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• API description: Performs backpropagation of . It is used to compute the points of the output gradient tensor and map them to the positions of the input gradient tensor. The output gradient values are accumulated to the input gradient tensor.
• Formulas:$$gradInput(N, C, floor ( scales * ( L + 0.5 ))) += gradOutput( N, C, L)$$

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Each operator has calls. First, aclnnUpsampleNearestExact1dBackwardGetWorkspaceSize is called to obtain the workspace size required for computation and the executor that contains the operator computation process. Then, aclnnUpsampleNearestExact1dBackward is called to perform computation.

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• Parameters

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• Returns:

  [object Object]: status code. For details, see .

  The first-phase API implements input parameter validation. The following error codes may be returned.

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• Parameters

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• Returns:

  [object Object]: status code. For details, see .

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• In the input data scaling scenario, the scaling factor must be less than or equal to 50. That is:

  $$outputSize[0]/Height of the output shape (L) <= 50$$

• The inputSize, outputSize, and scales parameters must meet the following restrictions:

  $$outputSize = floor(inputSize\_L * scales)$$

• Deterministic computation:

  • The aclnnUpsampleNearestExact1dBackward is implemented in deterministic mode by default.

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The following example is for reference only. For details, see .