mindspore.ops.iou

mindspore.ops.iou(anchor_boxes, gt_boxes, mode='iou')[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.

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

Warning

In Ascend, only computation of float16 data is supported. To avoid overflow, the input length and width are scaled by 0.2 internally.

Parameters

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 either float16, float32 or float64.
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 either float16, float32 or float64.
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' .

Returns

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

>>> import mindspore
>>> import numpy as np
>>> from mindspore import Tensor, ops
>>> 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)
>>> mode = 'iou'
>>> output = ops.iou(anchor_boxes, gt_boxes, mode)
>>> print(output.shape)
(3, 3)