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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.

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Problem description

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

mindspore.ops.lerp

View Source On Gitee
mindspore.ops.lerp(input, end, weight)[source]

Does a linear interpolation of two tensors input and end based on a float or tensor weight.

outputi=inputi+weighti(endiinputi)

Note

  • The shapes of input and end must be broadcastable.

  • If weight is a tensor, then the shapes of weight , start , and end must be broadcastable.

  • On the Ascend platform, if weight dtype is float, the type of input and end need to be float32.

Parameters
  • input (Tensor) – The tensor with the starting points.

  • end (Tensor) – The tensor with the ending points.

  • weight (Union[float, Tensor]) – The weight for the interpolation formula.

Returns

Tensor

Supported Platforms:

Ascend GPU CPU

Examples

>>> import mindspore
>>> start = mindspore.tensor([1., 2., 3., 4.], mindspore.float32)
>>> end = mindspore.tensor([10., 10., 10., 10.], mindspore.float32)
>>> output = mindspore.ops.lerp(start, end, 0.5)
>>> print(output)
[5.5 6.  6.5 7. ]
>>> output = mindspore.ops.lerp(start, end, mindspore.tensor([0.5, 0.5, 0.5, 0.5], mindspore.float32))
>>> print(output)
[5.5 6.  6.5 7. ]