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Ruff Docstring formatting (#15793)

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Signed-off-by: UltralyticsAssistant <web@ultralytics.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
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Glenn Jocher 2024-08-25 04:27:55 +08:00 • committed by GitHub
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commit 776ca86369
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60 changed files with 241 additions and 309 deletions
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30
ultralytics/utils/metrics.py
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@ -30,7 +30,6 @@ def bbox_ioa(box1, box2, iou=False, eps=1e-7):
Returns:
(np.ndarray): A numpy array of shape (n, m) representing the intersection over box2 area.
"""
# Get the coordinates of bounding boxes
b1_x1, b1_y1, b1_x2, b1_y2 = box1.T
b2_x1, b2_y1, b2_x2, b2_y2 = box2.T
@ -53,7 +52,7 @@ def bbox_ioa(box1, box2, iou=False, eps=1e-7):
def box_iou(box1, box2, eps=1e-7):
"""
Calculate intersection-over-union (IoU) of boxes. Both sets of boxes are expected to be in (x1, y1, x2, y2) format.
Based on https://github.com/pytorch/vision/blob/master/torchvision/ops/boxes.py
Based on https://github.com/pytorch/vision/blob/master/torchvision/ops/boxes.py.
Args:
box1 (torch.Tensor): A tensor of shape (N, 4) representing N bounding boxes.
@ -63,7 +62,6 @@ def box_iou(box1, box2, eps=1e-7):
Returns:
(torch.Tensor): An NxM tensor containing the pairwise IoU values for every element in box1 and box2.
"""
# NOTE: Need .float() to get accurate iou values
# inter(N,M) = (rb(N,M,2) - lt(N,M,2)).clamp(0).prod(2)
(a1, a2), (b1, b2) = box1.float().unsqueeze(1).chunk(2, 2), box2.float().unsqueeze(0).chunk(2, 2)
@ -90,7 +88,6 @@ def bbox_iou(box1, box2, xywh=True, GIoU=False, DIoU=False, CIoU=False, eps=1e-7
Returns:
(torch.Tensor): IoU, GIoU, DIoU, or CIoU values depending on the specified flags.
"""
# 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)
@ -195,15 +192,22 @@ def _get_covariance_matrix(boxes):
def probiou(obb1, obb2, CIoU=False, eps=1e-7):
"""
Calculate the prob IoU between oriented bounding boxes, https://arxiv.org/pdf/2106.06072v1.pdf.
Calculate probabilistic IoU between oriented bounding boxes.
Implements the algorithm from https://arxiv.org/pdf/2106.06072v1.pdf.
Args:
obb1 (torch.Tensor): A tensor of shape (N, 5) representing ground truth obbs, with xywhr format.
obb2 (torch.Tensor): A tensor of shape (N, 5) representing predicted obbs, with xywhr format.
eps (float, optional): A small value to avoid division by zero. Defaults to 1e-7.
obb1 (torch.Tensor): Ground truth OBBs, shape (N, 5), format xywhr.
obb2 (torch.Tensor): Predicted OBBs, shape (N, 5), format xywhr.
CIoU (bool, optional): If True, calculate CIoU. Defaults to False.
eps (float, optional): Small value to avoid division by zero. Defaults to 1e-7.
Returns:
(torch.Tensor): A tensor of shape (N, ) representing obb similarities.
(torch.Tensor): OBB similarities, shape (N,).
Note:
OBB format: [center_x, center_y, width, height, rotation_angle].
If CIoU is True, returns CIoU instead of IoU.
"""
x1, y1 = obb1[..., :2].split(1, dim=-1)
x2, y2 = obb2[..., :2].split(1, dim=-1)
@ -507,7 +511,6 @@ def compute_ap(recall, precision):
(np.ndarray): Precision envelope curve.
(np.ndarray): Modified recall curve with sentinel values added at the beginning and end.
"""
# Append sentinel values to beginning and end
mrec = np.concatenate(([0.0], recall, [1.0]))
mpre = np.concatenate(([1.0], precision, [0.0]))
@ -560,7 +563,6 @@ def ap_per_class(
x (np.ndarray): X-axis values for the curves. Shape: (1000,).
prec_values: Precision values at mAP@0.5 for each class. Shape: (nc, 1000).
"""
# Sort by objectness
i = np.argsort(-conf)
tp, conf, pred_cls = tp[i], conf[i], pred_cls[i]
@ -792,8 +794,8 @@ class Metric(SimpleClass):
class DetMetrics(SimpleClass):
"""
This class is a utility class for computing detection metrics such as precision, recall, and mean average precision
(mAP) of an object detection model.
Utility class for computing detection metrics such as precision, recall, and mean average precision (mAP) of an
object detection model.
Args:
save_dir (Path): A path to the directory where the output plots will be saved. Defaults to current directory.
@ -942,7 +944,6 @@ class SegmentMetrics(SimpleClass):
pred_cls (list): List of predicted classes.
target_cls (list): List of target classes.
"""
results_mask = ap_per_class(
tp_m,
conf,
@ -1084,7 +1085,6 @@ class PoseMetrics(SegmentMetrics):
pred_cls (list): List of predicted classes.
target_cls (list): List of target classes.
"""
results_pose = ap_per_class(
tp_p,
conf,