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• API description: Performs backpropagation of .
• Formulas:$$gradInput(N, C, floor ( scalesH * ( H + 0.5 )), floor ( scalesW * ( W+ 0.5 ))) += gradOutput( N, C, H ,W)$$

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Each operator has calls. First, aclnnUpsampleNearestExact2dBackwardGetWorkspaceSize is called to obtain the workspace size required for computation and the executor that contains the operator computation process. Then, aclnnUpsampleNearestExact2dBackward 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:

  $$outputSize_H/Height of the output shape (H) <= 50$$ $$outputSize_W/Width of the output shape (W) <= 50$$

• 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:

  • The default deterministic implementation of aclnnUpsampleNearestExact2dBackward is used.

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