrÚultralytics.utilsrr©rrúr"siðð € € € àEÐEÐEÐEÐEÐEØ!Ð!Ð!Ð!Ð!Ð!ð/:ð/:ð/:ð/:ð/:Ð%ñ/:ô/:ð/:ð/:ð/:r