94 lines
4.1 KiB
Python
94 lines
4.1 KiB
Python
# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/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.data.augment import classify_transforms
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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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This predictor handles the specific requirements of classification models, including preprocessing images
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and postprocessing predictions to generate classification results.
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Attributes:
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args (dict): Configuration arguments for the predictor.
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Methods:
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preprocess: Convert input images to model-compatible format.
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postprocess: Process model predictions into Results objects.
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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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Examples:
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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="yolo11n-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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def __init__(self, cfg=DEFAULT_CFG, overrides=None, _callbacks=None):
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"""
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Initialize the ClassificationPredictor with the specified configuration and set task to 'classify'.
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This constructor initializes a ClassificationPredictor instance, which extends BasePredictor for classification
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tasks. It ensures the task is set to 'classify' regardless of input configuration.
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Args:
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cfg (dict): Default configuration dictionary containing prediction settings.
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overrides (dict, optional): Configuration overrides that take precedence over cfg.
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_callbacks (list, optional): List of callback functions to be executed during prediction.
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"""
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super().__init__(cfg, overrides, _callbacks)
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self.args.task = "classify"
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def setup_source(self, source):
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"""Set up source and inference mode and classify transforms."""
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super().setup_source(source)
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updated = (
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self.model.model.transforms.transforms[0].size != max(self.imgsz)
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if hasattr(self.model.model, "transforms") and hasattr(self.model.model.transforms.transforms[0], "size")
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else False
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)
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self.transforms = (
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classify_transforms(self.imgsz) if updated or not self.model.pt else self.model.model.transforms
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)
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def preprocess(self, img):
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"""Convert input images to model-compatible tensor format with appropriate normalization."""
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if not isinstance(img, torch.Tensor):
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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() # Convert uint8 to fp16/32
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def postprocess(self, preds, img, orig_imgs):
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"""
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Process predictions to return Results objects with classification probabilities.
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Args:
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preds (torch.Tensor): Raw predictions from the model.
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img (torch.Tensor): Input images after preprocessing.
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orig_imgs (list[np.ndarray] | torch.Tensor): Original images before preprocessing.
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Returns:
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(list[Results]): List of Results objects containing classification results for each image.
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"""
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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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preds = preds[0] if isinstance(preds, (list, tuple)) else preds
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return [
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Results(orig_img, path=img_path, names=self.model.names, probs=pred)
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for pred, orig_img, img_path in zip(preds, orig_imgs, self.batch[0])
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]
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