AvgPool3D

class AvgPool3D(pool_size=(2, 2, 2), strides=None, padding=0, ceil_mode=False, layout='NCDHW', count_include_pad=True, **kwargs)

Average pooling operation for 3D data (spatial or spatio-temporal).

Parameters:

pool_size: int or list/tuple of 3 ints,

Size of the average pooling windows.

strides: int, list/tuple of 3 ints, or None.

Factor by which to downscale. E.g. 2 will halve the input size. If None, it will default to pool_size.

padding: int or list/tuple of 3 ints,

If padding is non-zero, then the input is implicitly zero-padded on both sides for padding number of points.

layout : str, default ‘NCDHW’

Dimension ordering of data and out (‘NCDHW’ or ‘NDHWC’). ‘N’, ‘C’, ‘H’, ‘W’, ‘D’ stands for batch, channel, height, width and depth dimensions respectively. padding is applied on ‘D’, ‘H’ and ‘W’ dimension.

ceil_mode : bool, default False

When True, will use ceil instead of floor to compute the output shape.

count_include_pad : bool, default True

When ‘False’, will exclude padding elements when computing the average value.

Inputs:

• data: 5D input tensor with shape (batch_size, in_channels, depth, height, width) when layout is NCDHW. For other layouts shape is permuted accordingly.

Outputs:

• out: 5D output tensor with shape (batch_size, channels, out_depth, out_height, out_width) when layout is NCDHW. out_depth, out_height and out_width are calculated as:

  out_depth = floor((depth+2*padding[0]-pool_size[0])/strides[0])+1
  out_height = floor((height+2*padding[1]-pool_size[1])/strides[1])+1
  out_width = floor((width+2*padding[2]-pool_size[2])/strides[2])+1
  

  When ceil_mode is True, ceil will be used instead of floor in this equation.