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
- MaxPooling
- BatchNorm
Currently supported activations:
- tanh
- ReLU
- Sigmoid
- SoftMax
RTNeural is capable of taking a neural network that
has already been trained, loading the weights from that
network, and running inference. An example of using
RTNeural within an audio plugin can be found on GitHub
here.
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')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);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 outputRTNeural 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)RTNeural supports three backends,
Eigen,
xsimd,
Accelerate,
or the C++ STL. You can choose your backend by passing
either -DRTNEURAL_EIGEN=ON, -DRTNEURAL_XSIMD=ON,
-DRTNEURAL_ACCELERATE=ON, or -DRTNEURAL_STL=ON
to your CMake configuration. By default, the Eigen
backend will be used. Please note that the Accelerate
backend can only be used when compiling for Apple devices.
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.
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.
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>.
Contributions to this project are most welcome! Currently, there is considerable need for the following improvements:
- Better implementation of convolutional layers:
- Faster implementations for Eigen and XSimd
- Implement more options (grouping, stride, etc...)
- Implement COnv2D
- 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.
- 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.
RTNeural is open source, and is licensed under the BSD 3-clause license.
Enjoy!