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ultralytics/models/yolo/classify/predict.py

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+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+import cv2
+import torch
+from PIL import Image
+
+from ultralytics.data.augment import classify_transforms
+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.
+
+    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'.
+
+        This constructor initializes a ClassificationPredictor instance, which extends BasePredictor for classification
+        tasks. It ensures the task is set to 'classify' regardless of input configuration.
+
+        Args:
+            cfg (dict): Default configuration dictionary containing prediction settings.
+            overrides (dict, optional): Configuration overrides that take precedence over cfg.
+            _callbacks (list, optional): List of callback functions to be executed during prediction.
+        """
+        super().__init__(cfg, overrides, _callbacks)
+        self.args.task = "classify"
+
+    def setup_source(self, source):
+        """Set up source and inference mode and classify transforms."""
+        super().setup_source(source)
+        updated = (
+            self.model.model.transforms.transforms[0].size != max(self.imgsz)
+            if hasattr(self.model.model, "transforms") and hasattr(self.model.model.transforms.transforms[0], "size")
+            else False
+        )
+        self.transforms = (
+            classify_transforms(self.imgsz) if updated or not self.model.pt else self.model.model.transforms
+        )
+
+    def preprocess(self, img):
+        """Convert input images to model-compatible tensor format with appropriate normalization."""
+        if not isinstance(img, torch.Tensor):
+            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()  # Convert 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])
+        ]