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# Ultralytics YOLO 🚀, AGPL-3.0 license |
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""" |
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YOLO-NAS model interface. |
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Usage - Predict: |
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from ultralytics import NAS |
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model = NAS('yolo_nas_s') |
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results = model.predict('ultralytics/assets/bus.jpg') |
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""" |
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from pathlib import Path |
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import torch |
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from ultralytics.yolo.cfg import get_cfg |
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from ultralytics.yolo.engine.exporter import Exporter |
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from ultralytics.yolo.utils import DEFAULT_CFG, DEFAULT_CFG_DICT, LOGGER, ROOT, is_git_dir |
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from ultralytics.yolo.utils.checks import check_imgsz |
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from ...yolo.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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def __init__(self, model='yolo_nas_s.pt') -> None: |
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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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if suffix == '.pt': |
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self._load(model) |
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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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# 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.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.yolo.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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Logs model info. |
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Args: |
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detailed (bool): Show detailed information about model. |
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verbose (bool): Controls verbosity. |
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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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