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• Function: Performs backpropagation of .
• Formulas:$$gradInput(N, C, D, H, W) += gradOut( N, C, ceil ( scalesD * D ), ceil ( scalesH * H ), ceil ( scalesW * W ))$$

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Each operator has calls. First, aclnnUpsampleNearest3dBackwardGetWorkspaceSize is called to obtain the input parameters and compute the required workspace size based on the process. Then, aclnnUpsampleNearest3dBackward 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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• The shape constraints of gradOut and gradInput are as follows:

  • The value of each dimension is less than or equal to 2^20.

  • The N and C axes of gradInput must be the same as those of gradOut.

  • The memory usage must be less than 60 GB. The memory size can be calculated according to the following formula:

    $$N * C * (gradOut\_D * gradOut\_H * gradOut\_W + gradInput\_D * gradInput\_H * gradInput\_W + gradOut\_D * gradOut\_H * gradInput\_W + gradOut\_D * gradInput\_H * gradInput\_W) * sizeof(float) < 60 * 1024 * 1024 * 1024$$

    The values are as follows:

    • N indicates the N axis of the input and output.
    • C indicates the C axis of the input and output.

  • N * C * gradOut_D * gradOut_H < 2^31

  • gradInput_W * gradInput_H < 2^31

• If the data formats of gradOut and gradInput are not NCDHW or NDHWC, the input data is processed as NCDHW by default.

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

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

• Deterministic computation:

  • The deterministic implementation of aclnnUpsampleNearest3dBackward is used by default.

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