convolutions-rs is a crate that provides a fast, well-tested convolutions library for machine learning written entirely in Rust with minimal dependencies. In particular, this is the first convolutions crate that has absolutely no dependencies on native C libraries. We provide both transposed convolutions (also called deconvolutions), as well as normal convolutions.
This crate has been developed in the course of the ZipNet project (https://github.com/Conzel/zipnet), where we required a C-free implementation of convolutions in order to compile our code to WebASM.
As of now, this crate is as fast as Pytorch on small images, but has a noticeable slowdown on large and medium images (takes ~20-50x as much time). We are still reaonsably fast enough for research/sample applications, but we aim to improve the speed to get close to PyTorch. Benchmarks can be found at https://github.com/Conzel/convolutions-rs-benchmarks/.
As mentioned, this package provides normal convolutions as well as transposed convolutions. We provide both in the form of free functions as well as something resembling a neural network layer. This crate also requires ndarray to use the functions, as input and output are in the form of ndarrays.
Example: ``` use convolutionsrs::convolutions::*; use ndarray::*; use convolutionsrs::Padding;
// Input has shape (channels, height, width) let input = Array::fromshapevec( (1, 4, 4), vec![1.,2.,3.,4.,5.,6.,7.,8.,9.,10.,11.,12.,13.,14.,15.,16.] ) .unwrap();
// Kernel has shape (channels out, channels in, height, width)
let kernel: Array4
let convlayer = ConvolutionLayer::new(kernel.clone(), 1, Padding::Valid);
let outputlayer: Array3
println!("Layer: {:?}", outputlayer); println!("Free: {:?}", outputfree); ```