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Add Cuda iniitialization in Classifier

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This commit is contained in:
Ricardo Montañana Gómez
2024-09-18 12:13:11 +02:00
parent d0955d9369
commit baa631dd66
5 changed files with 36 additions and 14 deletions
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14
bayesnet/classifiers/Classifier.cc
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@@ -9,7 +9,15 @@
#include "Classifier.h"
namespace bayesnet {
Classifier::Classifier(Network model) : model(model), m(0), n(0), metrics(Metrics()), fitted(false) {}
Classifier::Classifier(Network model) : model(model), m(0), n(0), metrics(Metrics()), fitted(false), device(torch::kCPU)
{
if (torch::cuda::is_available()) {
device = torch::Device(torch::kCUDA);
std::cout << "CUDA is available! Using GPU." << std::endl;
} else {
std::cout << "CUDA is not available. Using CPU." << std::endl;
}
}
const std::string CLASSIFIER_NOT_FITTED = "Classifier has not been fitted";
Classifier& Classifier::build(const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states, const torch::Tensor& weights, const Smoothing_t smoothing)
{
@@ -31,7 +39,7 @@ namespace bayesnet {
{
try {
auto yresized = torch::transpose(ytmp.view({ ytmp.size(0), 1 }), 0, 1);
dataset = torch::cat({ dataset, yresized }, 0);
dataset = torch::cat({ dataset, yresized }, 0).to(device);
}
catch (const std::exception& e) {
std::stringstream oss;
@@ -50,7 +58,7 @@ namespace bayesnet {
{
dataset = X;
buildDataset(y);
const torch::Tensor weights = torch::full({ dataset.size(1) }, 1.0 / dataset.size(1), torch::kDouble);
const torch::Tensor weights = torch::full({ dataset.size(1) }, 1.0 / dataset.size(1), torch::kDouble).to(device);
return build(features, className, states, weights, smoothing);
}
// X is nxm where n is the number of features and m the number of samples

1
bayesnet/classifiers/Classifier.h
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@@ -38,6 +38,7 @@ namespace bayesnet {
std::string dump_cpt() const override;
void setHyperparameters(const nlohmann::json& hyperparameters) override; //For classifiers that don't have hyperparameters
protected:
torch::Device device;
bool fitted;
unsigned int m, n; // m: number of samples, n: number of features
Network model;

2
bayesnet/network/Node.cc
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@@ -97,7 +97,7 @@ namespace bayesnet {
dimensions.push_back(numStates);
transform(parents.begin(), parents.end(), back_inserter(dimensions), [](const auto& parent) { return parent->getNumStates(); });
// Create a tensor of zeros with the dimensions of the CPT
cpTable = torch::zeros(dimensions, torch::kDouble) + smoothing;
cpTable = torch::zeros(dimensions, torch::kDouble).to(device) + smoothing;
// Fill table with counts
auto pos = find(features.begin(), features.end(), name);
if (pos == features.end()) {