mindspore.nn.HausdorffDistance
- class mindspore.nn.HausdorffDistance(distance_metric='euclidean', percentile=None, directed=False, crop=True)[source]
Calculates the Hausdorff distance. Hausdorff distance is the maximum and minimum distance between two point sets. Given two feature sets A and B, the Hausdorff distance between two point sets A and B is defined as follows:
\[\begin{split}\begin{array}{ll} \\ H(A, B) = \text{max}[h(A, B), h(B, A)]\\ h(A, B) = \underset{a \in A}{\text{max}}\{\underset{b \in B}{\text{min}} \rVert a - b \rVert \}\\ h(B, A) = \underset{b \in B}{\text{max}}\{\underset{a \in A}{\text{min}} \rVert b - a \rVert \} \end{array}\end{split}\]where h(A,B) is the maximum distance of a set A to the nearest point in the set B, h(B,A) is the maximum distance of a set B to the nearest point in the set A. The distance calculation is oriented, which means that most of times \(h(A, B)\) is not equal to \(h(B, A)\).
- Parameters
distance_metric (string) – Three distance measurement methods are supported: “euclidean”, “chessboard” or “taxicab”. Default: “euclidean”.
percentile (float) – Floating point numbers between 0 and 100. Specify the percentile parameter to get the percentile of the Hausdorff distance. Default: None.
directed (bool) – If True, it only calculates h(y_pred, y) distance, otherwise, max(h(y_pred, y), h(y, y_pred)) will be returned. Default: False.
crop (bool) – Crop input images and only keep the foregrounds. In order to maintain two inputs’ shapes, here the bounding box is achieved by (y_pred | y) which represents the union set of two images. Default: True.
- 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.HausdorffDistance() >>> metric.clear() >>> metric.update(x, y, 0) >>> mean_average_distance = metric.eval() >>> print(mean_average_distance) 1.4142135623730951
- eval()[source]
Calculate the no-directed or directed Hausdorff distance.
- Returns
numpy.float64, the hausdorff distance.
- Raises
RuntimeError – If the update method is not called first, an error will be reported.
- update(*inputs)[source]
Updates the internal evaluation result with the inputs: ‘y_pred’, ‘y’ and ‘label_idx’.
- Parameters
inputs – Input ‘y_pred’, ‘y’ and ‘label_idx’. ‘y_pred’ and ‘y’ are a Tensor, list or numpy.ndarray. ‘y_pred’ is the predicted binary image. ‘y’ is the actual binary image. Data type of ‘label_idx’ is int or float.
- 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.