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| # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license | |
| import cv2 | |
| import torch | |
| from PIL import Image | |
| from ultralytics.engine.predictor import BasePredictor | |
| from ultralytics.engine.results import Results | |
| from ultralytics.utils import DEFAULT_CFG, ops | |
| 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) | |
| preds = preds[0] if isinstance(preds, (list, tuple)) else preds | |
| return [ | |
| Results(orig_img, path=img_path, names=self.model.names, probs=pred) | |
| for pred, orig_img, img_path in zip(preds, orig_imgs, self.batch[0]) | |
| ] | |