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