์ฝ˜ํ…์ธ ๋กœ ๊ฑด๋„ˆ๋›ฐ๊ธฐ

์ฐธ์กฐ ultralytics/models/yolo/classify/predict.py

์ฐธ๊ณ 

์ด ํŒŒ์ผ์€ https://github.com/ultralytics/ ultralytics/blob/main/ ultralytics/models/ yolo/classify/predict .py์—์„œ ํ™•์ธํ•  ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค. ๋ฌธ์ œ๋ฅผ ๋ฐœ๊ฒฌํ•˜๋ฉด ํ’€ ๋ฆฌํ€˜์ŠคํŠธ (๐Ÿ› ๏ธ) ๋ฅผ ํ†ตํ•ด ๋ฌธ์ œ๋ฅผ ํ•ด๊ฒฐํ•˜๋„๋ก ๋„์™€์ฃผ์„ธ์š”. ๊ฐ์‚ฌํ•ฉ๋‹ˆ๋‹ค ๐Ÿ™!



ultralytics.models.yolo.classify.predict.ClassificationPredictor

๋ฒ ์ด์Šค: BasePredictor

๋ถ„๋ฅ˜ ๋ชจ๋ธ์— ๊ธฐ๋ฐ˜ํ•œ ์˜ˆ์ธก์„ ์œ„ํ•ด BasePredictor ํด๋ž˜์Šค๋ฅผ ํ™•์žฅํ•œ ํด๋ž˜์Šค์ž…๋‹ˆ๋‹ค.

์ฐธ๊ณ 
  • ํ† ์น˜๋น„์ „ ๋ถ„๋ฅ˜ ๋ชจ๋ธ์€ 'model' ์ธ์ˆ˜์— ์ „๋‹ฌํ•  ์ˆ˜๋„ ์žˆ์Šต๋‹ˆ๋‹ค(์˜ˆ: model='resnet18').
์˜ˆ
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()
์˜ ์†Œ์Šค ์ฝ”๋“œ 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)

์ž‘์—…์„ '๋ถ„๋ฅ˜'๋กœ ์„ค์ •ํ•œ ClassificationPredictor๋ฅผ ์ดˆ๊ธฐํ™”ํ•ฉ๋‹ˆ๋‹ค.

์˜ ์†Œ์Šค ์ฝ”๋“œ 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)

์˜ˆ์ธก์„ ํ›„์ฒ˜๋ฆฌํ•˜์—ฌ ๊ฒฐ๊ณผ ๊ฐœ์ฒด๋ฅผ ๋ฐ˜ํ™˜ํ•ฉ๋‹ˆ๋‹ค.

์˜ ์†Œ์Šค ์ฝ”๋“œ 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)

์ž…๋ ฅ ์ด๋ฏธ์ง€๋ฅผ ๋ชจ๋ธ ํ˜ธํ™˜ ๋ฐ์ดํ„ฐ ์œ ํ˜•์œผ๋กœ ๋ณ€ํ™˜ํ•ฉ๋‹ˆ๋‹ค.

์˜ ์†Œ์Šค ์ฝ”๋“œ 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





Created 2023-11-12, Updated 2024-06-02
Authors: glenn-jocher (5), Burhan-Q (1)