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• API description: Applies the nearest neighbor interpolation algorithm to upsample the input signal composed of several input channels. If the input shape is (N, C, H, W), the output shape is (N, C, outputSize[0], outputSize[1]).
• Formula:$$h_{src} = min(floor((h_{dst} + 0.5) / scalesH), H - 1),scalesH = outputSize[0] / H$$ $$w_{src} = min(floor((w_{dst} + 0.5) / scalesW), W - 1),scalesW = outputSize[1] / W$$ $$out(N, C, h_{dst}, w_{dst}) = self(N, C, h_{src}, w_{src})$$

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Each operator has calls. First, aclnnUpsampleNearestExact2dGetWorkspaceSize is called to obtain the workspace size required for computation and the executor that contains the operator computation process. Then, aclnnUpsampleNearestExact2d is called to perform computation.

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• Parameters:

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

    The data types of self and out support only FLOAT32 and FLOAT16.

• 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:

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• 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.
  • If the value of scalesH or scalesW is less than or equal to 0, the value of the corresponding axis in outputSize is used.
  • 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 $floor(self\_H * scalesH)$ or $floor(self\_W * scalesW)$.
• Deterministic computing:
  • aclnnUpsampleNearestExact2d defaults to a deterministic implementation.

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