a yf @shdZddlmZddlZddlmZddlmZddlm Z ddl m Z dd l m Z Gd d d eZdS) z YOLO-NAS model interface. Example: ```python from ultralytics import NAS model = NAS("yolo_nas_s") results = model.predict("ultralytics/assets/bus.jpg") ``` )PathN)Model)attempt_download_asset) model_info) NASPredictor) NASValidatorcsPeZdZdZdddfdd Zdedddd Zdd d ZeddZ Z 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. Example: ```python from ultralytics import NAS model = NAS("yolo_nas_s") results = model.predict("ultralytics/assets/bus.jpg") ``` Attributes: model (str): Path to the pre-trained model or model name. Defaults to 'yolo_nas_s.pt'. Note: YOLO-NAS models only support pre-trained models. Do not provide YAML configuration files. yolo_nas_s.ptN)returncs*t|jdvsJdtj|dddS)zMInitializes the NAS model with the provided or default 'yolo_nas_s.pt' model.>z.ymlz.yamlz0YOLO-NAS models only support pre-trained models.detect)taskN)rsuffixsuper__init__)selfmodel __class__X/var/www/html/django/DPS/env/lib/python3.9/site-packages/ultralytics/models/nas/model.pyr0sz NAS.__init__)weightsr csddl}t|j}|dkr,tt|_n|dkrH|jjj |dd_fdd}jj j_ |j_ dfd d j_ t d gj_ttjjj_d d j_ij_|j_dj_dS)zgLoads an existing NAS model weights or creates a new NAS model with pretrained weights if not provided.rNz.ptZcoco)Zpretrained_weightscs j|S)z%Ignore additional __call__ arguments.)r_original_forward)xargskwargsrrr new_forwardAszNAS._load..new_forwardTcsjS)N)r)verboserrrIzNAS._load.. cSsdS)NFrrrrrr Lr!r )T)super_gradientsrrtorchloadrrZtrainingmodelsgetforwardrZfuseZtensorZstridedict enumerateZ _class_namesnamesZis_fusedyamlZpt_pathr )rrr r#rrrrr_load5s     z NAS._loadFTcCst|j||ddS)z Logs model info. Args: detailed (bool): Show detailed information about model. verbose (bool): Controls verbosity. i)detailedrZimgsz)rr)rr.rrrrinfoQszNAS.infocCsdttdiS)zQReturns a dictionary mapping tasks to respective predictor and validator classes.r )Z predictor validator)rrrrrrtask_map[sz NAS.task_map)r )N)FT) __name__ __module__ __qualname____doc__rstrr-r/propertyr1 __classcell__rrrrr s  r )r5pathlibrr$Zultralytics.engine.modelrZultralytics.utils.downloadsrZultralytics.utils.torch_utilsrZpredictrvalrr rrrrs