CUDANet
Convolutional Neural Network inference library running on CUDA.
Quickstart Guide
requirements
- cmake
- CUDA
- Google Test (for testing only)
build
mkdir build
cd build
cmake -S .. -DCMAKE_CUDA_ARCHITECTURES=75 # Replace with you cuda architecture
make
cd build
cmake -S .. -DCMAKE_CUDA_ARCHITECTURES=75 # Replace with you cuda architecture
make
build and run tests
make test_main
./test/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);
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());
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);
}
...
}
...
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.
[u_short version, u_int64 header size, header, tensor values]
where header is a csv format
,,
To load weights call load_weights function on Model object. To export weights from pytorch you can use the export_model_weights function from tools/utils.py script. Currently only float32 weights are supported