CUDANet

⚠️ Work in progress

Convolutional Neural Network inference library running on CUDA.

Features

• Input layer
• Dense (fully-connected) layer
• Conv2d layer
• Max pooling
• Average pooling
• Concat layer
• Sigmoid activation
• ReLU activation
• Softmax activation
• Load weights from file

Usage

requirements

• cmake
• CUDA
• Google Test (for testing only)

build

mkdir build
cd build
cmake -S ..
make

build and run tests

make test_main
./test/test_main

Create Layers and Model

CUDANet::Model *model =
    new CUDANet::Model(inputSize, inputChannels, outputSize);

// Conv2d
CUDANet::Layers::Conv2d *conv2d = new CUDANet::Layers::Conv2d(
    inputSize, inputChannels, kernelSize, stride, numFilters,
    CUDANet::Layers::Padding::VALID,
    CUDANet::Layers::ActivationType::NONE
);

if (setWeights) {
    conv2d->setWeights(getConv1Weights().data());
}
model->addLayer("conv1", conv2d);

Sequential and Functional API

Run prediction by passing the input through the layers in the order they have been added.

std::vector<float> input = {...};
model->predict(input.data());

If you want to use more complex forward pass, using Concat or Add layers, you can subclass the model class and override the default predict function

class MyModel : public CUDANet::Model {
    ...
}

...

float* MyModel::predict(const float* input) {
    float* d_input = inputLayer->forward(input);

    d_conv1 = getLayer("conv1")->forward(d_input);
    d_conv2 = getLayer("conv2")->forward(d_input);

    d_output = concatLayer->forward(d_conv1, d_conv2);

    return outputLayer->forward(d_input);
}

Load Pre-trained Weights

CUDANet uses format similar to safetensors to load weights and biases.

[int64 header size, header, tensor values]

where header is a csv format

<tensor_name>,<tensor_size>,<tensor_offset>

To load weights call load_weights function on Model object.

To export weights from pytorch you can use tools/export_model_weights.py script