ndarray-conv

ndarray-conv is a crate that provides a fast convolutions library in pure Rust.

Inspired by

ndarray-vision (https://github.com/rust-cv/ndarray-vision)

convolutions-rs (https://github.com/Conzel/convolutions-rs#readme)

pocketfft (https://github.com/mreineck/pocketfft)

ndarray-conv is still under heavily developing, the first stage aims to provide a fast conv_2d func for ndarray::Array2.

First Stage

• basic conv_2d
• use fft to accelerate big kernel's conv_2d computation
• impl padding_size and padding_mode
  • PaddingSize: Full Same Valid Custom Explicit
  • PaddingMode: Zeros Const Reflect Replicate Circular Custom Explicit
• strides for kernel and data
• explict error type

Roughly Bench

conv_2d

2x-4x faster than ndarray-vision and 4x-10x faster than convolutions-rs. 2x slower than opencv with small kernel (size < 11)

conv_2d_fft

10x~ faster than ndarray-vision and convolutions-rs

as fast as opencv on large data and kernel (2000, 5000) * (21, 41)

2x faster than opencv on much larger data and kernel

Example

    use ndarray_conv::*;
    x.conv_2d(&k);

fn main() {
    use ndarray_conv::*;
    use ndarray::prelude::*;
    use ndarray_rand::rand_distr::Uniform;
    use ndarray_rand::RandomExt;
    use std::time::Instant;

    let mut small_duration = 0u128;
    let test_cycles_small = 1;
    // small input data
    for _ in 0..test_cycles_small {
        let x = Array::random((2000, 4000), Uniform::new(0., 1.));
        let k = Array::random((9, 9), Uniform::new(0., 1.));

        let now = Instant::now();
        // or use x.conv_2d_fft() for large input data
        x.conv_2d(
            &k,
            PaddingSize::Same,
            PaddingMode::Custom([BorderType::Reflect, BorderType::Circular]),
        );
        small_duration += now.elapsed().as_nanos();
    }

    println!(
        "Time for small arrays, {} iterations: {} milliseconds",
        test_cycles_small,
        small_duration / 1_000_000
    );
}