ml-convolution
v2.0.0
Published
Convolution using the FFT or standard algorithm
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convolution
Convolution using the FFT or direct algorithm.
Installation
npm install ml-convolution
Usage
One execution
import { directConvolution, fftConvolution } from 'ml-convolution';
const input = [0, 1, 2, 3];
const kernel = [-1, 1, -1];
const outputDirect = directConvolution(input, kernel); // [-1, -1, -2, 1]
const outputFFT = fftConvolution(input, kernel); // [-1, -1, -2, 1]
The functions both take an optional third argument to determine the way borders
are processed. The default value, CONSTANT
, will consider that the values out
of the bounds are all 0. If it is set to CUT
, borders will be ignored and the
result will be smaller than te input by kernel.length - 1
:
const outputDirect = directConvolution(input, kernel, 'CUT'); // [-1, -2]
Optimized, multiple executions
If you need to execute the convolution many times with the same kernel and input length, you should consider instead to use the class-based API:
import { DirectConvolution, FFTConvolution } from 'ml-convolution';
// const input = [0, 255, 255, 255, 255, 0, 0, 0];
const kernel = [0.1, 0.2, 0.3];
// First parameter is the size of the inputs and allows to pre-allocate an array with the correct size
const direct = new DirectConvolution(8, kernel, 'CUT');
// The convolve function mutates the same array at each execution
direct.convolve([0, 255, 255, 255, 255, 0, 0, 0]); // [ 127.5, 153, 153, 76.5, 25.5, 0 ]
direct.convolve([255, 0, 0, 255, 255, 255, 0, 0]); // [ 25.5, 76.5, 127.5, 153, 76.5, 25.5 ]
const fft = new FFTConvolution(8, kernel, 'CONSTANT');
fft.convolve([0, 255, 255, 255, 255, 0, 0, 0]); // [ 76.5, 127.5, 153, 153, 76.5, 25.5, 0, 0 ]
fft.convolve([255, 0, 0, 255, 255, 255, 0, 0]); // [ 51, 25.5, 76.5, 127.5, 153, 76.5, 25.5, 0 ]
Benchmark
With small kernels, direct convolution is usually faster:
Current results suggest that from a kernel size around 64, FFT convolution should be used.
| Data x Kernel | fft [ops/s] | direct [ops/s] | | ------------- | ----------- | -------------- | | 128 x 5 | 97889 | 569110 | | 128 x 11 | 99403 | 280271 | | 128 x 17 | 97686 | 181608 | | 128 x 33 | 94633 | 93847 | | 128 x 65 | 96585 | 49320 | | 128 x 129 | 97189 | 25346 | | 128 x 513 | 21771 | 6469 | | 512 x 5 | 20712 | 144025 | | 512 x 11 | 21134 | 73189 | | 512 x 17 | 21201 | 44320 | | 512 x 33 | 21037 | 23591 | | 512 x 65 | 21398 | 12405 | | 512 x 129 | 21514 | 6358 | | 512 x 513 | 21494 | 1618 | | 2048 x 5 | 4746 | 36360 | | 2048 x 11 | 4740 | 18422 | | 2048 x 17 | 4735 | 11248 | | 2048 x 33 | 4689 | 5927 | | 2048 x 65 | 4740 | 3100 | | 2048 x 129 | 4741 | 1591 | | 2048 x 513 | 4753 | 405 | | 4096 x 5 | 2068 | 18201 | | 4096 x 11 | 2062 | 9241 | | 4096 x 17 | 2071 | 5629 | | 4096 x 33 | 2069 | 2976 | | 4096 x 65 | 2079 | 1551 | | 4096 x 129 | 2074 | 797 | | 4096 x 513 | 2079 | 203 | | 16384 x 5 | 370 | 4036 | | 16384 x 11 | 371 | 2295 | | 16384 x 17 | 377 | 1390 | | 16384 x 33 | 374 | 748 | | 16384 x 65 | 370 | 389 | | 16384 x 129 | 375 | 199 | | 16384 x 513 | 376 | 51 | | 65536 x 5 | 70 | 991 | | 65536 x 11 | 70 | 541 | | 65536 x 17 | 70 | 351 | | 65536 x 33 | 69 | 186 | | 65536 x 65 | 71 | 97 | | 65536 x 129 | 71 | 50 | | 65536 x 513 | 70 | 13 | | 262144 x 5 | 10 | 247 | | 262144 x 11 | 10 | 135 | | 262144 x 17 | 10 | 88 | | 262144 x 33 | 10 | 47 | | 262144 x 65 | 10 | 24 | | 262144 x 129 | 10 | 12 | | 262144 x 513 | 10 | 3 | | 1048576 x 5 | 2 | 60 | | 1048576 x 11 | 2 | 32 | | 1048576 x 17 | 2 | 22 | | 1048576 x 33 | 2 | 12 | | 1048576 x 65 | 2 | 6 | | 1048576 x 129 | 2 | 3 | | 1048576 x 513 | 2 | 1 |