[FIXED] how to perform max/mean pooling on a 2d array using numpy

Issue

Given a 2D(M x N) matrix, and a 2D Kernel(K x L), how do i return a matrix that is the result of max or mean pooling using the given kernel over the image?

I'd like to use numpy if possible.

Note: M, N, K, L can be both even or odd and they need not be perfectly divisible by each other, eg: 7x5 matrix and 2x2 kernel.

eg of max pooling:

matrix:
array([[  20,  200,   -5,   23],
       [ -13,  134,  119,  100],
       [ 120,   32,   49,   25],
       [-120,   12,   09,   23]])
kernel: 2 x 2
soln:
array([[  200,  119],
       [  120,   49]])

Solution

You could use scikit-image block_reduce:

import numpy as np
import skimage.measure

a = np.array([
      [  20,  200,   -5,   23],
      [ -13,  134,  119,  100],
      [ 120,   32,   49,   25],
      [-120,   12,    9,   23]
])
skimage.measure.block_reduce(a, (2,2), np.max)

Gives:

array([[200, 119],
       [120,  49]])

Answered By - mdh