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49 lines
1.8 KiB
49 lines
1.8 KiB
# Ultralytics YOLO 🚀, AGPL-3.0 license
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import torch
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from ultralytics.engine.predictor import BasePredictor
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from ultralytics.engine.results import Results
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from ultralytics.utils import DEFAULT_CFG
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class ClassificationPredictor(BasePredictor):
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"""
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A class extending the BasePredictor class for prediction based on a classification model.
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Notes:
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- Torchvision classification models can also be passed to the 'model' argument, i.e. model='resnet18'.
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Example:
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```python
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from ultralytics.utils import ASSETS
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from ultralytics.models.yolo.classify import ClassificationPredictor
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args = dict(model='yolov8n-cls.pt', source=ASSETS)
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predictor = ClassificationPredictor(overrides=args)
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predictor.predict_cli()
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```
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"""
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def __init__(self, cfg=DEFAULT_CFG, overrides=None, _callbacks=None):
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super().__init__(cfg, overrides, _callbacks)
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self.args.task = 'classify'
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def preprocess(self, img):
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"""Converts input image to model-compatible data type."""
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if not isinstance(img, torch.Tensor):
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img = torch.stack([self.transforms(im) for im in img], dim=0)
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img = (img if isinstance(img, torch.Tensor) else torch.from_numpy(img)).to(self.model.device)
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return img.half() if self.model.fp16 else img.float() # uint8 to fp16/32
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def postprocess(self, preds, img, orig_imgs):
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"""Post-processes predictions to return Results objects."""
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results = []
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for i, pred in enumerate(preds):
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orig_img = orig_imgs[i] if isinstance(orig_imgs, list) else orig_imgs
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path = self.batch[0]
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img_path = path[i] if isinstance(path, list) else path
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results.append(Results(orig_img=orig_img, path=img_path, names=self.model.names, probs=pred))
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return results
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