// Todo: move it to separate unit tests.
public void NDArrayViewTest(DeviceDescriptor device)
{
Console.WriteLine("Test creating NDArrayView on devices.");
var data = new float[10];
var shape = new NDShape(1, 10);
var n1 = new NDArrayView(shape, data, device);
var n1Clone = n1.DeepClone(device);
var n1CloneCPU = n1.DeepClone(DeviceDescriptor.CPUDevice);
Console.WriteLine("n1: on " + n1.Device.AsString() + ", Storage:" + n1.StorageFormat + ", Shape:" + n1.Shape.AsString());
Console.WriteLine("n1Clone: on " + n1Clone.Device.AsString() + ", Storage:" + n1Clone.StorageFormat + ", Shape:" + n1Clone.Shape.AsString());
Console.WriteLine("n1CloneCPU: on " + n1CloneCPU.Device.AsString() + ", Storage:" + n1CloneCPU.StorageFormat + ", Shape:" + n1CloneCPU.Shape.AsString());
int[] dimensions = { 4, 5 };
var shape2 = NDShape.CreateNDShape(dimensions);
float[] nonZeroValues = { 1, 5, 4, 2, 3, 9, 7, 8, 6 };
int[] rowIndices = { 0, 2, 0, 1, 1, 3, 2, 2, 3 };
int[] colStarts = { 0, 2, 4, 6, 7, 9 };
var s1 = new NDArrayView(shape2, colStarts, rowIndices, nonZeroValues, device, true);
var s1Clone = s1.DeepClone(device);
var s1DenseCPU = new NDArrayView(DataType.Float, StorageFormat.Dense, shape2, DeviceDescriptor.CPUDevice);
s1DenseCPU.CopyFrom(s1);
Console.WriteLine("s1: on " + s1.Device.AsString() + ", Storage:" + s1.StorageFormat + ", Shape:" + s1.Shape.AsString());
Console.WriteLine("s1Clone: on " + s1Clone.Device.AsString() + ", Storage:" + s1Clone.StorageFormat + ", Shape:" + s1Clone.Shape.AsString());
Console.WriteLine("s1DenseCPU: on " + s1DenseCPU.Device.AsString() + ", Storage:" + s1DenseCPU.StorageFormat + ", Shape:" + s1DenseCPU.Shape.AsString());
}
//add zeros to the input so that input has a larger width
public static Function AddDummy(Variable input, int numToAdd, DeviceDescriptor device)
{
int inputSize = input.Shape[0];
//create the dummy data
float[] addValues = new float[numToAdd];
for (int i = 0; i < numToAdd; i++)
{
addValues[i] = 0;
}
var addArray = new NDArrayView(DataType.Float, new int[] { numToAdd }, device);
addArray.CopyFrom(new NDArrayView(new int[] { numToAdd }, addValues, (uint)addValues.Length, device));
var dummyData = new Constant(addArray);
//append the dummy data to the input
var vars = new VariableVector();
vars.Add(input);
vars.Add(dummyData);
var added = CNTKLib.Splice(vars, new Axis(0));
return(added);
}
private static Constant GetProjectionMap(int outputDim, int inputDim, DeviceDescriptor device)
{
if (inputDim > outputDim)
throw new Exception("Can only project from lower to higher dimensionality");
float[] projectionMapValues = new float[inputDim * outputDim];
for (int i = 0; i < inputDim * outputDim; i++)
projectionMapValues[i] = 0;
for (int i = 0; i < inputDim; ++i)
projectionMapValues[(i * (int)inputDim) + i] = 1.0f;
var projectionMap = new NDArrayView(DataType.Float, new int[] { 1, 1, inputDim, outputDim }, device);
projectionMap.CopyFrom(new NDArrayView(new int[] { 1, 1, inputDim, outputDim }, projectionMapValues, (uint)projectionMapValues.Count(), device));
return new Constant(projectionMap);
}
public static void EvaluateUsingCreateValue()
{
// Load the model
var myFunc = Function.LoadModel("01_OneHidden");
// Ouput funciton info.
