Table of Contents
Shortcuts

# AvgPool3d

class torch.nn.AvgPool3d(kernel_size, stride=None, padding=0, ceil_mode=False, count_include_pad=True, divisor_override=None)

  Applies a 3D average pooling over an input signal composed of several input planes.

  In the simplest case, the output value of the layer with input size (N,C,D,H,W)(N, C, D, H, W)(N,C,D,H,W), output (N,C,D o u t,H o u t,W o u t)(N, C, D_{out}, H_{out}, W_{out})(N,C,D o u t​,H o u t​,W o u t​) and `kernel_size` (k D,k H,k W)(kD, kH, kW)(k D,k H,kW) can be precisely described as:

  out(N i,C j,d,h,w)=∑k=0 k D−1∑m=0 k H−1∑n=0 k W−1 input(N i,C j,stride[0]×d+k,stride[1]×h+m,stride[2]×w+n)k D×k H×k W\begin{aligned} \text{out}(N_i, C_j, d, h, w) ={} & \sum_{k=0}^{kD-1} \sum_{m=0}^{kH-1} \sum_{n=0}^{kW-1} \\ & \frac{\text{input}(N_i, C_j, \text{stride}[0] \times d + k, \text{stride}[1] \times h + m, \text{stride}[2] \times w + n)} {kD \times kH \times kW} \end{aligned} out(N i​,C j​,d,h,w)=​k=0∑k D−1​m=0∑k H−1​n=0∑kW−1​k D×k H×kW input(N i​,C j​,stride[0]×d+k,stride[1]×h+m,stride[2]×w+n)​​

  If `padding` is non-zero, then the input is implicitly zero-padded on all three sides for `padding` number of points.

  Note

  When ceil_mode=True, sliding windows are allowed to go off-bounds if they start within the left padding or the input. Sliding windows that would start in the right padded region are ignored.

  The parameters `kernel_size`, `stride` can either be:

  > - a single `int` – in which case the same value is used for the depth, height and width dimension

  > - a `tuple` of three ints – in which case, the first int is used for the depth dimension, the second int for the height dimension and the third int for the width dimension

  Parameters:

    - kernel_size (Union[int, Tuple[int, int, int]]) – the size of the window

    - stride (Union[int, Tuple[int, int, int]]) – the stride of the window. Default value is `kernel_size`

    - padding (Union[int, Tuple[int, int, int]]) – implicit zero padding to be added on all three sides

    - ceil_mode (bool) – when True, will use ceil instead of floor to compute the output shape

    - count_include_pad (bool) – when True, will include the zero-padding in the averaging calculation

    - divisor_override (Optional[int]) – if specified, it will be used as divisor, otherwise `kernel_size` will be used

  Shape:

    - Input: (N,C,D i n,H i n,W i n)(N, C, D_{in}, H_{in}, W_{in})(N,C,D in​,H in​,W in​) or (C,D i n,H i n,W i n)(C, D_{in}, H_{in}, W_{in})(C,D in​,H in​,W in​).

    - Output: (N,C,D o u t,H o u t,W o u t)(N, C, D_{out}, H_{out}, W_{out})(N,C,D o u t​,H o u t​,W o u t​) or (C,D o u t,H o u t,W o u t)(C, D_{out}, H_{out}, W_{out})(C,D o u t​,H o u t​,W o u t​), where

      D o u t=⌊D i n+2×padding[0]−kernel_size[0]stride[0]+1⌋D_{out} = \left\lfloor\frac{D_{in} + 2 \times \text{padding}[0] - \text{kernel\_size}[0]}{\text{stride}[0]} + 1\right\rfloor D o u t​=⌊stride[0]D in​+2×padding[0]−kernel_size[0]​+1⌋
      H o u t=⌊H i n+2×padding[1]−kernel_size[1]stride[1]+1⌋H_{out} = \left\lfloor\frac{H_{in} + 2 \times \text{padding}[1] - \text{kernel\_size}[1]}{\text{stride}[1]} + 1\right\rfloor H o u t​=⌊stride[1]H in​+2×padding[1]−kernel_size[1]​+1⌋
      W o u t=⌊W i n+2×padding[2]−kernel_size[2]stride[2]+1⌋W_{out} = \left\lfloor\frac{W_{in} + 2 \times \text{padding}[2] - \text{kernel\_size}[2]}{\text{stride}[2]} + 1\right\rfloor W o u t​=⌊stride[2]W in​+2×padding[2]−kernel_size[2]​+1⌋

  Examples:

      >>> # pool of square window of size=3, stride=2
      >>> m = nn.AvgPool3d(3, stride=2)
      >>> # pool of non-square window
      >>> m = nn.AvgPool3d((3, 2, 2), stride=(2, 1, 2))
      >>> input = torch.randn(20, 16, 50, 44, 31)
      >>> output = m(input)

## Docs

Access comprehensive developer documentation for PyTorch

View Docs

## Tutorials

Get in-depth tutorials for beginners and advanced developers

View Tutorials

## Resources

Find development resources and get your questions answered

View Resources

To analyze traffic and optimize your experience, we serve cookies on this site. By clicking or navigating, you agree to allow our usage of cookies. As the current maintainers of this site, Facebook’s Cookies Policy applies. Learn more, including about available controls: Cookies Policy.