/// <summary>
/// Create Value object from dense input as batch data.
/// </summary>
/// <typeparam name="T">float or double</typeparam>
/// <param name="sampleShape">shape of the Value</param>
/// <param name="batch">batch of data</param>
/// <param name="device">device</param>
/// <param name="readOnly">readonly value</param>
/// <returns>the value</returns>
public static Value CreateBatch <T>(NDShape sampleShape, IEnumerable <T> batch, DeviceDescriptor device, bool readOnly = false)
{
if (typeof(T).Equals(typeof(float)))
{
var inputVector = Helper.AsFloatVector(batch);
return(Value._CreateBatchFloat(sampleShape, inputVector, device, readOnly));
}
else if (typeof(T).Equals(typeof(double)))
{
var inputVector = Helper.AsDoubleVector(batch);
return(Value._CreateBatchDouble(sampleShape, inputVector, device, readOnly));
}
else
{
throw new ArgumentException("The data type " + typeof(T).ToString() + " is not supported. Only float or double is supported by CNTK.");
}
}
/// <summary>
/// Loads a model from memory buffer.
/// </summary>
/// <param name="modelBuffer"></param>
/// <param name="computeDevice"></param>
/// <returns></returns>
public static Function Load(byte[] modelBuffer, DeviceDescriptor computeDevice)
{
return(_Load(modelBuffer, (uint)modelBuffer.Length, computeDevice));
}
/// <summary>
/// Create Value object from dense input as batch data.
/// </summary>
/// <typeparam name="T">float or double</typeparam>
/// <param name="sampleShape">shape of the Value</param>
/// <param name="batch">batch of data</param>
/// <param name="device">device</param>
/// <param name="readOnly">readonly value</param>
/// <returns>the value</returns>
public static Value CreateBatch <T>(NDShape sampleShape, IEnumerable <T> batch, DeviceDescriptor device, bool readOnly = false)
{
T[] batchAsArray = batch.ToArray();
return(CreateBatch <T>(sampleShape, batchAsArray, 0, batchAsArray.Count(), device, readOnly));
}
/// <summary>
/// Evaluates the Function using provided inputs.
/// </summary>
/// <param name="inputs"></param>
/// <param name="outputs"></param>
/// <param name="createPersistentOutputValues"></param>
/// <param name="computeDevice"></param>
public void Evaluate(IDictionary <Variable, Value> inputs, IDictionary <Variable, Value> outputs, bool createPersistentOutputValues, DeviceDescriptor computeDevice)
{
// Evaluate the rootFunction.
var inMap = new UnorderedMapVariableValuePtr();
var outMap = new UnorderedMapVariableValuePtr();
foreach (var p in inputs)
{
inMap.Add(p.Key, p.Value);
}
foreach (var p in outputs)
{
outMap.Add(p.Key, p.Value);
}
_Evaluate(inMap, outMap, computeDevice);
foreach (var p in outMap)
{
if (createPersistentOutputValues && (outputs[p.Key] == null))
{
outputs[p.Key] = p.Value.DeepClone();
}
else
{
// for shared_ptr<Value>, the p.Value returns a copy, so it is safe to use it directly in outputs.
outputs[p.Key] = p.Value;
}
}
}
/// <summary>
/// Loads a model from file.
/// </summary>
/// <param name="filepath"></param>
/// <param name="computeDevice"></param>
/// <returns></returns>
public static Function Load(string filepath, DeviceDescriptor computeDevice)
{
return(_Load(filepath, computeDevice));
}
/// <summary>
/// Loads a model from memory buffer.
/// </summary>
/// <param name="modelBuffer"></param>
/// <param name="computeDevice"></param>
/// <returns></returns>
public static Function Load(byte[] modelBuffer, DeviceDescriptor computeDevice, ModelFormat format = ModelFormat.CNTKv2)
{
return(_Load(modelBuffer, (uint)modelBuffer.Length, computeDevice, format));
}
/// <summary>
/// Evaluates the Function using provided inputs.
