mirror of
https://github.com/lordmathis/CUDANet.git
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91 lines
2.1 KiB
Markdown
91 lines
2.1 KiB
Markdown
# CUDANet
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Convolutional Neural Network inference library running on CUDA.
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## Quickstart Guide
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**requirements**
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- [cmake](https://cmake.org/)
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- [CUDA](https://developer.nvidia.com/cuda-downloads)
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- [Google Test](https://github.com/google/googletest) (for testing only)
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**build**
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```sh
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mkdir build
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cd build
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cmake -S .. -DCMAKE_CUDA_ARCHITECTURES=75 # Replace with you cuda architecture
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make
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```
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**build and run tests**
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```sh
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make test_main
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./test/test_main
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```
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### Create Layers and Model
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```cpp
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CUDANet::Model *model =
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new CUDANet::Model(inputSize, inputChannels, outputSize);
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// Conv2d
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CUDANet::Layers::Conv2d *conv2d = new CUDANet::Layers::Conv2d(
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inputSize, inputChannels, kernelSize, stride, numFilters,
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CUDANet::Layers::Padding::VALID,
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CUDANet::Layers::ActivationType::NONE
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);
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if (setWeights) {
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conv2d->setWeights(getConv1Weights().data());
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}
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model->addLayer("conv1", conv2d);
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```
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### Sequential and Functional API
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Run prediction by passing the input through the layers in the order they have been added.
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```cpp
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std::vector<float> input = {...};
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model->predict(input.data());
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```
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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
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```cpp
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class MyModel : public CUDANet::Model {
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...
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}
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...
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float* MyModel::predict(const float* input) {
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float* d_input = inputLayer->forward(input);
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d_conv1 = getLayer("conv1")->forward(d_input);
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d_conv2 = getLayer("conv2")->forward(d_input);
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d_output = concatLayer->forward(d_conv1, d_conv2);
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return outputLayer->forward(d_input);
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}
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```
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### Load Pre-trained Weights
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CUDANet uses format similar to safetensors to load weights and biases.
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```
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[u_short version, u_int64 header size, header, tensor values]
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```
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where `header` is a csv format
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```
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<tensor_name>,<tensor_size>,<tensor_offset>
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```
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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 |