MaxPool2D

class MaxPool2D(pool_size=(2, 2), strides=None, padding=0, layout='NCHW', ceil_mode=False, **kwargs)

Max pooling operation for two dimensional (spatial) data.

Parameters:

pool_size: int or list/tuple of 2 ints,

Size of the max pooling windows.

strides: int, list/tuple of 2 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 2 ints,

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

layout : str, default ‘NCHW’

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

ceil_mode : bool, default False

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

Inputs:

• data: 4D input tensor with shape (batch_size, in_channels, height, width) when layout is NCHW. For other layouts shape is permuted accordingly.

Outputs:

• out: 4D output tensor with shape (batch_size, channels, out_height, out_width) when layout is NCHW. out_height and out_width are calculated as:

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

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