mindspore.nn.Dice

class mindspore.nn.Dice(smooth=1e-05)

The Dice coefficient is a set similarity metric. It is used to calculate the similarity between two samples. The value of the Dice coefficient is 1 when the segmentation result is the best and 0 when the segmentation result is the worst. The Dice coefficient indicates the ratio of the area between two objects to the total area. The function is shown as follows:

\[dice = \frac{2 * (pred \bigcap true)}{pred \bigcup true}\]

Parameters

smooth (float) – A term added to the denominator to improve numerical stability. Should be greater than 0. Default: 1e-5.

Supported Platforms:

Ascend GPU CPU

Examples

>>> import numpy as np
>>> from mindspore import nn, Tensor
>>>
>>> x = Tensor(np.array([[0.2, 0.5], [0.3, 0.1], [0.9, 0.6]]))
>>> y = Tensor(np.array([[0, 1], [1, 0], [0, 1]]))
>>> metric = nn.Dice(smooth=1e-5)
>>> metric.clear()
>>> metric.update(x, y)
>>> dice = metric.eval()
>>> print(dice)
0.20467791371802546

clear()

Clears the internal evaluation result.

eval()

Computes the Dice.

Returns

Float, the computed result.

Raises

RuntimeError – If the total samples num is 0.

update(*inputs)

Updates the internal evaluation result \(y_pred\) and \(y\).

Parameters

inputs – Input y_pred and y. y_pred and y are Tensor, list or numpy.ndarray. y_pred is the predicted value, y is the true value. The shape of y_pred and y are both \((N, ...)\).

Raises

• ValueError – If the number of the inputs is not 2.

• RuntimeError – If y_pred and y should have different the dimension.