mindspore.nn.RootMeanSquareDistance

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

This function is used to compute the Residual Mean Square Distance from y_pred to y under the default setting. Residual Mean Square Distance(RMS), the mean is taken from each of the points in the vector, these residuals are squared (to remove negative signs), summed, weighted by the mean and then the square-root is taken. Measured in mm.

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.

Examples

>>> 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.RootMeanSquareDistance(symmetric=False, distance_metric="euclidean")
>>> metric.clear()
>>> metric.update(x, y, 0)
>>> root_mean_square_distance = metric.eval()
>>> print(root_mean_square_distance)
1.0000000000000002

clear()

Clears the internal evaluation result.

eval()

Calculate residual mean square surface distance.

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.