protected override void EndProcessing()
{
foreach (var device in DeviceDescriptor.AllDevices())
{
WriteObject(device);
}
}
private void Awake()
{
contentWindow = new InputImageWindow(1, contentWindowDefaultRect, "Content Image");
contentWindow.showImageMat = showImageMat;
styleWindow = new InputImageWindow(2, styleWindowDefaultRect, "Style Image");
styleWindow.showImageMat = showImageMat;
resultWindow = new OutputImageWindow(3, resultWindowDefaultRect, "Result Image");
resultWindow.showImageMat = showImageMat;
contentSize = new Vector2Int(512, 512);
styleSize = new Vector2Int(512, 512);
//add the parameter sets
styleTransferParams = new List <UniversalStyleTransferModel.ParameterSet>();
for (int i = 0; i < 5; ++i)
{
styleTransferParams.Insert(0, new UniversalStyleTransferModel.ParameterSet((UniversalStyleTransferModel.PassIndex)i));
}
//get available device to run the style transfer on
foreach (var d in DeviceDescriptor.AllDevices())
{
if (d.Type == DeviceKind.CPU && cpuDevice == null)
{
cpuDevice = d;
}
else if (d.Type == DeviceKind.GPU && gpuDevice == null)
{
gpuDevice = d;
}
}
}
static void Main(string[] args)
{
try
{
var devices = DeviceDescriptor.AllDevices().Where(x => (x.Type == DeviceKind.GPU)).ToList();
if (devices.Count == 0)
{
throw new Exception("No GPU Device found. Please run the CPU examples instead!");
}
//Logging.OnWriteLog += Logging_OnWriteLog;
//Setting global device
GlobalParameters.Device = devices[0];
//XOR Example
XORExample.LoadData();
XORExample.BuildModel();
XORExample.Train();
//Housing regression example
HousingRegression.LoadData();
HousingRegression.BuildModel();
HousingRegression.Train();
//MNIST Classification example
MNISTClassifier.LoadData();
MNISTClassifier.BuildModel();
MNISTClassifier.Train();
//In-progress
TimeSeriesPrediction.LoadData();
TimeSeriesPrediction.BuildModel();
TimeSeriesPrediction.Train();
//Cifar-10 Classification example
//Cifar10Classification.LoadData();
//Cifar10Classification.BuildModel();
//Cifar10Classification.Train();
//Image classification example
Console.WriteLine("ResNet50 Prediction: " + ImageClassification.ImagenetTest(Common.ImageNetModel.ResNet50)[0].Name);
Console.WriteLine("Cifar 10 Prediction: " + ImageClassification.Cifar10Test(Common.Cifar10Model.ResNet110)[0].Name);
//Object Detection
//ObjectDetection.PascalDetection();
//ObjectDetection.GroceryDetection();
Console.ReadLine();
}
catch (Exception ex)
{
Console.WriteLine(ex.ToString());
Console.ReadLine();
}
}
private DeviceDescriptor GetDevice(NeuralNetworkSettingsEntity nnSettings)
{
if (!nnSettings.Device.HasText())
{
return(DeviceDescriptor.UseDefaultDevice());
}
var dev = DeviceDescriptor.AllDevices().FirstOrDefault(a => a.AsString() == nnSettings.Device);
if (dev == null)
{
return(DeviceDescriptor.UseDefaultDevice());
}
return(dev);
}
public void SetupUsingResetModel(DeviceDescriptor device)
{
try
{
Console.WriteLine("\n===== Setup memory tests using Resnet Model =====");
var deviceList = DeviceDescriptor.AllDevices();
MemoryTests.Device0 = deviceList[0];
// Load the model.
