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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]). Compared with , this API adds the input parameters scalesH and scalesW. You should select a proper API based on the actual situation.

• Formulas:

  $$h_{src} = min(floor(h_{dst} * scalesH), H - 1), \ scalesH = outputSize[0] / self\_H$$ $$w_{src} = min(floor(w_{dst} * scalesW), W - 1), \ scalesW = outputSize[1] / self\_W$$ $$out(N, C, h_{dst}, w_{dst}) = self(N, C, h_{src}, w_{src})$$

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Each operator has calls. First, aclnnUpsampleNearest2dV2GetWorkspaceSize is called to obtain the workspace size required for computation and the executor that contains the operator computation process. Then, aclnnUpsampleNearest2dV2 is called to perform computation.

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• Parameters

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

    The data types of the input parameter self and output parameter out do not support FLOAT32 or BFLOAT16.

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

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

• Deterministic computation:

  • aclnnUpsampleNearest2dV2 defaults to a deterministic implementation.

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