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• API description: Performs backpropagation of . If the shape of the input tensor is (N, C, H, W), then the shape of the output tensor is (N, C, inputSize[2], inputSize[3]).

• Formula: For a two-dimensional interpolation point $(N, C, h, w)$, the interpolation $gradInput(N, C, h, w)$ may be represented as:

  $${gradInput(N, C, h, w)}=\sum_{i=0}^{3}\sum_{j=0}^{3}{W(i, j)}*{f(h_i, w_j)}$$ $$scaleH =\begin{cases} (inputSize[2]-1) / (outputSize[0]-1) & alignCorners=true \\ 1 / scalesH & alignCorners=false\&scalesH>0\\ inputSize[2] / outputSize[0] & otherwise \end{cases}$$ $$scaleW =\begin{cases} (inputSize[3]-1) / (outputSize[1]-1) & alignCorners=true \\ 1 / scalesW & alignCorners=false\&scalesW>0\\ inputSize[3] / outputSize[1] & otherwise \end{cases}$$

  Where,

  • i and j are index variables of $W(i, j)$.
  • $f(h_i, w_j)$ is the pixel value of gradOut in $(h_i, w_j)$.
  • $W(i, j)$ is the weight of the bicubic anti-aliasing interpolation, which is defined as follows:$$W(d) =\begin{cases} (a+2)|d|^3-(a+3)|d|^2+1 & |d|\leq1 \\ a|d|^3-5a|d|^2+8a|d|-4a & 1<|d|<2 \\ 0 & otherwise \end{cases}$$ Where,
    • $a=-0.75$
    • $d = |(h, w) - (h_i, w_j)|$

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Each operator has calls. First, aclnnUpsampleBicubic2dBackwardGetWorkspaceSize is called to obtain the input parameters and compute the required workspace size based on the process. Then, aclnnUpsampleBicubic2dBackward is called to perform computation.

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• Parameters:

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

    • The data types of [object Object] and [object Object] do not support BFLOAT16.
    • The data formats of [object Object] and [object Object] do not support NHWC.

  • [object Object]Atlas A2 training products/Atlas A2 inference products[object Object] and [object Object]Atlas A3 training products/Atlas A3 inference products[object Object]:

    The data formats of [object Object] and [object Object] do not support NHWC.

• 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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• Either the H and W axes of the outputSize parameter or the scalesH and scalesW parameters can be used.

  • When alignCorners is set to True:
    • If the value of the corresponding axis of outputSize is equal to 1, the value of the corresponding axis of scales is 0.
    • In other cases, the values of the corresponding axes in the input parameters inputSize and outputSize are used, and $scales = (inputSize – 1)/(outputSize – 1)$.
  • If alignCorners is set to False:
    • If the value of scalesH or scalesW is less than or equal to 0, the value of the corresponding axis in outputSize is used, that is, $scales = (inputSize/outputSize)$.
    • If the value of scalesH or scalesW is greater than 0, the value of scalesH or scalesW is used. That is, the value of the corresponding axis of outputSize is $floor(inputSize\_H * scalesH)$ or $floor(inputSize\_W * scalesW)$.

• Deterministic computing:

  aclnnUpsampleBicubic2dBackward defaults to a non-deterministic implementation. You can call aclrtCtxSetSysParamOpt to enable deterministic computing. A deterministic implementation must meet the following conditions:

  • inputSize[3] > 130000
  • scaleH >=50
  • scaleW >=50 && inputSize[0] * inputSize[1] * inputSize[2] > inputSize[3] * 0.5
  • scaleH < 0.02 && scaleW < 0.02 && inputSize[0] * inputSize[1] * inputSize[2] > inputSize[3] * 10000

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