string modelFilePath = "resnet20.dnn";
CNTKLibraryManagedExamples.ThrowIfFileNotExist(modelFilePath, string.Format("Error: The model '{0}' does not exist. Please follow instructions in README.md in <CNTK>/Examples/Image/Classification/ResNet to create the model.", modelFilePath));
Function modelFunc = Function.LoadModel(modelFilePath, device);
Variable inputVar = modelFunc.Arguments.Single();
MemoryTests.ArgumentVar0 = inputVar;
MemoryTests.InputVar0 = modelFunc.Inputs.First();
MemoryTests.OutputVar0 = modelFunc.Outputs[0];
MemoryTests.OutputVar = modelFunc.Output;
Variable outputVar = MemoryTests.OutputVar;
MemoryTests.Axis0 = outputVar.DynamicAxes.FirstOrDefault();
// Get shape data for the input variable
NDShape inputShape = inputVar.Shape;
int imageWidth = inputShape[0];
int imageHeight = inputShape[1];
int imageChannels = inputShape[2];
int imageSize = inputShape.TotalSize;
var inputDataMap = new Dictionary <Variable, Value>();
var outputDataMap = new Dictionary <Variable, Value>();
var imageList = new List <string>()
{
"00000.png", "00001.png", "00002.png"
};
foreach (var image in imageList)
{
CNTKLibraryManagedExamples.ThrowIfFileNotExist(image, string.Format("Error: The sample image '{0}' does not exist. Please see README.md in <CNTK>/Examples/Image/DataSets/CIFAR-10 about how to download the CIFAR-10 dataset.", image));
}
Bitmap bmp, resized;
List <float> resizedCHW;
var seqData = new List <float>();
for (int sampleIndex = 0; sampleIndex < imageList.Count; sampleIndex++)
{
bmp = new Bitmap(Bitmap.FromFile(imageList[sampleIndex]));
resized = bmp.Resize((int)imageWidth, (int)imageHeight, true);
resizedCHW = resized.ParallelExtractCHW();
seqData.AddRange(resizedCHW);
}
var inputVal = Value.CreateBatch(inputVar.Shape, seqData, device);
inputDataMap.Add(inputVar, inputVal);
outputDataMap.Add(outputVar, null);
modelFunc.Evaluate(inputDataMap, outputDataMap, device);
var outputVal = outputDataMap[outputVar];
var outputData = outputVal.GetDenseData <float>(outputVar);
MemoryTests.OutputVal = outputVal;
Console.WriteLine("\nTest object reference inside SetupUsingResetModel.\n");
MemoryTests.WriteOutputs();
}
catch (Exception ex)
{
Console.WriteLine("Error: {0}\nCallStack: {1}\n Inner Exception: {2}", ex.Message, ex.StackTrace, ex.InnerException != null ? ex.InnerException.Message : "No Inner Exception");
throw ex;
}
}
public void SetupUsingResetModel(DeviceDescriptor device)
{
try
{
Console.WriteLine("\n===== Setup memory tests using Resnet Model =====");
var deviceList = DeviceDescriptor.AllDevices();
MemoryTests.Device0 = deviceList[0];
// Load the model.
