convolutions-rs

convolutions-rs is a 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.

Features

• [x] Minimal dependencies, especially no C-dependencies
• [x] Extensively tested through randomly generated unit tests
• [x] 100% compatible with Tensorflow and Pytorch implementations
• [ ] Speed verified by benchmarking
• [ ] Generics to ensure smooth usage

Usage

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 = Array::fromshapevec( (2,1,2,2), vec![1.,1.,1.,1.,1.,1.,1.,1.] ) .unwrap();

let convlayer = ConvolutionLayer::new(kernel.clone(), 1, Padding::Valid); let outputlayer: Array3 = convlayer.convolve(&input); let outputfree = conv2d(kernel, &input, Padding::Valid, 1);

println!("Layer: {:?}", outputlayer); println!("Free: {:?}", outputfree); ```