AvgPool2d

class torch.nn.AvgPool2d(kernel_size, stride=None, padding=0, ceil_mode=False, count_include_pad=True, divisor_override=None)

Applies a 2D average pooling over an input signal composed of several input planes.

In the simplest case, the output value of the layer with input size $(N, C, H, W)$, output $(N, C, H_{out}, W_{out})$ and kernel_size $(kH, kW)$ can be precisely described as:

$$out(N_i, C_j, h, w) = \frac{1}{kH * kW} \sum_{m=0}^{kH-1} \sum_{n=0}^{kW-1} input(N_i, C_j, stride[0] \times h + m, stride[1] \times w + n)$$

If padding is non-zero, then the input is implicitly zero-padded on both 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, padding can either be:

• a single int – in which case the same value is used for the height and width dimension

• a tuple of two ints – in which case, the first int is used for the height dimension, and the second int for the width dimension

Parameters

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

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

• padding (Union[int, Tuple[int, int]]) – implicit zero padding to be added on both 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 size of the pooling region will be used.

Shape:

• Input: $(N, C, H_{in}, W_{in})$ or $(C, H_{in}, W_{in})$.

• Output: $(N, C, H_{out}, W_{out})$ or $(C, H_{out}, W_{out})$, where

  $$H_{out} = \left\lfloor\frac{H_{in} + 2 \times \text{padding}[0] - \text{kernel\_size}[0]}{\text{stride}[0]} + 1\right\rfloor$$
  $$W_{out} = \left\lfloor\frac{W_{in} + 2 \times \text{padding}[1] - \text{kernel\_size}[1]}{\text{stride}[1]} + 1\right\rfloor$$

  Per the note above, if ceil_mode is True and $(H_{out} - 1)\times \text{stride}[0]\geq H_{in} + \text{padding}[0]$, we skip the last window as it would start in the bottom padded region, resulting in $H_{out}$ being reduced by one.

  The same applies for $W_{out}$.

Examples:

>>> # pool of square window of size=3, stride=2
>>> m = nn.AvgPool2d(3, stride=2)
>>> # pool of non-square window
>>> m = nn.AvgPool2d((3, 2), stride=(2, 1))
>>> input = torch.randn(20, 16, 50, 32)
>>> output = m(input)