string modelFilePath = "resnet20.dnn";
CNTKLibraryManagedExamples.ThrowIfFileNotExist(modelFilePath, string.Format("Error: The model '{0}' does not exist. Please follow instructions in README.md in <CNTK>/Examples/Image/Classification/ResNet to create the model.", modelFilePath));
Function modelFunc = Function.Load(modelFilePath, device);
Variable inputVar = modelFunc.Arguments.Single();
MemoryTests.ArgumentVar0 = inputVar;
MemoryTests.InputVar0 = modelFunc.Inputs.First();
MemoryTests.OutputVar0 = modelFunc.Outputs[0];
MemoryTests.OutputVar = modelFunc.Output;
Variable outputVar = MemoryTests.OutputVar;
MemoryTests.Axis0 = outputVar.DynamicAxes.FirstOrDefault();
// Get shape data for the input variable
NDShape inputShape = inputVar.Shape;
int imageWidth = inputShape[0];
int imageHeight = inputShape[1];
int imageChannels = inputShape[2];
int imageSize = inputShape.TotalSize;
var imageList = new List <string>()
{
"00000.png", "00001.png", "00002.png"
};
foreach (var image in imageList)
{
CNTKLibraryManagedExamples.ThrowIfFileNotExist(image, string.Format("Error: The sample image '{0}' does not exist. Please see README.md in <CNTK>/Examples/Image/DataSets/CIFAR-10 about how to download the CIFAR-10 dataset.", image));
}
Bitmap bmp, resized;
List <float> resizedCHW;
var seqData1 = new List <float>();
var seqData2 = new List <float>();
for (int sampleIndex = 0; sampleIndex < imageList.Count; sampleIndex++)
{
bmp = new Bitmap(Bitmap.FromFile(imageList[sampleIndex]));
resized = bmp.Resize((int)imageWidth, (int)imageHeight, true);
resizedCHW = resized.ParallelExtractCHW();
if (sampleIndex < imageList.Count - 1)
{
seqData1.AddRange(resizedCHW);
}
seqData2.AddRange(resizedCHW);
}
var inputDataMap1 = new Dictionary <Variable, Value>();
var outputDataMap1 = new Dictionary <Variable, Value>();
var inputVal1 = Value.CreateBatch(inputVar.Shape, seqData1, device);
inputDataMap1.Add(inputVar, inputVal1);
outputDataMap1.Add(outputVar, null);
// Using temprary Value object returned by Evaluate().
modelFunc.Evaluate(inputDataMap1, outputDataMap1, device);
var outputVal1 = outputDataMap1[outputVar];
var outputData1 = outputVal1.GetDenseData <float>(outputVar);
// Using cloned persistent Value object returned by Evaluate().
var outputDataMap1WithClone = new Dictionary <Variable, Value>();
outputDataMap1WithClone.Add(outputVar, null);
modelFunc.Evaluate(inputDataMap1, outputDataMap1WithClone, true, device);
// Using temprary Value object which overwrites the one returned by the previous Evaluate().
var inputDataMap2 = new Dictionary <Variable, Value>();
var outputDataMap2 = new Dictionary <Variable, Value>();
var inputVal2 = Value.CreateBatch(inputVar.Shape, seqData2, device);
inputDataMap2.Add(inputVar, inputVal2);
outputDataMap2.Add(outputVar, null);
modelFunc.Evaluate(inputDataMap2, outputDataMap2, device);
// Test access to the persistent Value object, which should be still valid.
var outputVal1WithClone = outputDataMap1WithClone[outputVar];
var outputData1WithClone = outputVal1WithClone.GetDenseData <float>(outputVar);
// Test access to the temprary Value object returned by the latest Evaluate().
var outputVal2 = outputDataMap2[outputVar];
var outputData2 = outputVal2.GetDenseData <float>(outputVar);
// Test access to the temprary Value object returned by the previous Evaluate(), which is not valid any more.
bool exceptionCaught = false;
try
{
var data = outputVal1.GetDenseData <float>(outputVar);
}
catch (Exception ex)
{
if (ex is ApplicationException && ex.Message.StartsWith("This Value object is invalid and can no longer be accessed."))
{
exceptionCaught = true;
}
}
if (exceptionCaught == false)
{
throw new ApplicationException("The expected exception has not been caught.");
}
MemoryTests.OutputVal = outputVal1WithClone;
Console.WriteLine("\nTest object reference inside SetupUsingResetModel.\n");
MemoryTests.WriteOutputs();
}
catch (Exception ex)
{
Console.WriteLine("Error: {0}\nCallStack: {1}\n Inner Exception: {2}", ex.Message, ex.StackTrace, ex.InnerException != null ? ex.InnerException.Message : "No Inner Exception");
throw ex;
}
}
public string[] GetAvailableDevices()
{
InitialSetup();
return(DeviceDescriptor.AllDevices().Select(a => a.AsString()).ToArray());
}