ultralytics 8.0.143 add Model base class (#3934)
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
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@ -13,105 +13,36 @@ from pathlib import Path
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import torch
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from ultralytics.cfg import get_cfg
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from ultralytics.engine.exporter import Exporter
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from ultralytics.utils import DEFAULT_CFG, DEFAULT_CFG_DICT, LOGGER, ROOT, is_git_dir
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from ultralytics.utils.checks import check_imgsz
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from ultralytics.engine.model import Model
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from ultralytics.utils.torch_utils import model_info, smart_inference_mode
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from .predict import NASPredictor
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from .val import NASValidator
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class NAS:
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class NAS(Model):
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def __init__(self, model='yolo_nas_s.pt') -> None:
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assert Path(model).suffix != '.yaml', 'YOLO-NAS models only support pre-trained models.'
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super().__init__(model, task='detect')
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@smart_inference_mode()
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def _load(self, weights: str, task: str):
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# Load or create new NAS model
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import super_gradients
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self.predictor = None
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suffix = Path(model).suffix
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suffix = Path(weights).suffix
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if suffix == '.pt':
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self._load(model)
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self.model = torch.load(weights)
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elif suffix == '':
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self.model = super_gradients.training.models.get(model, pretrained_weights='coco')
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self.task = 'detect'
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self.model.args = DEFAULT_CFG_DICT # attach args to model
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self.model = super_gradients.training.models.get(weights, pretrained_weights='coco')
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# Standardize model
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self.model.fuse = lambda verbose=True: self.model
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self.model.stride = torch.tensor([32])
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self.model.names = dict(enumerate(self.model._class_names))
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self.model.is_fused = lambda: False # for info()
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self.model.yaml = {} # for info()
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self.model.pt_path = model # for export()
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self.model.pt_path = weights # for export()
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self.model.task = 'detect' # for export()
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self.info()
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@smart_inference_mode()
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def _load(self, weights: str):
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self.model = torch.load(weights)
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@smart_inference_mode()
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def predict(self, source=None, stream=False, **kwargs):
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"""
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Perform prediction using the YOLO model.
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Args:
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source (str | int | PIL | np.ndarray): The source of the image to make predictions on.
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Accepts all source types accepted by the YOLO model.
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stream (bool): Whether to stream the predictions or not. Defaults to False.
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**kwargs : Additional keyword arguments passed to the predictor.
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Check the 'configuration' section in the documentation for all available options.
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Returns:
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(List[ultralytics.engine.results.Results]): The prediction results.
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"""
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if source is None:
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source = ROOT / 'assets' if is_git_dir() else 'https://ultralytics.com/images/bus.jpg'
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LOGGER.warning(f"WARNING ⚠️ 'source' is missing. Using 'source={source}'.")
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overrides = dict(conf=0.25, task='detect', mode='predict')
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overrides.update(kwargs) # prefer kwargs
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if not self.predictor:
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self.predictor = NASPredictor(overrides=overrides)
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self.predictor.setup_model(model=self.model)
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else: # only update args if predictor is already setup
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self.predictor.args = get_cfg(self.predictor.args, overrides)
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return self.predictor(source, stream=stream)
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def train(self, **kwargs):
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"""Function trains models but raises an error as NAS models do not support training."""
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raise NotImplementedError("NAS models don't support training")
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def val(self, **kwargs):
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"""Run validation given dataset."""
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overrides = dict(task='detect', mode='val')
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overrides.update(kwargs) # prefer kwargs
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args = get_cfg(cfg=DEFAULT_CFG, overrides=overrides)
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args.imgsz = check_imgsz(args.imgsz, max_dim=1)
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validator = NASValidator(args=args)
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validator(model=self.model)
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self.metrics = validator.metrics
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return validator.metrics
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@smart_inference_mode()
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def export(self, **kwargs):
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"""
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Export model.
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Args:
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**kwargs : Any other args accepted by the predictors. To see all args check 'configuration' section in docs
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"""
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overrides = dict(task='detect')
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overrides.update(kwargs)
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overrides['mode'] = 'export'
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args = get_cfg(cfg=DEFAULT_CFG, overrides=overrides)
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args.task = self.task
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if args.imgsz == DEFAULT_CFG.imgsz:
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args.imgsz = self.model.args['imgsz'] # use trained imgsz unless custom value is passed
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if args.batch == DEFAULT_CFG.batch:
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args.batch = 1 # default to 1 if not modified
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return Exporter(overrides=args)(model=self.model)
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def info(self, detailed=False, verbose=True):
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"""
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@ -123,11 +54,6 @@ class NAS:
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"""
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return model_info(self.model, detailed=detailed, verbose=verbose, imgsz=640)
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def __call__(self, source=None, stream=False, **kwargs):
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"""Calls the 'predict' function with given arguments to perform object detection."""
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return self.predict(source, stream, **kwargs)
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def __getattr__(self, attr):
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"""Raises error if object has no requested attribute."""
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name = self.__class__.__name__
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raise AttributeError(f"'{name}' object has no attribute '{attr}'. See valid attributes below.\n{self.__doc__}")
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@property
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def task_map(self):
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return {'detect': {'predictor': NASPredictor, 'validator': NASValidator}}
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