/// </summary>
/// <param name="inputs"></param>
/// <param name="outputs"></param>
/// <param name="computeDevice"></param>
public void Evaluate(IDictionary <Variable, Value> inputs, IDictionary <Variable, Value> outputs, DeviceDescriptor computeDevice)
{
Evaluate(inputs, outputs, false, computeDevice);
}
public UniversalStyleTransferModel(DeviceDescriptor device, byte[] modelWithWeights)
{
this.device = device;
modelWeights = modelWithWeights;
}
/// <summary>
/// Loads a model from file.
/// </summary>
/// <param name="filepath"></param>
/// <param name="computeDevice"></param>
/// <returns></returns>
public static Function Load(string filepath, DeviceDescriptor computeDevice, ModelFormat format = ModelFormat.CNTKv2)
{
return(_Load(filepath, computeDevice, format));
}
/// <summary>
/// ニューラルネットワークモデルの入力設定
/// </summary>
/// <param name="width">入力画像の幅</param>
/// <param name="height">入力画像の高さ</param>
/// <param name="channel">入力画像のチャンネル</param>
/// <param name="dataType">データタイプ</param>
/// <param name="device">CPU/GPUの使用設定</param>
/// <returns></returns>
public static Function Input(int width, int height, int channel, CNTK.DataType dataType = DataType.Float, CNTK.DeviceDescriptor device = null, string name = "")
{
if (device == null)
{
// CPU or GPUの設定（GPU優先）
GetDevice();
}
else
{
_device = device;
}
_dataType = dataType;
return(CognitiveCSharpKit.Data.Variable(width, height, channel, dataType, name));
}
/// <summary>
/// create a constant
/// </summary>
/// <param name="shape">shape of the constant</param>
/// <param name="initValue">initial value</param>
/// <param name="device">device</param>
/// <param name="name">name</param>
public Constant(NDShape shape, float initValue, DeviceDescriptor device, string name = "") :
this(shape, DataType.Float, initValue, device, name)
{
}
// Todo: set default parameters = DeviceDescriptor.UseDefaultDevice()
// Todo: how to define DeviceDescriptor?? Better just enum or something easier
// Todo: use Variable as method, string as extension method.
public void Evaluate(Dictionary <string, Value> arguments, Dictionary <string, Value> outputs, DeviceDescriptor computeDevice)
{
if (rootFunction == null)
{
throw new NullReferenceException("No rootFunction is loaded. Please load the rootFunction first before evaluation.");
}
// Evaluate the rootFunction.
var argMap = new UnorderedMapVariableValuePtr();
foreach (var p in arguments)
{
var variable = rootFunction.Arguments.Where(v => string.Equals(v.Name, p.Key)).FirstOrDefault();
if (variable == null)
{
throw new KeyNotFoundException("No input variable '" + p.Key + "' found.");
}
argMap.Add(variable, p.Value);
}
var outMap = new UnorderedMapVariableValuePtr();
foreach (var p in outputs)
{
var variable = rootFunction.Outputs.Where(v => string.Equals(v.Name, p.Key)).FirstOrDefault();
if (variable == null)
{
throw new KeyNotFoundException("No output variable '" + p.Key + "' found.");
}
outMap.Add(variable, p.Value);
}
rootFunction.Evaluate(argMap, outMap, computeDevice);
foreach (var p in outMap)
{
outputs[p.Key.Name] = p.Value;
}
}
// Create Value based on onehot input
// Todo: could this be a extension to Value class??
// Todo: use Variable instead of varName. VarName as extension method
public Value CreateValue <T>(string varName, List <List <long> > oneHotIndex, DeviceDescriptor computeDevice)
{
throw new NotImplementedException("Not implemented");
}
// Create Value based on sparse input
// Todo: could this be a extension to Value class??
// Todo: use Variable instead of varName. VarName as extension method
public Value CreateValue <T>(string varName, List <List <T> > data, List <List <long> > indexes, List <List <long> > nnzCounts, DeviceDescriptor computeDevice)
{
throw new NotImplementedException("Not implemented");
}
