mindspore.nn.Dice
- class mindspore.nn.Dice(smooth=1e-05)[source]
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 is 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
- eval()[source]
Computes the Dice.
- Returns
Float, the computed result.
- Raises
RuntimeError – If the total number of samples is 0.
- update(*inputs)[source]
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 do not have the same shape.