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Referencia para ultralytics/models/yolo/classify/predict.py

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Este archivo est谩 disponible en https://github.com/ultralytics/ ultralytics/blob/main/ ultralytics/models/ yolo/classify/predict .py. Si detectas alg煤n problema, por favor, ayuda a solucionarlo contribuyendo con una Pull Request 馃洜锔. 隆Gracias 馃檹!



ultralytics.models.yolo.classify.predict.ClassificationPredictor

Bases: BasePredictor

Una clase que ampl铆a la clase BasePredictor para la predicci贸n basada en un modelo de clasificaci贸n.

Notas
  • Los modelos de clasificaci贸n de Torchvision tambi茅n se pueden pasar al argumento 'modelo', es decir, model='resnet18'.
Ejemplo
from ultralytics.utils import ASSETS
from ultralytics.models.yolo.classify import ClassificationPredictor

args = dict(model='yolov8n-cls.pt', source=ASSETS)
predictor = ClassificationPredictor(overrides=args)
predictor.predict_cli()
C贸digo fuente en ultralytics/models/yolo/classify/predict.py
class ClassificationPredictor(BasePredictor):
    """
    A class extending the BasePredictor class for prediction based on a classification model.

    Notes:
        - Torchvision classification models can also be passed to the 'model' argument, i.e. model='resnet18'.

    Example:
        ```python
        from ultralytics.utils import ASSETS
        from ultralytics.models.yolo.classify import ClassificationPredictor

        args = dict(model='yolov8n-cls.pt', source=ASSETS)
        predictor = ClassificationPredictor(overrides=args)
        predictor.predict_cli()
        ```
    """

    def __init__(self, cfg=DEFAULT_CFG, overrides=None, _callbacks=None):
        """Initializes ClassificationPredictor setting the task to 'classify'."""
        super().__init__(cfg, overrides, _callbacks)
        self.args.task = "classify"
        self._legacy_transform_name = "ultralytics.yolo.data.augment.ToTensor"

    def preprocess(self, img):
        """Converts input image to model-compatible data type."""
        if not isinstance(img, torch.Tensor):
            is_legacy_transform = any(
                self._legacy_transform_name in str(transform) for transform in self.transforms.transforms
            )
            if is_legacy_transform:  # to handle legacy transforms
                img = torch.stack([self.transforms(im) for im in img], dim=0)
            else:
                img = torch.stack(
                    [self.transforms(Image.fromarray(cv2.cvtColor(im, cv2.COLOR_BGR2RGB))) for im in img], dim=0
                )
        img = (img if isinstance(img, torch.Tensor) else torch.from_numpy(img)).to(self.model.device)
        return img.half() if self.model.fp16 else img.float()  # uint8 to fp16/32

    def postprocess(self, preds, img, orig_imgs):
        """Post-processes predictions to return Results objects."""
        if not isinstance(orig_imgs, list):  # input images are a torch.Tensor, not a list
            orig_imgs = ops.convert_torch2numpy_batch(orig_imgs)

        results = []
        for i, pred in enumerate(preds):
            orig_img = orig_imgs[i]
            img_path = self.batch[0][i]
            results.append(Results(orig_img, path=img_path, names=self.model.names, probs=pred))
        return results

__init__(cfg=DEFAULT_CFG, overrides=None, _callbacks=None)

Inicializa el ClassificationPredictor estableciendo la tarea como 'clasificar'.

C贸digo fuente en ultralytics/models/yolo/classify/predict.py
def __init__(self, cfg=DEFAULT_CFG, overrides=None, _callbacks=None):
    """Initializes ClassificationPredictor setting the task to 'classify'."""
    super().__init__(cfg, overrides, _callbacks)
    self.args.task = "classify"
    self._legacy_transform_name = "ultralytics.yolo.data.augment.ToTensor"

postprocess(preds, img, orig_imgs)

Postprocesa las predicciones para devolver objetos Resultados.

C贸digo fuente en ultralytics/models/yolo/classify/predict.py
def postprocess(self, preds, img, orig_imgs):
    """Post-processes predictions to return Results objects."""
    if not isinstance(orig_imgs, list):  # input images are a torch.Tensor, not a list
        orig_imgs = ops.convert_torch2numpy_batch(orig_imgs)

    results = []
    for i, pred in enumerate(preds):
        orig_img = orig_imgs[i]
        img_path = self.batch[0][i]
        results.append(Results(orig_img, path=img_path, names=self.model.names, probs=pred))
    return results

preprocess(img)

Convierte la imagen de entrada en un tipo de datos compatible con el modelo.

C贸digo fuente en ultralytics/models/yolo/classify/predict.py
def preprocess(self, img):
    """Converts input image to model-compatible data type."""
    if not isinstance(img, torch.Tensor):
        is_legacy_transform = any(
            self._legacy_transform_name in str(transform) for transform in self.transforms.transforms
        )
        if is_legacy_transform:  # to handle legacy transforms
            img = torch.stack([self.transforms(im) for im in img], dim=0)
        else:
            img = torch.stack(
                [self.transforms(Image.fromarray(cv2.cvtColor(im, cv2.COLOR_BGR2RGB))) for im in img], dim=0
            )
    img = (img if isinstance(img, torch.Tensor) else torch.from_numpy(img)).to(self.model.device)
    return img.half() if self.model.fp16 else img.float()  # uint8 to fp16/32





Creado 2023-11-12, Actualizado 2024-05-18
Autores: glenn-jocher (4), Burhan-Q (1)