[object Object]

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

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.

[object Object]

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

[object Object]

• Parameters:

  [object Object]
  • For the [object Object]Atlas inference products[object Object] and [object Object]Atlas training products[object Object], the data types of the [object Object] and [object Object] parameters do not support BFLOAT16.

• Returns:

  [object Object]: 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:

  [object Object]: status code. For details, see .

[object Object]

• 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)

[object Object]

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