[object Object]

[object Object][object Object]undefined[object Object]

• API description: Performs backpropagation of . [object Object]

• Formula:

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

[object Object]

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.

[object Object]

• Parameters:

  [object Object]

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

    The data types of the input parameter [object Object] and output parameter [object Object] support only FLOAT16.

• Returns:

  aclnnStatus: status code. For details, see .

  The first-phase API implements input parameter verification. The following errors may be thrown.

  [object Object]

[object Object]

• Parameters:

  [object Object]

• Returns:

  aclnnStatus: status code. For details, see .

[object Object]

• Either outputSize or scalesH/scalesW must be used.
  • If the value of scalesH or scalesW is less than or equal to 0, the value of outputSize is used.
  • If the values of scalesH and scalesW are greater than 0, the values of scalesH and scalesW are used, $outputSize=[floor(inputSize\_H * scalesH), and floor(inputSize\_W * scalesW)]$.
• Deterministic computing:
  • aclnnUpsampleNearest2dBackward defaults to a deterministic implementation.

[object Object]

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