mindspore.ops.IOU

class mindspore.ops.IOU(*args, **kwargs)[source]

Calculates intersection over union for boxes.

Computes the intersection over union (IOU) or the intersection over foreground (IOF) based on the ground-truth and predicted regions.

\[ \begin{align}\begin{aligned}\text{IOU} = \frac{\text{Area of Overlap}}{\text{Area of Union}}\\\text{IOF} = \frac{\text{Area of Overlap}}{\text{Area of Ground Truth}}\end{aligned}\end{align} \]

Parameters: mode (string) – The mode is used to specify the calculation method, now supporting ‘iou’ (intersection over union) or ‘iof’ (intersection over foreground) mode. Default: ‘iou’.

Inputs:

anchor_boxes (Tensor) - Anchor boxes, tensor of shape (N, 4). “N” indicates the number of anchor boxes, and the value “4” refers to “x0”, “y0”, “x1”, and “y1”. Data type must be float16 or float32.
gt_boxes (Tensor) - Ground truth boxes, tensor of shape (M, 4). “M” indicates the number of ground truth boxes, and the value “4” refers to “x0”, “y0”, “x1”, and “y1”. Data type must be float16 or float32.

Outputs:

Tensor, the ‘iou’ values, tensor of shape (M, N), with the same data type as anchor_boxes.

Raises: KeyError – When mode is not ‘iou’ or ‘iof’.

Supported Platforms:: Ascend GPU CPU

Examples

>>> iou = ops.IOU()
>>> anchor_boxes = Tensor(np.random.randint(1.0, 5.0, [3, 4]), mindspore.float16)
>>> gt_boxes = Tensor(np.random.randint(1.0, 5.0, [3, 4]), mindspore.float16)
>>> output = iou(anchor_boxes, gt_boxes)
>>> print(output.shape)
(3, 3)