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/segment/__init__.py

@@ -1,7 +1,7 @@
 # Ultralytics YOLO 🚀, AGPL-3.0 license
 
-from .predict import SegmentationPredictor, predict
-from .train import SegmentationTrainer, train
-from .val import SegmentationValidator, val
+from .predict import SegmentationPredictor
+from .train import SegmentationTrainer
+from .val import SegmentationValidator
 
-__all__ = 'SegmentationPredictor', 'predict', 'SegmentationTrainer', 'train', 'SegmentationValidator', 'val'
+__all__ = 'SegmentationPredictor', 'SegmentationTrainer', 'SegmentationValidator'

ultralytics/models/yolo/segment/predict.py

@@ -4,10 +4,23 @@ import torch
 
 from ultralytics.engine.results import Results
 from ultralytics.models.yolo.detect.predict import DetectionPredictor
-from ultralytics.utils import ASSETS, DEFAULT_CFG, ops
+from ultralytics.utils import DEFAULT_CFG, ops
 
 
 class SegmentationPredictor(DetectionPredictor):
+    """
+    A class extending the DetectionPredictor class for prediction based on a segmentation model.
+
+    Example:
+        ```python
+        from ultralytics.utils import ASSETS
+        from ultralytics.models.yolo.segment import SegmentationPredictor
+
+        args = dict(model='yolov8n-seg.pt', source=ASSETS)
+        predictor = SegmentationPredictor(overrides=args)
+        predictor.predict_cli()
+        ```
+    """
 
     def __init__(self, cfg=DEFAULT_CFG, overrides=None, _callbacks=None):
         super().__init__(cfg, overrides, _callbacks)
@@ -42,21 +55,3 @@ class SegmentationPredictor(DetectionPredictor):
             results.append(
                 Results(orig_img=orig_img, path=img_path, names=self.model.names, boxes=pred[:, :6], masks=masks))
         return results
-
-
-def predict(cfg=DEFAULT_CFG, use_python=False):
-    """Runs YOLO object detection on an image or video source."""
-    model = cfg.model or 'yolov8n-seg.pt'
-    source = cfg.source or ASSETS
-
-    args = dict(model=model, source=source)
-    if use_python:
-        from ultralytics import YOLO
-        YOLO(model)(**args)
-    else:
-        predictor = SegmentationPredictor(overrides=args)
-        predictor.predict_cli()
-
-
-if __name__ == '__main__':
-    predict()

ultralytics/models/yolo/segment/train.py

@@ -9,6 +9,18 @@ from ultralytics.utils.plotting import plot_images, plot_results
 
 
 class SegmentationTrainer(yolo.detect.DetectionTrainer):
+    """
+    A class extending the DetectionTrainer class for training based on a segmentation model.
+
+    Example:
+        ```python
+        from ultralytics.models.yolo.segment import SegmentationTrainer
+
+        args = dict(model='yolov8n-seg.pt', data='coco8-seg.yaml', epochs=3)
+        trainer = SegmentationTrainer(overrides=args)
+        trainer.train()
+        ```
+    """
 
     def __init__(self, cfg=DEFAULT_CFG, overrides=None, _callbacks=None):
         """Initialize a SegmentationTrainer object with given arguments."""
@@ -46,19 +58,11 @@ class SegmentationTrainer(yolo.detect.DetectionTrainer):
         plot_results(file=self.csv, segment=True, on_plot=self.on_plot)  # save results.png
 
 
-def train(cfg=DEFAULT_CFG, use_python=False):
+def train(cfg=DEFAULT_CFG):
     """Train a YOLO segmentation model based on passed arguments."""
-    model = cfg.model or 'yolov8n-seg.pt'
-    data = cfg.data or 'coco8-seg.yaml'
-    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 = SegmentationTrainer(overrides=args)
-        trainer.train()
+    args = dict(model=cfg.model or 'yolov8n-seg.pt', data=cfg.data or 'coco8-seg.yaml')
+    trainer = SegmentationTrainer(overrides=args)
+    trainer.train()
 
 
 if __name__ == '__main__':

ultralytics/models/yolo/segment/val.py

@@ -15,6 +15,18 @@ from ultralytics.utils.plotting import output_to_target, plot_images
 
 
 class SegmentationValidator(DetectionValidator):
+    """
+    A class extending the DetectionValidator class for validation based on a segmentation model.
+
+    Example:
+        ```python
+        from ultralytics.models.yolo.segment import SegmentationValidator
+
+        args = dict(model='yolov8n-seg.pt', data='coco8-seg.yaml')
+        validator = SegmentationValidator(args=args)
+        validator(model=args['model'])
+        ```
+    """
 
     def __init__(self, dataloader=None, save_dir=None, pbar=None, args=None, _callbacks=None):
         """Initialize SegmentationValidator and set task to 'segment', metrics to SegmentMetrics."""
@@ -233,18 +245,11 @@ class SegmentationValidator(DetectionValidator):
         return stats
 
 
-def val(cfg=DEFAULT_CFG, use_python=False):
+def val(cfg=DEFAULT_CFG):
     """Validate trained YOLO model on validation data."""
-    model = cfg.model or 'yolov8n-seg.pt'
-    data = cfg.data or 'coco8-seg.yaml'
-
-    args = dict(model=model, data=data)
-    if use_python:
-        from ultralytics import YOLO
-        YOLO(model).val(**args)
-    else:
-        validator = SegmentationValidator(args=args)
-        validator(model=args['model'])
+    args = dict(model=cfg.model or 'yolov8n-seg.pt', data=cfg.data or 'coco8-seg.yaml')
+    validator = SegmentationValidator(args=args)
+    validator(model=args['model'])
 
 
 if __name__ == '__main__':