Document feedback

Question document fragment

When a question document fragment contains a formula, it is displayed as a space.

Submission type

issue

It's a little complicated...

I'd like to ask someone.

Please select the submission type

Problem type

Specifications and Common Mistakes

- Specifications and Common Mistakes:

- Misspellings or punctuation mistakes,incorrect formulas, abnormal display.

- Incorrect links, empty cells, or wrong formats.

- Chinese characters in English context.

- Minor inconsistencies between the UI and descriptions.

- Low writing fluency that does not affect understanding.

- Incorrect version numbers, including software package names and version numbers on the UI.

Usability

- Usability:

- Incorrect or missing key steps.

- Missing main function descriptions, keyword explanation, necessary prerequisites, or precautions.

- Ambiguous descriptions, unclear reference, or contradictory context.

- Unclear logic, such as missing classifications, items, and steps.

Correctness

- Correctness:

- Technical principles, function descriptions, supported platforms, parameter types, or exceptions inconsistent with that of software implementation.

- Incorrect schematic or architecture diagrams.

- Incorrect commands or command parameters.

- Incorrect code.

- Commands inconsistent with the functions.

- Wrong screenshots.

- Sample code running error, or running results inconsistent with the expectation.

Risk Warnings

- Risk Warnings:

- Lack of risk warnings for operations that may damage the system or important data.

Content Compliance

- Content Compliance:

- Contents that may violate applicable laws and regulations or geo-cultural context-sensitive words and expressions.

- Copyright infringement.

Please select the type of question

Problem description

Describe the bug so that we can quickly locate the problem.

Submit

Document feedback

mindspore.nn.HausdorffDistance

class mindspore.nn.HausdorffDistance(distance_metric='euclidean', percentile=None, directed=False, crop=True)

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:

$$H(A,B)=\text{max}[h(A,B),h(B,A)]h(A,B)=\underset{a\in A}{\text{max}}\{\underset{b\in B}{\text{min}}\Vert a-b\Vert \}h(A,B)=\underset{b\in B}{\text{max}}\{\underset{a\in A}{\text{min}}\Vert b-a\Vert \}$$

Parameters

• distance_metric (string) – The parameter of calculating Hausdorff distance supports three measurement methods, “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. Defaults: None.

• directed (bool) – It can be divided into directional and non directional Hausdorff distance, and the default is non directional Hausdorff distance, specify the percentile parameter to get the percentile of the Hausdorff distance. 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

clear()

Clears the internal evaluation result.

eval()

Calculate the no-directed or directed Hausdorff distance.

Returns

A float with hausdorff_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.