/PyTorch

# AvgPool3d

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 $(N, C, D, H, W)$ , output $(N, C, D_{out}, H_{out}, W_{out})$ and kernel_size $(kD, kH, kW)$ can be precisely described as:

\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} \times d + k, \text{stride} \times h + m, \text{stride} \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
Parameters
• 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
Shape:
• Input: $(N, C, D_{in}, H_{in}, W_{in})$
• Output: $(N, C, D_{out}, H_{out}, W_{out})$ , where

$D_{out} = \left\lfloor\frac{D_{in} + 2 \times \text{padding} - \text{kernel\_size}}{\text{stride}} + 1\right\rfloor$
$H_{out} = \left\lfloor\frac{H_{in} + 2 \times \text{padding} - \text{kernel\_size}}{\text{stride}} + 1\right\rfloor$
$W_{out} = \left\lfloor\frac{W_{in} + 2 \times \text{padding} - \text{kernel\_size}}{\text{stride}} + 1\right\rfloor$

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)


© 2019 Torch Contributors
Licensed under the 3-clause BSD License.
https://pytorch.org/docs/1.7.0/generated/torch.nn.AvgPool3d.html