[object Object]

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

• Function: DepthwiseConv2D is a two-dimensional depthwise convolution operation. In this operation, each input channel is convolved with an independent kernel (called a depthwise kernel).

• Formula:

  Assume that the shape of the input self is $(N, C_{\text{in}}, H, W)$, and the shape of the output out is $(N, C_{\text{out}}, H_{\text{out}}, W_{\text{out}})$. The output of each convolution kernel is represented as follows:

  $$\text{out}(N_i, C_{\text{out}_j}) = \text{bias}(C_{\text{out}_j}) + \text{weight}(C_{\text{out}_j}, C_{\text{in}_j}) \star \text{self}(N_i, C_{\text{in}_j})$$

  Where $\star$ denotes the convolution operation, $N$ is the batch size, $C$ is the number of channels, and $W$ and $H$ represent the width and height, respectively.

[object Object]

Each operator has [object Object]two-phase API calls[object Object]. First, aclnnConvDepthwise2dGetWorkspaceSize is called to obtain the workspace size required for computation and the executor that contains the operator computation flow. Then, aclnnConvDepthwise2d is called to perform computation.

[object Object]

• Parameters

  [object Object]

• Return Value

  aclnnStatus: status code. For details, see [object Object]aclnn Return Codes[object Object].

  The first-phase API implements input parameter verification. The following errors may be thrown.

  [object Object]

[object Object]

• Parameters

  [object Object]

• Return Value

  aclnnStatus: status code. For details, see [object Object]aclnn Return Codes[object Object].

[object Object]

• Deterministic computation:
  • aclnnConvDepthwise2d defaults to a deterministic implementation.

[object Object][object Object]

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