/jOddlmZddlmZddlmZddlZddlmZddl m Z ddl m Z ddl mZdd lmZd d lmZd d lmZGd deZdS)) annotations)Path)AnyN)Model)DEFAULT_CFG_DICT)attempt_download_asset) torch_load) model_info) NASPredictor) NASValidatorcReZdZdZddfd Zddd ZdddZeddZxZ S)NASaYOLO-NAS model for object detection. This class provides an interface for the YOLO-NAS models and extends the `Model` class from Ultralytics engine. It is designed to facilitate the task of object detection using pre-trained or custom-trained YOLO-NAS models. Attributes: model (torch.nn.Module): The loaded YOLO-NAS model. task (str): The task type for the model, defaults to 'detect'. predictor (NASPredictor): The predictor instance for making predictions. validator (NASValidator): The validator instance for model validation. Methods: info: Log model information and return model details. Examples: >>> from ultralytics import NAS >>> model = NAS("yolo_nas_s") >>> results = model.predict("ultralytics/assets/bus.jpg") Notes: YOLO-NAS models only support pre-trained models. Do not provide YAML configuration files. yolo_nas_s.ptmodelstrreturnNonect|jdvs Jdt|ddS)z.yml.yamlz0YOLO-NAS models only support pre-trained models.detect)taskN)rsuffixsuper__init__)selfr __class__s a/home/longshao/multi-rider-rag/.venv/lib/python3.11/site-packages/ultralytics/models/nas/model.pyrz NAS.__init__,sJE{{!)::::.new_forwardAs://22 2r TcjSN)r)verbosers rzNAS._load..Is tzr cdS)NFr2r rr/zNAS._load..Lser r)T)super_gradientsrrr rrtrainingmodelsgetforwardr'fusetorchtensorstridedict enumerate _class_namesnamesis_fusedyamlrpt_pathrr overridesr)eval)rr!rr3rr+s` r_loadz NAS._load1sH g% U??#$:7$C$CDDDJJ r\\(18<ras #""""" ******......>>>>>>000000444444!!!!!!NRNRNRNRNR%NRNRNRNRNRr