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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, D, H, W), the output shape is (N, C, outputSize[0], outputSize[1], outputSize[2]).
• Formulas:$$d_{src} = min(floor((d_{dst} + 0.5) / scalesD), D - 1),scalesD = outputSize[0] / D$$ $$h_{src} = min(floor((h_{dst} + 0.5) / scalesH), H - 1),scalesH = outputSize[1] / H$$ $$w_{src} = min(floor((w_{dst} + 0.5) / scalesW), W - 1),scalesW = outputSize[2] / W$$ $$out(N, C, d_{dst},h_{dst}, w_{dst}) = self(N, C, d_{src},h_{src}, w_{src})$$

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Each operator has calls. First, aclnnUpsampleNearestExact3dGetWorkspaceSize is called to obtain the input parameters and compute the required workspace size based on the process. Then, aclnnUpsampleNearestExact3d 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:

  [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, scalesD, scalesH, and scalesW must meet the following restrictions:

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

• Deterministic computation:

  • aclnnUpsampleNearestExact3d defaults to a deterministic implementation.

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