OutputFunctionInfo(myFunc);
// prepare input for evaluation
uint numOfSamples = 1;
const string inputNodeName = "features";
var inputVar = myFunc.Arguments.Where(variable => string.Equals(variable.Name, inputNodeName)).Single();
// Todo: get size directly from inputVar.
uint numOfInputData = inputVar.Shape.TotalSize;
var inputData = new List <float>();
for (uint i = 0; i < numOfInputData; ++i)
{
inputData.Add(i % 255);
}
var inputVector = new FloatVector(inputData);
var data = new FloatVectorVector()
{
inputVector
};
// Create value directly from data.
var inputValue = Value.CreateDenseFloat(inputVar.Shape, data, DeviceDescriptor.CPUDevice);
// Create input map
// Todo: create a Dictionary wrapper?
var inputMap = new UnorderedMapVariableValuePtr();
inputMap.Add(inputVar, inputValue);
// Prepare output
const string outputNodeName = "out.z_output";
var outputVar = myFunc.Outputs.Where(variable => string.Equals(variable.Name, outputNodeName)).Single();
// Create ouput map. Using null as Value to indicate using system allocated memory.
var outputMap = new UnorderedMapVariableValuePtr();
outputMap.Add(outputVar, null);
// Evalaute
// Todo: test on GPUDevice()?
myFunc.Evaluate(inputMap, outputMap, DeviceDescriptor.CPUDevice);
// Get output value after evaluation
var outputValue = outputMap[outputVar];
var outputNDArrayView = outputValue.Data();
var dynamicAxisShape = new SizeTVector()
{
1, numOfSamples
};
var outputShape = outputVar.Shape.AppendShape(new NDShape(dynamicAxisShape));
// Copy the data from the output buffer.
// Todo: directly access the data in output buffer if it is on CPU?
uint numOfOutputData = outputNDArrayView.Shape().TotalSize;
float[] outputData = new float[numOfOutputData];
var cpuOutputNDArrayView = new NDArrayView(outputShape, outputData, numOfOutputData, DeviceDescriptor.CPUDevice);
cpuOutputNDArrayView.CopyFrom(outputNDArrayView);
// Output results
Console.WriteLine("Evaluation results:");
for (uint i = 0; i < numOfOutputData; ++i)
{
Console.WriteLine(outputData[i]);
}
}
public static void EvaluateUsingSystemAllocatedMemory()
{
// Load the model
var myFunc = Function.LoadModel("z.model");
// Ouput funciton info.
OutputFunctionInfo(myFunc);
// prepare input for evaluation
uint numOfSamples = 1;
// Only one input for the model.
var inputVar = myFunc.Arguments.First();
// Todo: get size directly from inputVar.
uint numOfInputData = inputVar.Shape.TotalSize * numOfSamples;
float[] inputData = new float[numOfInputData];
for (uint i = 0; i < numOfInputData; ++i)
{
inputData[i] = (float)(i % 255);
}
// Todo: create value directly from data.
var dynamicAxisShape = new SizeTVector()
{
1, numOfSamples
};
var inputShape = inputVar.Shape.AppendShape(new NDShape(dynamicAxisShape));
var inputNDArrayView = new NDArrayView(inputShape, inputData, numOfInputData, DeviceDescriptor.CPUDevice);
var inputValue = new Value(inputNDArrayView);
// Create input map
// Todo: create a Dictionary wrapper?
var inputMap = new UnorderedMapVariableValuePtr();
inputMap.Add(inputVar, inputValue);
// Prepare output. The model has only one output.
var outputVar = myFunc.Output;
// Create ouput map. Using null as Value to indicate using system allocated memory.
var outputMap = new UnorderedMapVariableValuePtr();
outputMap.Add(outputVar, null);
// Evalaute
// Todo: test on GPUDevice()?
myFunc.Evaluate(inputMap, outputMap, DeviceDescriptor.CPUDevice);
// Get output value after evaluation
var outputValue = outputMap[outputVar];
var outputNDArrayView = outputValue.Data();
var outputShape = outputVar.Shape.AppendShape(new NDShape(dynamicAxisShape));
// Copy the data from the output buffer.
// Todo: directly access the data in output buffer if it is on CPU?
uint numOfOutputData = outputNDArrayView.Shape().TotalSize;
float[] outputData = new float[numOfOutputData];
var cpuOutputNDArrayView = new NDArrayView(outputShape, outputData, numOfOutputData, DeviceDescriptor.CPUDevice);
cpuOutputNDArrayView.CopyFrom(outputNDArrayView);
// Output results
Console.WriteLine("Evaluation results:");
for (uint i = 0; i < numOfOutputData; ++i)
{
Console.WriteLine(outputData[i]);
}
}