RTNeural

A lightweight neural network inferencing engine written in C++. This library was designed with the intention of being used in real-time audio processing, but may be useful for other tasks as well.

Currently supported layers:

• dense
• GRU
• LSTM
• Conv1D

Currently supported activations:

• tanh
• ReLU
• Sigmoid
• SoftMax

How To Use

RTNeural is capable of taking a neural network that has already been trained, loading the weights from that network, and running inference.

Exporting weights from a trained network

Neural networks are typically trained using Python libraries including Tensorflow or PyTorch. Once you have trained a neural network using one of these frameworks, you must "export" the network weights to a json file, so that RTNeural can read them. An implementation of the export process for a Tensorflow model is provided in python/model_utils.py, and can be used as follows.

# import dependencies
import tensorflow as tf
from tensorflow import keras
from model_utils import save_model

# create Tensrflow model
model = keras.Sequential()
...

# train model
model.train()

# export model weights
save_model(model, 'model_weights.json')

Creating a model

Next, you can create an inferencing engine in C++ directly from the exported json file:

#include <RTNeural.h>
...
std::ifstream jsonStream("model_weights.json", std::ifstream::binary);
auto model = RTNeural::json_parser::parseJson<double>(jsonStream);

Running inference

Before running inference, it is recommended to "reset" the state of your model (if the model has state).

model->reset();

Then, you may run inference as follows:

double input[] = { 1.0, 0.5, -0.1 }; // set up input vector
double output = model->forward(input); // compute output

Building with CMake

RTNeural is built with CMake, and the easiest way to link is to include RTNeural as a submodule:

...
add_subdirectory(RTNeural)
include_directories(RTNeural)
...
target_link_libraries(MyCMakeProject LINK_PUBLIC RTNeural)

Choosing a Backend

RTNeural supports three backends, Eigen, xsimd or the C++ STL. You can choose your backend by passing either -DRTNEURAL_EIGEN=ON, -DRTNEURAL_XSIMD=ON, or -DRTNEURAL_STL=ON to your CMake configuration. By default, the Eigen backend will be used.

While the Eigen backend typically has the best performance, it is recommended to measure the performance of your network with all the backends that available on your target platform to ensure optimal performance. For more information see the benchmark results.

Building the Accuracy Tests

To build the accuracy tests, run cmake -Bbuild -DBUILD_TESTS=ON, followed by cmake --build build. To run the full testing suite, run ./build/rtneural_tests all. For more information, run ./buildrtneural_tests --help.

Building the Performance Benchmarks

To build the performance benchmarks, run cmake -Bbuild -DBUILD_BENCH=ON, followed by cmake --build build. To run the layer benchmarks, run ./build/rtneural_layer_bench <layer> <length> <in_size> <out_size>.

Contributing

Contributions to this project are most welcome! Currently, there is considerable need for the following improvements:

• Support for convolution layers
• Support for LSTMs
• Support for exporting/loading PyTorch models
• More robust support for exporting/loading Tensorflow models
• Support for more activation layers
• Better testing
• Better performance measurements

General code maintenance and documentation is always appreciated as well! Note that if you are implementing a new layer type, it is not required to provide support for all three backends, though it is recommended to at least provide a "fallback" implementation using the STL backend.

Related Projects

• Chow Centaur: A guitar pedal emulation plugin, using a real-time recurrent neural network.
• Chow Tape Model: An analog tape emulation, using a real-time dense neural network.

License

RTNeural is open source, and is licensed under the BSD 3-clause license.

Enjoy!