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• API description: Performs backpropagation of .

• Formulas:

  $$gradInput(N, C, H, W) += gradOutput( N, C, ceil ( scalesH * H ), ceil ( scalesW * W ))$$

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Each operator has calls. First, aclnnUpsampleNearest2dBackwardGetWorkspaceSize is called to obtain the input parameters and compute the required workspace size based on the process. Then, aclnnUpsampleNearest2dBackward is called to perform computation.

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• Parameters

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  • [object Object]Atlas inference products[object Object] and [object Object]Atlas training products[object Object]:

    The data types of the input parameter gradOut and output parameter gradInput support only FLOAT16.

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

  $$outputSize\_H = floor(inputSize\_H * scalesH)$$ $$outputSize\_W = floor(inputSize\_W * scalesW)$$

• Deterministic computation:

  • aclnnUpsampleNearest2dBackward defaults to a deterministic implementation.

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