.h?ddlmZddlmZddlmZddlZddlmZddl m Z ddl m Z ddl mZdd lmZd d lmZd d lmZGd deZy)) annotations)Path)AnyN)Model)DEFAULT_CFG_DICT)attempt_download_asset) torch_load) model_info) NASPredictor) NASValidatorcLeZdZdZddfd Zdd dZd d dZed dZxZ S) NASa YOLO-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. cdt|jdvsJdt| |dy)z.yml.yamlz0YOLO-NAS models only support pre-trained models.detect)taskN)rsuffixsuper__init__)selfmodel __class__s Z/var/www/html/ai-service/venv/lib/python3.12/site-packages/ultralytics/models/nas/model.pyrz NAS.__init__-s4E{!!)::n.new_forwardCs:://2 2rcjSN)r)verbosers rzNAS._load..Ks tzzr cy)NFr-rrr*zNAS._load..Nsrr)T)super_gradientsrrr rrtrainingmodelsgetforwardr"fusetorchtensorstridedict enumerate _class_namesnamesis_fusedyamlpt_pathrr overridesr$eval)rweightsrr.rr&s` r_loadz NAS._load2s g%% U?#$:7$CDDJ r\(1188<r^s8# *.>04!QR%QRr