oneflow.nn.functional.avg_pool2d¶
-
oneflow.nn.functional.
avg_pool2d
(input, kernel_size, stride=None, padding=0, ceil_mode=False, count_include_pad=True, divisor_override=0) → 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.
The documentation is referenced from: https://pytorch.org/docs/1.10/generated/torch.nn.functional.avg_pool2d.html.
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 or a tuple (kH, kW)
stride – stride of the pooling operation. Can be a single number or a tuple (sH, sW). Default:
kernel_size
padding – implicit zero paddings on both sides of the input. Can be a single number or a tuple (padH, padW). 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: 0