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Performs backpropagation of .

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Each operator has calls. First, aclnnAdaptiveAvgPool2dBackwardGetWorkspaceSize is called to obtain the workspace size required for computation and the executor that contains the operator computation process. Then, aclnnAdaptiveAvgPool2dBackward is called to perform computation.

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• Parameters:

  • gradOutput (aclTensor*, compute input): gradient of the current node, aclTensor on the device. The data type is the same as that of self, and the NC dimension should be the same. are supported. The can be NCHW or NCL, and must be the same as that of self. Input data in NCL format is processed according to (C, Hin, Win).

    • [object Object]Atlas training series products[object Object] and [object Object]Atlas inference series products[object Object]: The data type can be FLOAT16 or FLOAT32.
    • [object Object]Atlas A2 training products/Atlas A2 inference products[object Object] and [object Object]Atlas A3 training series products/Atlas A3 inference series products[object Object]: The data type can be BFLOAT16, FLOAT16, or FLOAT32.

  • self (aclTensor *, compute input): input tensor, leaf node. aclTensor on the device. are supported. The can be ND, NCHW, or NCL. Input data in NCL format is processed according to (C, Hin, Win).

    • [object Object]Atlas training series products[object Object] and [object Object]Atlas inference series products[object Object]: The data type can be FLOAT16 or FLOAT32.
    • [object Object]Atlas A2 training products/Atlas A2 inference products[object Object] and [object Object]Atlas A3 training series products/Atlas A3 inference series products[object Object]: The data type can be BFLOAT16, FLOAT16, or FLOAT32.

  • out (aclTensor*, compute output): output tensor, corresponding to the gradient of the input leaf node. aclTensor on the device. The shape is the same as that of self. The can be ND, NCHW, or NCL, and is the same as that of self. Input data in NCL format is processed according to (C, Hin, Win).

    • [object Object]Atlas training series products[object Object] and [object Object]Atlas inference series products[object Object]: The data type can be FLOAT16 or FLOAT32.
    • [object Object]Atlas A2 training products/Atlas A2 inference products[object Object] and [object Object]Atlas A3 training series products/Atlas A3 inference series products[object Object]: The data type can be BFLOAT16, FLOAT16, or FLOAT32.

  • workspaceSize(uint64_t*, output): size of the workspace to be allocated on the device.

  • executor (aclOpExecutor**, output): operator executor, containing the operator computation process.

• Returns: aclnnStatus: status code. For details, see .

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• Parameters:

  • workspace (void*, input): address of the workspace to be allocated on the device.
  • workspaceSize (uint64_t, input): size of the workspace to be allocated on the device, which is obtained by calling the first-phase API aclnnAdaptiveAvgPool2dBackwardGetWorkspaceSize.
  • executor (aclOpExecutor*, input): operator executor, containing the operator computation process.
  • stream (aclrtStream, input): stream for executing the task.

• Returns:

  aclnnStatus: status code. For details, see .

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• Deterministic compute:
  • aclnnAdaptiveAvgPool2dBackward defaults to a non-deterministic implementation. You can call aclrtCtxSetSysParamOpt to enable deterministic compute.

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