[object Object]

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

• 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:

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

  The values are as follows:

  • 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:$$W(d) =\begin{cases} (a+2)|d|^3-(a+3)|d|^2+1 & |d|\leq1 \\ a|d|^3-5a|d|^2+8a|d|-4a & 1<|d|<2 \\ 0 & otherwise \end{cases}$$ The values are as follows:
    • $a=-0.75$
    • $d = |(h, w) - (h_i, w_j)|$

[object Object]

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

[object Object]

• Parameters

  [object Object]

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

    • The data types of gradOut and gradInput do not support BFLOAT16.
    • The data formats of gradOut and gradInput 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 gradOut and gradInput do not support NHWC.

• Returns:

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

  The first-phase API implements input parameter validation. The following error codes may be returned.

  [object Object]

[object Object]

• Parameters

  [object Object]

• Returns:

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

[object Object]

• The inputSize, outputSize, scalesH, and scalesW parameters must meet the following requirements:

  $$outputSize\_H = floor(inputSize\_H * scalesH)$$ $$outputSize\_W = floor(inputSize\_W * scalesW)$$

• Deterministic computation:

  aclnnUpsampleBicubic2dBackward defaults to a non-deterministic implementation. You can call aclrtCtxSetSysParamOpt to enable deterministic computing. Deterministic computation is not supported when the following conditions are met:

  • $inputSize\_W$ > 130000
  • $inputSize\_H$/$outputSize\_H$ >=50
  • $inputSize\_W$/$outputSize\_W$ >=50 && $inputSize\_N$ * $inputSize\_C$ * $inputSize\_H$ > $inputSize\_W$ * 0.5
  • $inputSize\_H$/$outputSize\_H$ < 0.02 && $inputSize\_W$/$outputSize\_W$ < 0.02 && $inputSize\_N$ * $inputSize\_C$ * $inputSize\_H$ > $inputSize\_W$ * 10000

[object Object]

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