// The value represents a n-dimensional tensor with 2 dynamic axes: sequence and batch
// It assumes that only the highest 2 axes are dynamic, and all the other axes are static.
public static void CopyTo <T>(this Value value, Variable variable, List <List <T> > data)
{
if ((value.GetDataType() == DataType.Float) && (!typeof(T).Equals(typeof(float))) ||
(value.GetDataType() == DataType.Double) && (!typeof(T).Equals(typeof(double))))
{
throw new ArgumentException("The value type does not match the list type.");
}
// Todo: how to check whether the dynamic axes are the highest 2 axes in the shape.
if (variable.DynamicAxes().Count != 2)
{
throw new ArgumentException("The variable should have 2 dynamic axes.");
}
var variableShape = variable.Shape;
var valueShape = value.Shape;
if (variableShape != value.Shape.SubShape(0, valueShape.Rank - 2))
{
throw new ArgumentException("The variable and value does not have same shape.");
}
// Todo: transform sparse to dense
// Currently only for dense
if ((value.GetStorageFormat() != StorageFormat.Dense))
{
throw new ArgumentException("The value is not in denst format.");
}
var outputNDArrayView = value.Data();
var outputShape = outputNDArrayView.Shape();
var outputShapeRank = outputShape.Rank;
var numOfElementsInSample = variableShape.TotalSize;
var numOfSamplesInSequence = outputShape.GetDimensionSize(outputShapeRank - 2);
var numOfSequences = outputShape.GetDimensionSize(outputShapeRank - 1);
// Copy the data from the output buffer.
// Todo: directly access the data in output buffer?
// Todo: need to map DataBuffer() to C#
NDArrayView cpuOutputNDArrayView;
uint numOfOutputData = outputNDArrayView.Shape().TotalSize;
// Todo: consider mask.
Debug.Assert(numOfElementsInSample * numOfSamplesInSequence * numOfSequences == numOfOutputData);
T[] outputData = new T[numOfOutputData];
if (value.GetDataType() == DataType.Float)
{
cpuOutputNDArrayView = new NDArrayView(outputNDArrayView.Shape(), outputData as float[], numOfOutputData, DeviceDescriptor.CPUDevice);
}
else if (value.GetDataType() == DataType.Double)
{
cpuOutputNDArrayView = new NDArrayView(outputNDArrayView.Shape(), outputData as double[], numOfOutputData, DeviceDescriptor.CPUDevice);
}
else
{
throw new ArgumentException("The data type " + value.GetDataType().ToString() + " is not supported. Only float or double is supported by CNTK.");
}
cpuOutputNDArrayView.CopyFrom(outputNDArrayView);
for (int seqIndex = 0, dataIndex = 0; seqIndex < numOfSequences; seqIndex++)
{
var seqData = new List <T>();
// Todo: consider mask
// Todo: make it more efficient.
for (int i = 0; i < numOfElementsInSample * numOfSamplesInSequence; i++)
{
seqData.Add(outputData[dataIndex++]);
}
data.Add(seqData);
}
}
// Copy Value to List<List<T>> for dense input
// Todo: could this be a extension to Value class??
public void CopyValueTo <T>(string varName, Value value, List <List <T> > sequences)
{
// Todo: deal with GPUDevice.
if (value.Device != DeviceDescriptor.CPUDevice)
{
throw new InvalidOperationException("Currently only CPU device is supported.");
}
if ((value.GetDataType() == DataType.Float) && (!typeof(T).Equals(typeof(float))) ||
(value.GetDataType() == DataType.Double) && (!typeof(T).Equals(typeof(double))))
{
throw new InvalidDataException("The value type does not match the list type.");
}
// Todo: transform sparse to dense
// Currently only for dense
if ((value.GetStorageFormat() != StorageFormat.Dense))
{
throw new InvalidDataException("The value is not in denst format.");
}
var variable = getVariableByName(varName);
var outputNDArrayView = value.Data();
var outputShape = outputNDArrayView.Shape();
var varRank = variable.Shape.Rank;
var valueRank = outputNDArrayView.Shape().Rank;
Debug.Assert(varRank + 2 == valueRank);
var numOfElementsInSample = variable.Shape.TotalSize;
var numOfSamplesInSequence = outputShape.GetDimensionSize(varRank);
var numOfSequences = outputShape.GetDimensionSize(varRank + 1);
//var outputShape = outputVar.Shape().AppendShape(new NDShape(dynamicAxisShape));
// Copy the data from the output buffer.
// Todo: directly access the data in output buffer?
// Todo: need to map DataBuffer() to C#
NDArrayView cpuOutputNDArrayView;
uint numOfOutputData = outputNDArrayView.Shape().TotalSize;
Debug.Assert(numOfElementsInSample * numOfSamplesInSequence * numOfSequences == numOfOutputData);
T[] outputData = new T[numOfOutputData];
if (value.GetDataType() == DataType.Float)
{
cpuOutputNDArrayView = new NDArrayView(outputNDArrayView.Shape(), outputData as float[], numOfOutputData, DeviceDescriptor.CPUDevice);
}
else if (value.GetDataType() == DataType.Double)
{
cpuOutputNDArrayView = new NDArrayView(outputNDArrayView.Shape(), outputData as double[], numOfOutputData, DeviceDescriptor.CPUDevice);
}
else
{
throw new InvalidDataException("The data type " + value.GetDataType().ToString() + " is not supported. Only float or double is supported by CNTK.");
}
cpuOutputNDArrayView.CopyFrom(outputNDArrayView);
for (int seqIndex = 0, dataIndex = 0; seqIndex < numOfSequences; seqIndex++)
{
var seqData = new List <T>();
// Todo: make it more efficient.
for (int i = 0; i < numOfElementsInSample * numOfSamplesInSequence; i++)
{
seqData.Add(outputData[dataIndex++]);
}
sequences.Add(seqData);
}
}