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/utils/__init__.py

@@ -169,7 +169,7 @@ def plt_settings(rcparams=None, backend='Agg'):
     """
     Decorator to temporarily set rc parameters and the backend for a plotting function.
 
-    Usage:
+    Example:
         decorator: @plt_settings({"font.size": 12})
         context manager: with plt_settings({"font.size": 12}):
 

ultralytics/utils/ops.py

@@ -18,8 +18,7 @@ from .metrics import box_iou
 
 class Profile(contextlib.ContextDecorator):
     """
-    YOLOv8 Profile class.
-    Usage: as a decorator with @Profile() or as a context manager with 'with Profile():'
+    YOLOv8 Profile class. Use as a decorator with @Profile() or as a context manager with 'with Profile():'.
     """
 
     def __init__(self, t=0.0):

ultralytics/utils/tal.py

@@ -10,12 +10,14 @@ TORCH_1_10 = check_version(torch.__version__, '1.10.0')
 
 
 def select_candidates_in_gts(xy_centers, gt_bboxes, eps=1e-9):
-    """select the positive anchor center in gt
+    """
+    Select the positive anchor center in gt.
 
     Args:
         xy_centers (Tensor): shape(h*w, 4)
         gt_bboxes (Tensor): shape(b, n_boxes, 4)
-    Return:
+
+    Returns:
         (Tensor): shape(b, n_boxes, h*w)
     """
     n_anchors = xy_centers.shape[0]
@@ -27,13 +29,14 @@ def select_candidates_in_gts(xy_centers, gt_bboxes, eps=1e-9):
 
 
 def select_highest_overlaps(mask_pos, overlaps, n_max_boxes):
-    """if an anchor box is assigned to multiple gts,
-        the one with the highest iou will be selected.
+    """
+    If an anchor box is assigned to multiple gts, the one with the highest IoI will be selected.
 
     Args:
         mask_pos (Tensor): shape(b, n_max_boxes, h*w)
         overlaps (Tensor): shape(b, n_max_boxes, h*w)
-    Return:
+
+    Returns:
         target_gt_idx (Tensor): shape(b, h*w)
         fg_mask (Tensor): shape(b, h*w)
         mask_pos (Tensor): shape(b, n_max_boxes, h*w)