Apply Ruff Docstring reformat (#13278)

* Apply Ruff Docstring reformat

Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>

* Auto-format by https://ultralytics.com/actions

* Update export.py

Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>

---------

Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
Co-authored-by: Ultralytics Assistant <135830346+UltralyticsAssistant@users.noreply.github.com>

utils/metrics.py

@@ -41,7 +41,6 @@ def ap_per_class(tp, conf, pred_cls, target_cls, plot=False, save_dir=".", names
     # Returns
         The average precision as computed in py-faster-rcnn.
     """
-
     # Sort by objectness
     i = np.argsort(-conf)
     tp, conf, pred_cls = tp[i], conf[i], pred_cls[i]
@@ -103,7 +102,6 @@ def compute_ap(recall, precision):
     # Returns
         Average precision, precision curve, recall curve
     """
-
     # Append sentinel values to beginning and end
     mrec = np.concatenate(([0.0], recall, [1.0]))
     mpre = np.concatenate(([1.0], precision, [0.0]))
@@ -137,6 +135,7 @@ class ConfusionMatrix:
         Return intersection-over-union (Jaccard index) of boxes.
 
         Both sets of boxes are expected to be in (x1, y1, x2, y2) format.
+
         Arguments:
             detections (Array[N, 6]), x1, y1, x2, y2, conf, class
             labels (Array[M, 5]), class, x1, y1, x2, y2
@@ -233,7 +232,6 @@ def bbox_iou(box1, box2, xywh=True, GIoU=False, DIoU=False, CIoU=False, eps=1e-7
 
     Input shapes are box1(1,4) to box2(n,4).
     """
-
     # Get the coordinates of bounding boxes
     if xywh:  # transform from xywh to xyxy
         (x1, y1, w1, h1), (x2, y2, w2, h2) = box1.chunk(4, -1), box2.chunk(4, -1)
@@ -279,14 +277,15 @@ def box_iou(box1, box2, eps=1e-7):
     Return intersection-over-union (Jaccard index) of boxes.
 
     Both sets of boxes are expected to be in (x1, y1, x2, y2) format.
+
     Arguments:
         box1 (Tensor[N, 4])
         box2 (Tensor[M, 4])
+
     Returns:
         iou (Tensor[N, M]): the NxM matrix containing the pairwise
             IoU values for every element in boxes1 and boxes2
     """
-
     # inter(N,M) = (rb(N,M,2) - lt(N,M,2)).clamp(0).prod(2)
     (a1, a2), (b1, b2) = box1.unsqueeze(1).chunk(2, 2), box2.unsqueeze(0).chunk(2, 2)
     inter = (torch.min(a2, b2) - torch.max(a1, b1)).clamp(0).prod(2)
@@ -304,7 +303,6 @@ def bbox_ioa(box1, box2, eps=1e-7):
     box2:       np.array of shape(nx4)
     returns:    np.array of shape(n)
     """
-
     # Get the coordinates of bounding boxes
     b1_x1, b1_y1, b1_x2, b1_y2 = box1
     b2_x1, b2_y1, b2_x2, b2_y2 = box2.T