This question has been asked multiple times but still I could not get what I was looking for. Imagine
data=np.random.rand(N,N) #shape N x N
kernel=np.random.rand(3,3) #shape M x M
I know convolution typically means placing the kernel all over the data. But in my case N and M are of the orders of 10000. So I wish to get the value of the convolution at a specific location in the data, say at (10,37) without doing unnecessary calculations at all locations. So the output will be just a number. The main goal is to reduce the computation and memory expenses. Is there any inbuilt function that does this with minimal adjustments?
>Solution :
Indeed, applying the convolution for a particular position coincides with the mere sum over the entries of a (pointwise) multiplication of the submatrix in data and the flipped kernel itself. Here, is a reproducible example.
Code
N = 1000
M = 3
np.random.seed(777)
data = np.random.rand(N,N) #shape N x N
kernel= np.random.rand(M,M) #shape M x M
# Pointwise convolution = pointwise product
data[10:10+M,37:37+M]*kernel[::-1, ::-1]
>array([[0.70980514, 0.37426475, 0.02392947],
[0.24387766, 0.1985901 , 0.01103323],
[0.06321042, 0.57352696, 0.25606805]])
with output
conv = np.sum(data[10:10+M,37:37+M]*kernel[::-1, ::-1])
conv
>2.45430578
The kernel is being flipped by definition of the convolution as explained in here and was kindly pointed Warren Weckesser. Thanks!
The key is to make sense of the index you provided. I assumed it refers to the upper left corner of the sub-matrix in data. However, it can refer to the midpoint as well when M is odd.
Concept
A different example with N=7 and M=3 exemplifies the idea
and is presented in here.