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.AdaptiveMaxPool3d

class mindspore.nn.AdaptiveMaxPool3d(output_size, return_indices=False)

Calculates the 3D adaptive max pooling for an input Tensor. That is, for any input size, the size of the specified output is $(D,H,W)$.

Parameters

• output_size (Union[int, tuple]) – The specified output size, which is a positive integer that represents depth, height and width, or a tuple of three positive integers that represent depth, height and width respectively. If it is None, the output size and input size of the corresponding dimension are the same.

• return_indices (bool, optional) – If return_indices is True , the indices of max value would be output. Otherwise, the indices will not be returned. Default: False .

Inputs:

• input (Tensor) - Tensor, has shape of $(C,D,H,W)$ or $(N,C,D,H,W)$.

Outputs:

• y (Tensor) - Tensor, has the same number of dims and data type as the input .

• argmax (Tensor) - Tensor, the indices of the maximum values along with the outputs, has the same shape as y and a dtype of int32. Return this only when return_indices is True .

Raises

• TypeError – If input is not a Tensor.

• ValueError – If the dimensions number of input is not 4 or 5.

• TypeError – If dtype of input is not int, uint or float.

• ValueError – If output_size is neither an int nor a tuple with shape $(3,)$.

Supported Platforms:

GPU CPU

Examples

>>> import mindspore as ms
>>> import numpy as np
>>> input = ms.Tensor(np.arange(0,36).reshape((1, 3, 3, 4)).astype(np.float32))
>>> output_size = (1, 1, 2)
>>> net = ms.nn.AdaptiveMaxPool3d(output_size, True)
>>> output = net(input)
>>> print(output[0].asnumpy())
[[[[33. 35.]]]]
>>> print(output[1].asnumpy())
[[[[33 35]]]]