public void TestAlexNetCiFar()
{
CancelEvent evtCancel = new CancelEvent();
SettingsCaffe settings = new SettingsCaffe();
settings.ImageDbLoadMethod = IMAGEDB_LOAD_METHOD.LOAD_ON_DEMAND;
settings.EnableRandomInputSelection = true;
settings.GpuIds = getGpuIds();
Trace.WriteLine("Running TestAlexNetCiFar on GPU " + settings.GpuIds);
ProjectEx p = getProject();
MyCaffeControl <T> ctrl = new MyCaffeControl <T>(settings, m_log, evtCancel);
try
{
ctrl.Load(Phase.TRAIN, p);
ctrl.OnTrainingIteration += ctrl_OnTrainingIteration;
ctrl.Train();
}
catch (Exception excpt)
{
throw excpt;
}
finally
{
ctrl.Dispose();
}
}
//-----------------------------------------------------------------------------------------
// Simplest Classification (using MyCaffeControl and MyCaffeImageDatabase)
//-----------------------------------------------------------------------------------------
/// <summary>
/// The SimplestClassification shows how to the MyCaffeControl (and internal MyCaffeImageDatabase) to train and test on the MNIST dataset.
/// </summary>
/// <param name="sender">Specifies the event sender.</param>
/// <param name="e">Specifies the event args.</param>
private void btnSimplestClassification_Click(object sender, EventArgs e)
{
DatasetFactory factory = new DatasetFactory();
Stopwatch sw = new Stopwatch();
SettingsCaffe settings = new SettingsCaffe();
// Load all images into memory before training.
settings.ImageDbLoadMethod = IMAGEDB_LOAD_METHOD.LOAD_ALL;
// Use GPU ID = 0.
settings.GpuIds = "0";
string strSolver;
string strModel;
// Load the descriptors from their respective files (installed by MyCaffe Test Application install)
load_descriptors("mnist", out strSolver, out strModel);
// NOTE: model fixup not needed for we will use the DATA layer which pulls data from SQL or SQLEXPRESS via the MyCaffeImageDatabase.
// Set the interval beyond the iterations to skip testing during solving.
strSolver = fixup_solver(strSolver, 10000);
// Load the MNIST dataset descriptor.
DatasetDescriptor ds = factory.LoadDataset("MNIST");
// Create a test project with the dataset and descriptors.
ProjectEx project = new ProjectEx("Test");
project.SetDataset(ds);
project.ModelDescription = strModel;
project.SolverDescription = strSolver;
project.WeightsState = null;
// Create the MyCaffeControl
MyCaffeControl <float> mycaffe = new MyCaffeControl <float>(settings, m_log, m_evtCancel);
// Load the project, using the TRAIN phase.
mycaffe.Load(Phase.TRAIN, project);
// Trian the model for 5000 interations (which uses the internal solver and internal training net)
int nIterations = 5000;
mycaffe.Train(nIterations);
// Test the model for 100 iterations (which uses the internal solver and internal testing net)
nIterations = 100;
double dfAccuracy = mycaffe.Test(nIterations);
// Report the testing accuracy.
m_log.WriteLine("Accuracy = " + dfAccuracy.ToString("P"));
MessageBox.Show("Average Accuracy = " + dfAccuracy.ToString("P"), "Traing/Test on MNIST Completed", MessageBoxButtons.OK, MessageBoxIcon.Information);
}
private ProjectEx getProject()
{
ProjectEx p = new ProjectEx("AlexNet Project");
DatasetFactory factory = new DatasetFactory();
DatasetDescriptor ds = factory.LoadDataset("CIFAR-10");
p.SetDataset(ds);
string strModelFile = getTestPath("\\MyCaffe\\test_data\\models\\alexnet\\cifar\\alexnet_cifar_train_val.prototxt");
string strSolverFile = getTestPath("\\MyCaffe\\test_data\\models\\alexnet\\cifar\\alexnet_cifar_solver.prototxt");
p.LoadModelFile(strModelFile);
RawProto proto = RawProtoFile.LoadFromFile(strSolverFile);
RawProto iter = proto.FindChild("max_iter");
iter.Value = m_nMaxIteration.ToString();
p.SolverDescription = proto.ToString();
return(p);
}
static void Main(string[] args)
{
if (!sqlCheck())
{
return;
}
Log log = new Log("test");
log.OnWriteLine += Log_OnWriteLine;
CancelEvent cancel = new CancelEvent();
SettingsCaffe settings = new SettingsCaffe();
// Load all images into memory before training.
settings.ImageDbLoadMethod = IMAGEDB_LOAD_METHOD.LOAD_ALL;
// Use GPU ID = 0
settings.GpuIds = "0";
// Load the descriptors from their respective files
string strSolver = load_file("C:\\ProgramData\\MyCaffe\\test_data\\models\\siamese\\mnist\\solver.prototxt");
string strModel = load_file("C:\\ProgramData\\MyCaffe\\test_data\\models\\siamese\\mnist\\train_val.prototxt");
RawProto proto = RawProto.Parse(strModel);
NetParameter net_param = NetParameter.FromProto(proto);
LayerParameter layer = net_param.FindLayer(LayerParameter.LayerType.DECODE);
layer.decode_param.target = DecodeParameter.TARGET.CENTROID;
proto = net_param.ToProto("root");
strModel = proto.ToString();
// Load the MNIST data descriptor.
DatasetFactory factory = new DatasetFactory();
DatasetDescriptor ds = factory.LoadDataset("MNIST");
// Create a test project with the dataset and descriptors
ProjectEx project = new ProjectEx("Test");
project.SetDataset(ds);
project.ModelDescription = strModel;
project.SolverDescription = strSolver;
// Crate the MyCaffeControl (with the 'float' base type)
string strCudaPath = "C:\\Program Files\\SignalPop\\MyCaffe\\cuda_11.3\\CudaDnnDll.11.3.dll";
MyCaffeControl <float> mycaffe = new MyCaffeControl <float>(settings, log, cancel, null, null, null, null, strCudaPath);
// Load the project, using the TRAIN phase.
mycaffe.Load(Phase.TRAIN, project);
// Train the model for 4000 iterations
// (which uses the internal solver and internal training net)
int nIterations = 4000;
mycaffe.Train(nIterations);
// Test the model for 100 iterations
// (which uses the internal testing net)
nIterations = 100;
double dfAccuracy = mycaffe.Test(nIterations);
// Report the testing accuracy.
log.WriteLine("Accuracy = " + dfAccuracy.ToString("P"));
mycaffe.Dispose();
Console.Write("Press any key...");
Console.ReadKey();
}
