mindspore.nn.DiceLoss

class mindspore.nn.DiceLoss(smooth=1e-5)

The Dice coefficient is a set similarity loss, which is used to calculate the similarity between two samples.

Warning

This interface has been deprecated since version 2.8 and will be removed in versions 2.9.0 and later.

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:

\[dice = 1 - \frac{2 * |pred \bigcap true|}{|pred| + |true| + smooth}\]

\(pred\) represent logits, \(true\) represent labels .

Parameters

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

Inputs:

• logits (Tensor) - Input predicted value. The data type must be float16 or float32.

• labels (Tensor) - Input target value. Same shape as the logits. The data type must be float16 or float32.

Outputs:

Tensor, a tensor of shape with the per-example sampled Dice losses.

Raises

• ValueError – If the dimension of logits is different from labels.

• TypeError – If the type of logits or labels is not a tensor.

Supported Platforms:

Deprecated

Examples

>>> import mindspore
>>> from mindspore import Tensor, nn
>>> import numpy as np
>>> loss = nn.DiceLoss(smooth=1e-5)
>>> logits = Tensor(np.array([[0.2, 0.5], [0.3, 0.1], [0.9, 0.6]]), mindspore.float32)
>>> labels = Tensor(np.array([[0, 1], [1, 0], [0, 1]]), mindspore.float32)
>>> output = loss(logits, labels)
>>> print(output)
0.38596618