h?SSKJr SSKJr SSKJr SSKrSSKJr SSK J r SSK J r SSK Jr SS KJr S S KJr S S KJr "S S\5rg)) annotations)Path)AnyN)Model)DEFAULT_CFG_DICT)attempt_download_asset) torch_load) model_info) NASPredictor) NASValidatorcd^\rSrSrSrSS U4SjjjrS S SjjrS S Sjjr\SSj5r Sr U=r $)NASa_ YOLO-NAS model for object detection. This class provides an interface for the YOLO-NAS models and extends the `Model` class from Ultralytics engine. It is designed to facilitate the task of object detection using pre-trained or custom-trained YOLO-NAS models. Attributes: model (torch.nn.Module): The loaded YOLO-NAS model. task (str): The task type for the model, defaults to 'detect'. predictor (NASPredictor): The predictor instance for making predictions. validator (NASValidator): The validator instance for model validation. Methods: info: Log model information and return model details. Examples: >>> from ultralytics import NAS >>> model = NAS("yolo_nas_s") >>> results = model.predict("ultralytics/assets/bus.jpg") Notes: YOLO-NAS models only support pre-trained models. Do not provide YAML configuration files. cb>[U5RS;dS5e[TU] USS9 g)z.yml.yamlz0YOLO-NAS models only support pre-trained models.detect)taskN)rsuffixsuper__init__)selfmodel __class__s V/home/james-whalen/.local/lib/python3.13/site-packages/ultralytics/models/nas/model.pyr NAS.__init__-s4E{!!)::nTRRU5$)z%Ignore additional __call__ arguments.)r_original_forward)xargskwargsrs r new_forwardNAS._load..new_forwardCs:://2 2rc>TR$Nr)verbosers rNAS._load..Ks tzzr cg)NFr2rrr.r/Nserr)T)super_gradientsrrr rrtrainingmodelsgetforwardr$fusetorchtensorstridedict enumerate _class_namesnamesis_fusedyamlpt_pathrr overridesr&eval)rweightsrr3rr(s` r_load NAS._load2s g%% U?#$:7$CDDJ r\(1188<rhs8# *.>04!QR%QRr