class torch.nn.AvgPool3d(kernel_size: Union[T, Tuple[T, T, T]], stride: Optional[Union[T, Tuple[T, T, T]]] = None, padding: Union[T, Tuple[T, T, T]] = 0, ceil_mode: bool = False, count_include_pad: bool = True, divisor_override=None)[source]

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) , output (N,C,Dout,Hout,Wout)(N, C, D_{out}, H_{out}, W_{out}) and kernel_size (kD,kH,kW)(kD, kH, kW) can be precisely described as:

out(Ni,Cj,d,h,w)=k=0kD1m=0kH1n=0kW1input(Ni,Cj,stride[0]×d+k,stride[1]×h+m,stride[2]×w+n)kD×kH×kW\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}

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

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

  • kernel_size – the size of the window

  • stride – the stride of the window. Default value is kernel_size

  • padding – implicit zero padding to be added on all three sides

  • ceil_mode – when True, will use ceil instead of floor to compute the output shape

  • count_include_pad – when True, will include the zero-padding in the averaging calculation

  • divisor_override – if specified, it will be used as divisor, otherwise kernel_size will be used

  • Input: (N,C,Din,Hin,Win)(N, C, D_{in}, H_{in}, W_{in})

  • Output: (N,C,Dout,Hout,Wout)(N, C, D_{out}, H_{out}, W_{out}) , where

    Dout=Din+2×padding[0]kernel_size[0]stride[0]+1D_{out} = \left\lfloor\frac{D_{in} + 2 \times \text{padding}[0] - \text{kernel\_size}[0]}{\text{stride}[0]} + 1\right\rfloor
    Hout=Hin+2×padding[1]kernel_size[1]stride[1]+1H_{out} = \left\lfloor\frac{H_{in} + 2 \times \text{padding}[1] - \text{kernel\_size}[1]}{\text{stride}[1]} + 1\right\rfloor
    Wout=Win+2×padding[2]kernel_size[2]stride[2]+1W_{out} = \left\lfloor\frac{W_{in} + 2 \times \text{padding}[2] - \text{kernel\_size}[2]}{\text{stride}[2]} + 1\right\rfloor


>>> # 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)


Access comprehensive developer documentation for PyTorch

View Docs


Get in-depth tutorials for beginners and advanced developers

View Tutorials


Find development resources and get your questions answered

View Resources