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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. | |
| This predictor handles the specific requirements of classification models, including preprocessing images | |
| and postprocessing predictions to generate classification results. | |
| Attributes: | |
| args (dict): Configuration arguments for the predictor. | |
| _legacy_transform_name (str): Name of the legacy transform class for backward compatibility. | |
| Methods: | |
| preprocess: Convert input images to model-compatible format. | |
| postprocess: Process model predictions into Results objects. | |
| Notes: | |
| - Torchvision classification models can also be passed to the 'model' argument, i.e. model='resnet18'. | |
| Examples: | |
| >>> from ultralytics.utils import ASSETS | |
| >>> from ultralytics.models.yolo.classify import ClassificationPredictor | |
| >>> args = dict(model="yolo11n-cls.pt", source=ASSETS) | |
| >>> predictor = ClassificationPredictor(overrides=args) | |
| >>> predictor.predict_cli() | |
| """ | |
| def __init__(self, cfg=DEFAULT_CFG, overrides=None, _callbacks=None): | |
| """Initialize the ClassificationPredictor with the specified configuration and set 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): | |
| """Convert input images to model-compatible tensor format with appropriate normalization.""" | |
| 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): | |
| """ | |
| Process predictions to return Results objects with classification probabilities. | |
| Args: | |
| preds (torch.Tensor): Raw predictions from the model. | |
| img (torch.Tensor): Input images after preprocessing. | |
| orig_imgs (List[np.ndarray] | torch.Tensor): Original images before preprocessing. | |
| Returns: | |
| (List[Results]): List of Results objects containing classification results for each image. | |
| """ | |
| 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]) | |
| ] | |