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https://github.com/THU-MIG/yolov10.git
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fe27db2f6e
Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com> Co-authored-by: UltralyticsAssistant <web@ultralytics.com> Co-authored-by: Burhan <62214284+Burhan-Q@users.noreply.github.com> Co-authored-by: Kayzwer <68285002+Kayzwer@users.noreply.github.com>
62 lines
2.5 KiB
Python
62 lines
2.5 KiB
Python
# Ultralytics YOLO 🚀, AGPL-3.0 license
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import cv2
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import torch
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from PIL import Image
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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, ops
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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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"""Initializes ClassificationPredictor setting the task to 'classify'."""
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super().__init__(cfg, overrides, _callbacks)
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self.args.task = "classify"
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self._legacy_transform_name = "ultralytics.yolo.data.augment.ToTensor"
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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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is_legacy_transform = any(
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self._legacy_transform_name in str(transform) for transform in self.transforms.transforms
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)
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if is_legacy_transform: # to handle legacy transforms
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img = torch.stack([self.transforms(im) for im in img], dim=0)
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else:
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img = torch.stack(
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[self.transforms(Image.fromarray(cv2.cvtColor(im, cv2.COLOR_BGR2RGB))) for im in img], dim=0
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)
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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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if not isinstance(orig_imgs, list): # input images are a torch.Tensor, not a list
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orig_imgs = ops.convert_torch2numpy_batch(orig_imgs)
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results = []
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for i, pred in enumerate(preds):
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orig_img = orig_imgs[i]
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img_path = self.batch[0][i]
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results.append(Results(orig_img, path=img_path, names=self.model.names, probs=pred))
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return results
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