torch.nn.functional.avg_pool2d

torch.nn.functional.avg_pool2d(input, kernel_size, stride=None, padding=0, ceil_mode=False, count_include_pad=True, divisor_override=None) → Tensor

Applies 2D average-pooling operation in $kH \times kW$ regions by step size $sH \times sW$ steps. The number of output features is equal to the number of input planes.

See AvgPool2d for details and output shape.

Parameters:

• input – input tensor $(\text{minibatch} , \text{in\_channels} , iH , iW)$

• kernel_size – size of the pooling region. Can be a single number, a single-element tuple or a tuple (kH, kW)

• stride – stride of the pooling operation. Can be a single number, a single-element tuple or a tuple (sH, sW). Default: kernel_size

• padding – implicit zero paddings on both sides of the input. Can be a single number, a single-element tuple or a tuple (padH, padW). Should be at most half of effective kernel size, that is $((kernelSize - 1) * dilation + 1) / 2$. Default: 0

• ceil_mode – when True, will use ceil instead of floor in the formula to compute the output shape. Default: False

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

• divisor_override – if specified, it will be used as divisor, otherwise size of the pooling region will be used. Default: None