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Performs 2D adaptive average pooling on tensor self with the specified 2D output shape (outputSize). Unlike aclnnAvgPool2d, aclnnAdaptiveAvgPool2d only needs to specify the output size to automatically deduce the kernel size and corresponding stride.

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Each operator has calls. First, aclnnAdaptiveAvgPool2dGetWorkspaceSize is called to obtain the workspace size required for computation and the executor that contains the operator computation process. Then, aclnnAdaptiveAvgPool2d is called to perform computation.

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• Parameters:

  [object Object]
  • [object Object]Atlas training series products[object Object] and [object Object]Atlas inference series products[object Object]: The BFLOAT16 data type is not supported.

• Returns:

  aclnnStatus: status code. For details, see .

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

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• Parameters:

  [object Object]

• Returns:

  aclnnStatus: status code. For details, see .

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• Deterministic compute:

  • aclnnAdaptiveAvgPool2d defaults to a deterministic implementation.

• Shape description:

  • self.shape = (N, C, Hin, Win) or (C, Hin, Win)
  • outputSize = [Hout, Wout]
  • out.shape = (N, C, Hout, Wout) or (C, Hout, Wout)

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