mindspore.train.RootMeanSquareDistance

class mindspore.train.RootMeanSquareDistance(symmetric=False, distance_metric='euclidean')[source]

Computes the Root Mean Square Surface Distance from y_pred to y under the default setting.

Given two sets A and B, S(A) denotes the set of surface voxels of A, the shortest distance of an arbitrary voxel v to S(A) is defined as:

\[{\text{dis}}\left (v, S(A)\right ) = \underset{s_{A} \in S(A)}{\text{min }}\rVert v - s_{A} \rVert\]

The Root Mean Square Surface Distance from set(B) to set(A) is:

\[RmsSurDis(B \rightarrow A) = \sqrt{\frac{\sum_{s_{B} \in S(B)}^{} {\text{dis}^2 \left ( s_{B}, S(A) \right )} }{\left | S(B) \right |}}\]

Where the ||*|| denotes a distance measure. |*| denotes the number of elements.

The Root Mean Square Surface Distance from set(B) to set(A) and from set(A) to set(B) is:

\[RmsSurDis(A \leftrightarrow B) = \sqrt{\frac{\sum_{s_{A} \in S(A)}^{} {\text{dis} \left ( s_{A}, S(B) \right ) ^{2}} + \sum_{s_{B} \in S(B)}^{} {\text{dis} \left ( s_{B}, S(A) \right ) ^{2}}}{\left | S(A) \right | + \left | S(B) \right |}}\]
Parameters

  • distance_metric (string) – Three measurement methods are supported: "euclidean" (Euclidean Distance) , "chessboard" (Chessboard Distance, Chebyshev Distance) or "taxicab" (Taxicab Distance, Manhattan Distance). Default: "euclidean" .

  • symmetric (bool) – Whether to calculate the symmetric average root mean square distance between y_pred and y. If False, only calculates \(RmsSurDis(y\_pred, y)\) surface distance, otherwise, the mean of distance from y_pred to y and from y to y_pred, i.e. \(RmsSurDis(y\_pred \leftrightarrow y)\) will be returned. Default: False .

Supported Platforms:

Ascend GPU CPU

Examples

>>> import numpy as np
>>> from mindspore import Tensor
>>> from mindspore.train import RootMeanSquareDistance
>>>
>>> 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 = 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()[source]

Clears the internal evaluation result.

eval()[source]

Calculate Root Mean Square Distance.

Returns

numpy.float64, root mean square surface distance.

Raises

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

update(*inputs)[source]

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, list 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 is not int or float.

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

  • ValueError – If y_pred and y have different shapes.