[object Object]

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

• Description: Implements convolution operations, supporting 1D, 2D, and 3D convolutions, as well as transposed convolution, dilated convolution, and grouped convolution. When [object Object], the operator performs transposed convolution (also known as fractionally-strided convolution). This can be viewed as the gradient or inverse operation of regular convolution: it maps the output shape back to the input shape while maintaining a connection pattern compatible with convolution. Its parameters are similar to those of regular convolution, including input channels, output channels, kernel size, stride, padding, output padding, groups, bias, and dilation.

• Formula:

  Assume the input tensor has shape $(N, C_{\text{in}}, D, H, W)$, the weight tensor has shape $(C_{\text{out}}, C_{\text{in}}, K_d, K_h, K_w)$, and the output tensor has shape $(N, C_{\text{out}}, D_{\text{out}}, H_{\text{out}}, W_{\text{out}})$, where $N$ denotes the batch size, $C$ denotes the number of channels, $D$, $H$, and $W$ denote the depth, height, and width of the sample respectively, and $K_d$, $K_h$, and $K_w$ denote the depth, height, and width of the kernel respectively. The output is then expressed as:

  $$\text{output}(N_i, C_{\text{out}_j}, D_{\text{out}}, H_{\text{out}}, W_{\text{out}}) = \sum_{k = 0}^{C_{\text{in}} - 1} \text{weight}(C_{\text{out}_j}, k) \star \text{input}(N_i, k) + \text{bias}(C_{\text{out}_j})$$

  Where $\star$ denotes the convolution operation, whose exact computation depends on the input dimensionality and the convolution type (dilated or grouped). $N$ denotes the batch size, $C$ denotes the number of channels, and $D$, $H$, and $W$ denote the depth, height, and width, respectively. The formulas for computing the corresponding output dimensions are as follows:

  • When [object Object]:

    $$D_{\text{out}}=[(D + 2 \times padding[0] - dilation[0] \times (K_d - 1) - 1 ) / stride[0]] + 1 \\ H_{\text{out}}=[(H + 2 \times padding[1] - dilation[1] \times (K_h - 1) - 1 ) / stride[1]] + 1 \\ W_{\text{out}}=[(W + 2 \times padding[2] - dilation[2] \times (K_w - 1) - 1 ) / stride[2]] + 1$$

  • When [object Object]:

    $$D_{\text{out}}=(D - 1) \times \text{stride}[0] - 2 \times \text{padding}[0] + \text{dilation}[0] \times (K_d - 1) + \text {outputPadding}[0] + 1 \\ H_{\text{out}}=(H - 1) \times \text{stride}[1] - 2 \times \text{padding}[1] + \text{dilation}[1] \times (K_h - 1) + \text {outputPadding}[1] + 1 \\ W_{\text{out}}=(W - 1) \times \text{stride}[2] - 2 \times \text{padding}[2] + \text{dilation}[2] \times (K_w - 1) + \text {outputPadding}[2] + 1$$

[object Object]

Each operator has [object Object]two-phase API calls[object Object]. You must call aclnnConvolutionGetWorkspaceSize to obtain the workspace size required for computation and the executor that contains the operator computation process, and then call aclnnConvolution to perform the 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: return status code. For details, see [object Object]aclnn Return Codes[object Object].

[object Object]

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

[object Object][object Object]

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