half the dimension of a numpy array

Question

Lets say I have a numpy array of 4x4 dimension and want to change it to 2x2 by taking its halve. So, theoretically do something like this:

is this possible without using any loop and for it to work on not only a 4x4 but lets say a 500x500?

#input:
x_4= np.array([[1, 2, 4, 5], [3, 4, 6, 8], [5, 3, 1, -1], [2, 3, 5, 0]])

# thinking it would work with something like this:
new = x_4[:2, :2]/4 + x_4[:2, -2:]/4 + x_4[-2:, :2]/4 + x_4[-2:, -2:]/4
new
# output: array([[11,  9],[16, 15]])

#Expected output: array([[2.5, 5.75], [3.25, 1.25]])

Yes! This operation is called _average pooling_. – jkr Jul 21 '22 at 22:27 — jkr, Jul 21 '22 at 22:27
both solutions work perfectly, thank you – zay_117 Jul 21 '22 at 22:50 — zay_117, Jul 21 '22 at 22:50

score 3 · Accepted Answer · answered Jul 21 '22 at 22:46

Numpy Version:

you can do a reshape and perform mean over two axis to get the desired result

import numpy as np

blocksize = 500

Mat = np.random.rand(blocksize,blocksize)

## reshape into (blocksize/2 x blocksize/2 ) 2x2 matrices

blocks = Mat.reshape(blocksize//2, 2, blocksize//2, 2)

block_mean = np.mean(blocks, axis=(1,-1))

score 0 · Answer 2 · answered Jul 21 '22 at 22:40

0

As was pointed out in the comments, you can use pooling, which is e.g. available in the scikit-image package:

import skimage.measure
shape = (2, 2)
skimage.measure.block_reduce(x_4, shape, np.mean)

Where shape gives you the dimensions of your pools.

answered Jul 21 '22 at 22:40

Pickniclas

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1
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score 0 · Answer 3 · answered Jul 21 '22 at 22:51

This Operation called average Pooling it used in CNN and image processing to reduce the dimension of the image you can use TensorFlow or PyTorch first you need to reshape the image to (batch_size,Channels,Rows,Columns) for PyTorch to work

import numpy as np
import torch
from torch import nn
m= nn.AvgPool2d(2, stride=2)
x_4= np.array([[1, 2, 4, 5], [3, 4, 6, 8], [5, 3, 1, -1], [2, 3, 5, 0]])
x_4=x_4[None,None,:,:]
x_4=torch.as_tensor(x_4,dtype=torch.float64)
x_4.shape
m(x_4).numpy()

Output

array([[[[2.5 , 5.75],
         [3.25, 1.25]]]])

half the dimension of a numpy array

3 Answers3

Output