ultralytics 8.0.158 add benchmarks to coverage (#4432)

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Yonghye Kwon <developer.0hye@gmail.com>

ultralytics/models/yolo/detect/__init__.py

@@ -1,7 +1,7 @@
 # Ultralytics YOLO 🚀, AGPL-3.0 license
 
-from .predict import DetectionPredictor, predict
-from .train import DetectionTrainer, train
-from .val import DetectionValidator, val
+from .predict import DetectionPredictor
+from .train import DetectionTrainer
+from .val import DetectionValidator
 
-__all__ = 'DetectionPredictor', 'predict', 'DetectionTrainer', 'train', 'DetectionValidator', 'val'
+__all__ = 'DetectionPredictor', 'DetectionTrainer', 'DetectionValidator'

ultralytics/models/yolo/detect/predict.py

@@ -4,10 +4,23 @@ import torch
 
 from ultralytics.engine.predictor import BasePredictor
 from ultralytics.engine.results import Results
-from ultralytics.utils import ASSETS, DEFAULT_CFG, ops
+from ultralytics.utils import ops
 
 
 class DetectionPredictor(BasePredictor):
+    """
+    A class extending the BasePredictor class for prediction based on a detection model.
+
+    Example:
+        ```python
+        from ultralytics.utils import ASSETS
+        from ultralytics.models.yolo.detect import DetectionPredictor
+
+        args = dict(model='yolov8n.pt', source=ASSETS)
+        predictor = DetectionPredictor(overrides=args)
+        predictor.predict_cli()
+        ```
+    """
 
     def postprocess(self, preds, img, orig_imgs):
         """Post-processes predictions and returns a list of Results objects."""
@@ -27,21 +40,3 @@ class DetectionPredictor(BasePredictor):
             img_path = path[i] if isinstance(path, list) else path
             results.append(Results(orig_img=orig_img, path=img_path, names=self.model.names, boxes=pred))
         return results
-
-
-def predict(cfg=DEFAULT_CFG, use_python=False):
-    """Runs YOLO model inference on input image(s)."""
-    model = cfg.model or 'yolov8n.pt'
-    source = cfg.source or ASSETS
-
-    args = dict(model=model, source=source)
-    if use_python:
-        from ultralytics import YOLO
-        YOLO(model)(**args)
-    else:
-        predictor = DetectionPredictor(overrides=args)
-        predictor.predict_cli()
-
-
-if __name__ == '__main__':
-    predict()

ultralytics/models/yolo/detect/train.py

@@ -8,12 +8,24 @@ from ultralytics.data import build_dataloader, build_yolo_dataset
 from ultralytics.engine.trainer import BaseTrainer
 from ultralytics.models import yolo
 from ultralytics.nn.tasks import DetectionModel
-from ultralytics.utils import DEFAULT_CFG, LOGGER, RANK
+from ultralytics.utils import LOGGER, RANK
 from ultralytics.utils.plotting import plot_images, plot_labels, plot_results
 from ultralytics.utils.torch_utils import de_parallel, torch_distributed_zero_first
 
 
 class DetectionTrainer(BaseTrainer):
+    """
+    A class extending the BaseTrainer class for training based on a detection model.
+
+    Example:
+        ```python
+        from ultralytics.models.yolo.detect import DetectionTrainer
+
+        args = dict(model='yolov8n.pt', data='coco8.yaml', epochs=3)
+        trainer = DetectionTrainer(overrides=args)
+        trainer.train()
+        ```
+    """
 
     def build_dataset(self, img_path, mode='train', batch=None):
         """
@@ -102,22 +114,3 @@ class DetectionTrainer(BaseTrainer):
         boxes = np.concatenate([lb['bboxes'] for lb in self.train_loader.dataset.labels], 0)
         cls = np.concatenate([lb['cls'] for lb in self.train_loader.dataset.labels], 0)
         plot_labels(boxes, cls.squeeze(), names=self.data['names'], save_dir=self.save_dir, on_plot=self.on_plot)
-
-
-def train(cfg=DEFAULT_CFG, use_python=False):
-    """Train and optimize YOLO model given training data and device."""
-    model = cfg.model or 'yolov8n.pt'
-    data = cfg.data or 'coco8.yaml'  # or yolo.ClassificationDataset("mnist")
-    device = cfg.device if cfg.device is not None else ''
-
-    args = dict(model=model, data=data, device=device)
-    if use_python:
-        from ultralytics import YOLO
-        YOLO(model).train(**args)
-    else:
-        trainer = DetectionTrainer(overrides=args)
-        trainer.train()
-
-
-if __name__ == '__main__':
-    train()

ultralytics/models/yolo/detect/val.py

@@ -8,7 +8,7 @@ import torch
 
 from ultralytics.data import build_dataloader, build_yolo_dataset, converter
 from ultralytics.engine.validator import BaseValidator
-from ultralytics.utils import DEFAULT_CFG, LOGGER, ops
+from ultralytics.utils import LOGGER, ops
 from ultralytics.utils.checks import check_requirements
 from ultralytics.utils.metrics import ConfusionMatrix, DetMetrics, box_iou
 from ultralytics.utils.plotting import output_to_target, plot_images
@@ -16,6 +16,18 @@ from ultralytics.utils.torch_utils import de_parallel
 
 
 class DetectionValidator(BaseValidator):
+    """
+    A class extending the BaseValidator class for validation based on a detection model.
+
+    Example:
+        ```python
+        from ultralytics.models.yolo.detect import DetectionValidator
+
+        args = dict(model='yolov8n.pt', data='coco8.yaml')
+        validator = DetectionValidator(args=args)
+        validator(model=args['model'])
+        ```
+    """
 
     def __init__(self, dataloader=None, save_dir=None, pbar=None, args=None, _callbacks=None):
         """Initialize detection model with necessary variables and settings."""
@@ -254,21 +266,3 @@ class DetectionValidator(BaseValidator):
             except Exception as e:
                 LOGGER.warning(f'pycocotools unable to run: {e}')
         return stats
-
-
-def val(cfg=DEFAULT_CFG, use_python=False):
-    """Validate trained YOLO model on validation dataset."""
-    model = cfg.model or 'yolov8n.pt'
-    data = cfg.data or 'coco8.yaml'
-
-    args = dict(model=model, data=data)
-    if use_python:
-        from ultralytics import YOLO
-        YOLO(model).val(**args)
-    else:
-        validator = DetectionValidator(args=args)
-        validator(model=args['model'])
-
-
-if __name__ == '__main__':
-    val()