[object Object]

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

• Description: Performs backpropagation of forward adaptive max pooling. The gradient is backfilled to the coordinate of the maximum value in each adaptive window. The coordinates are accumulated.
• Forward propagation formula: N (Batch) represents the batch size, C (Channels) represents the number of feature map channels, D (Depth) represents the feature map depth, H (Height) represents the feature map height, and W (Width) represents the feature map width. For the input self dimension $[N,C,D,H,W]$ and outputSize value $[D_o,H_o,W_o]$, the output dimension is $[N,C,D_o,H_o,W_o]$ and the index dimension is $[N,C,D_o,H_o,W_o]$. The calculation formula of each element $(l,m,n)$ in the corresponding tensor is as follows:$$D^{l}_{left} = \lfloor(l*D)/D_o\rfloor$$ $$D^{l}_{right} = \lceil((l+1)*D)/D_o\rceil$$ $$H^{m}_{left} = \lfloor(m*H)/H_o\rfloor$$ $$H^{m}_{right} = \lceil((m+1)*H)/H_o\rceil$$ $$W^{n}_{left} = \lfloor(n*W)/W_o\rfloor$$ $$W^{n}_{right} = \lceil((n+1)*W)/W_o\rceil$$ $$output(N,C,l,m,n)=\underset {i \in [D^{l}_{left}, D^{l}_{right}],j\in [H^m_{left},H^m_{right}], k \in [W^n_{left},W^n_{right}] }{max} input(N,C,i,j,k)$$ $$indices(N,C,l,m,n)=\underset {i \in [D^{l}_{left}, D^{l}_{right}],j\in [H^m_{left},H^m_{right}], k \in [W^n_{left},W^n_{right}] }{argmax} input(N,C,i,j,k)$$

[object Object]

Each operator has calls. First, aclnnAdaptiveMaxPool3dBackwardGetWorkspaceSize is called to obtain the workspace size required for computation and the executor that contains the operator computation process. Then, aclnnAdaptiveMaxPool3dBackward is called to perform computation.

[object Object]

• Parameters:

  [object Object]

• Returns:

  [object Object]: status code. For details, see .

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

  [object Object]

[object Object]

• Parameters:

  [object Object]

• Returns:

  [object Object]: status code. For details, see .

[object Object]

• Deterministic compute:

  • aclnnAdaptiveMaxPool3dBackward defaults to a non-deterministic implementation. You can call aclrtCtxSetSysParamOpt to enable deterministic compute.

• If the shape is not exactly divisible, the value of shape cannot exceed 2 to the power of 24. If the shape is exactly divisible, there is no such restriction.

[object Object]

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