mindspore.nn.MeanSurfaceDistance

class mindspore.nn.MeanSurfaceDistance(symmetric=False, distance_metric='euclidean')

This function is used to compute the Average Surface Distance from y_pred to y under the default setting. Mean Surface Distance(MSD), the mean of the vector is taken. This tell us how much, on average, the surface varies between the segmentation and the GT.

Parameters

• distance_metric (string) – The parameter of calculating Hausdorff distance supports three measurement methods, “euclidean”, “chessboard” or “taxicab”. Default: “euclidean”.

• symmetric (bool) – if calculate the symmetric average surface distance between y_pred and y. In addition, if sets symmetric = True, the average symmetric surface distance between these two inputs will be returned. Defaults: False.

Supported Platforms:

Ascend GPU CPU

Examples

>>> import numpy as np
>>> from mindspore import nn, Tensor
>>>
>>> x = Tensor(np.array([[3, 0, 1], [1, 3, 0], [1, 0, 2]]))
>>> y = Tensor(np.array([[0, 2, 1], [1, 2, 1], [0, 0, 1]]))
>>> metric = nn.MeanSurfaceDistance(symmetric=False, distance_metric="euclidean")
>>> metric.clear()
>>> metric.update(x, y, 0)
>>> mean_average_distance = metric.eval()
>>> print(mean_average_distance)
0.8047378541243649

clear()

Clears the internal evaluation result.

eval()

Calculate mean surface distance.

Returns

A float with mean surface distance.

Raises

RuntimeError – If the update method is not called first, an error will be reported.

update(*inputs)

Updates the internal evaluation result ‘y_pred’, ‘y’ and ‘label_idx’.

Parameters

inputs – Input ‘y_pred’, ‘y’ and ‘label_idx’. ‘y_pred’ and ‘y’ are Tensor or numpy.ndarray. ‘y_pred’ is the predicted binary image. ‘y’ is the actual binary image. ‘label_idx’, the data type of label_idx is int.

Raises

• ValueError – If the number of the inputs is not 3.

• TypeError – If the data type of label_idx not be int or float.

• ValueError – If the value of label_idx is not in y_pred or y.

• ValueError – If y_pred and y should have different shape.