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• API description: Applies 2D bicubic upsampling to an input signal composed of several input channels. If the shape of the input tensor x is (N, C, H, W), then the shape of the output tensor out is (N, C, outputSize[0], outputSize[1]).

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

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

  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 the original image 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:
    • $a=-0.75$
    • $d = |(h, w) - (h_i, w_j)|$

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Each operator has calls. First, aclnnUpsampleBicubic2dGetWorkspaceSize is called to obtain the workspace size required for computation and the executor that contains the operator computation process. Then, aclnnUpsampleBicubic2d is called to perform computation.

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• Parameters

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  • [object Object]Atlas 200I/500 A2 inference products[object Object], [object Object]Atlas inference products[object Object], and [object Object]Atlas training products[object Object]:

    • Data type: The self and out parameters do not support BFLOAT16.
    • Format: The self and out parameters 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 self and out 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.

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• Parameters

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• Returns:

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

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• The shape constraints of self and out are as follows:

  • The value of each dimension is less than or equal to 2^20.

  • The N and C axes of out must be the same as those of self.

  • The memory usage must be less than 60 GB. The memory size can be calculated according to the following formula:

    $$(self\_H * self\_W + out\_H * out\_W + self\_H * out\_W) * N * C * sizeof(float) < 60 * 1024 * 1024 * 1024$$

    The values are as follows:

    • N indicates the N axis of the input and output.
    • C indicates the C axis of the input and output.

  • N * C * self_H < 2^31

• The self, outputSize, scalesH, and scalesW parameters must meet the following restrictions:

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

• Deterministic computation:

  • aclnnUpsampleBicubic2d defaults to a deterministic implementation.

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