/// <summary>
/// Sets a tensor data to given index.
/// </summary>
/// <param name="index">The index of the tensor.</param>
/// <param name="buffer">Raw tensor data to be set.</param>
/// <feature>http://tizen.org/feature/machine_learning.inference</feature>
/// <exception cref="ArgumentException">Thrown when the method failed due to an invalid parameter.</exception>
/// <exception cref="NotSupportedException">Thrown when the feature is not supported.</exception>
/// <since_tizen> 6 </since_tizen>
public void SetTensorData(int index, byte[] buffer)
{
NNStreamerError ret = NNStreamerError.None;
ret = Interop.Util.SetTensorData(_handle, index, buffer, buffer.Length);
NNStreamer.CheckException(ret, "unable to set the buffer of TensorsData: " + index.ToString());
}
/// <summary>
/// Creates a TensorsInfo instance.
/// </summary>
/// <feature>http://tizen.org/feature/machine_learning.inference</feature>
/// <exception cref="NotSupportedException">Thrown when the feature is not supported.</exception>
/// <since_tizen> 6 </since_tizen>
public TensorsInfo()
{
NNStreamer.CheckNNStreamerSupport();
Log.Info(NNStreamer.TAG, "TensorsInfo is created");
_infoList = new List <TensorInfo>();
}
private void CreateSingleShot(string modelAbsPath,
TensorsInfo inTensorInfo, TensorsInfo outTensorInfo,
NNFWType FWType, HWType HWType, bool IsDynamicMode)
{
NNStreamerError ret = NNStreamerError.None;
IntPtr input_info = IntPtr.Zero;
IntPtr output_info = IntPtr.Zero;
/* Check model path */
if (string.IsNullOrEmpty(modelAbsPath))
{
ret = NNStreamerError.InvalidParameter;
}
NNStreamer.CheckException(ret, "model path is invalid: " + modelAbsPath);
/* Set Dynamic Mode */
_dynamicMode = IsDynamicMode;
if (inTensorInfo != null)
{
input_info = inTensorInfo.GetTensorsInfoHandle();
_inInfo = inTensorInfo;
}
if (outTensorInfo != null)
{
output_info = outTensorInfo.GetTensorsInfoHandle();
_outInfo = outTensorInfo;
}
ret = Interop.SingleShot.OpenSingle(out _handle, modelAbsPath, input_info, output_info, FWType, HWType);
NNStreamer.CheckException(ret, "fail to open the single inference engine");
}
/// <summary>
/// Calculates the byte size of tensor data.
/// </summary>
/// <param name="idx">The index of the tensor information in the list</param>
/// <returns>The byte size of tensor</returns>
/// <feature>http://tizen.org/feature/machine_learning.inference</feature>
/// <exception cref="ArgumentException">Thrown when the method failed due to an invalid parameter.</exception>
/// <exception cref="NotSupportedException">Thrown when the feature is not supported.</exception>
/// <since_tizen> 8 </since_tizen>
public int GetTensorSize(int idx)
{
NNStreamer.CheckNNStreamerSupport();
CheckIndexBoundary(idx);
return(_infoList[idx].Size);
}
/// <summary> Sets the property value for the given model.
/// <para>A model/framework may support changing the model information, such as tensor dimension and data layout, after opening the model.</para>
/// <para>If tries to change unavailable property or the model does not allow changing the information, this will raise an exception.</para>
/// <para>For the details about the properties, see 'tensor_filter' plugin definition in <a href="https://github.com/nnstreamer/nnstreamer">NNStreamer</a>.</para>
/// </summary>
/// <param name="name">The property name</param>
/// <param name="value">The property value</param>
/// <feature>http://tizen.org/feature/machine_learning.inference</feature>
/// <exception cref="NotSupportedException">Thrown when the feature is not supported, or given property is not available.</exception>
/// <exception cref="ArgumentException">Thrown when the method failed due to an invalid parameter.</exception>
/// <since_tizen> 8 </since_tizen>
public void SetValue(string name, string value)
{
NNStreamerError ret = NNStreamerError.None;
NNStreamer.CheckNNStreamerSupport();
/* Check the argument */
if (string.IsNullOrEmpty(name))
{
throw NNStreamerExceptionFactory.CreateException(NNStreamerError.InvalidParameter, "The property name is invalid");
}
if (string.IsNullOrEmpty(value))
{
throw NNStreamerExceptionFactory.CreateException(NNStreamerError.InvalidParameter, "The property value is invalid");
}
ret = Interop.SingleShot.SetValue(_handle, name, value);
if (ret != NNStreamerError.None)
{
if (ret == NNStreamerError.NotSupported)
{
NNStreamer.CheckException(ret, "Failed to to set the property, the property name is not available.");
}
else
{
NNStreamer.CheckException(ret, "Failed to to set the property, the property value is invalid.");
}
}
}
/// <summary>
/// Creates new custom-filter with input and output tensors information.
/// </summary>
/// <param name="name">The name of custom-filter</param>
/// <param name="inInfo">The input tensors information</param>
/// <param name="outInfo">The output tensors information</param>
/// <param name="filter">Delegate to be called while processing the pipeline</param>
/// <returns>CustomFiter instance</returns>
/// <exception cref="NotSupportedException">Thrown when the feature is not supported.</exception>
/// <exception cref="ArgumentException">Thrown when the method failed due to an invalid parameter.</exception>
/// <since_tizen> 8 </since_tizen>
public static CustomFilter Create(string name,
TensorsInfo inInfo, TensorsInfo outInfo, Func <TensorsData, TensorsData> filter)
{
NNStreamer.CheckNNStreamerSupport();
return(new CustomFilter(name, inInfo, outInfo, filter));
}
/// <summary>
/// Add a Tensor information to the TensorsInfo instance. Note that we support up to 16 tensors in TensorsInfo.
/// </summary>
/// <param name="name">Name of Tensor.</param>
/// <param name="type">Data element type of Tensor.</param>
/// <param name="dimension">Dimension of Tensor. Note that we support up to 4th ranks.</param>
/// <feature>http://tizen.org/feature/machine_learning.inference</feature>
/// <exception cref="IndexOutOfRangeException">Thrown when the number of Tensor already exceeds the size limits (i.e. Tensor.SlzeLimit)</exception>
/// <exception cref="ArgumentException">Thrown when the method failed due to an invalid parameter.</exception>
/// <exception cref="NotSupportedException">Thrown when the feature is not supported.</exception>
/// <since_tizen> 6 </since_tizen>
public void AddTensorInfo(string name, TensorType type, int[] dimension)
{
NNStreamer.CheckNNStreamerSupport();
int idx = _infoList.Count;
if (idx >= Tensor.SizeLimit)
{
throw new IndexOutOfRangeException("Max size of the tensors is " + Tensor.SizeLimit);
}
_infoList.Add(new TensorInfo(name, type, dimension));
if (_handle != IntPtr.Zero)
{
NNStreamerError ret = NNStreamerError.None;
ret = Interop.Util.SetTensorsCount(_handle, _infoList.Count);
NNStreamer.CheckException(ret, "unable to set the number of tensors");
ret = Interop.Util.SetTensorType(_handle, idx, type);
NNStreamer.CheckException(ret, "fail to set TensorsInfo type");
ret = Interop.Util.SetTensorDimension(_handle, idx, dimension);
NNStreamer.CheckException(ret, "fail to set TensorsInfo dimension");
}
}
/// <summary>
/// Gets the tensor dimension with given index.
/// </summary>
/// <param name="idx">The index of the tensor.</param>
/// <returns>The tensor dimension.</returns>
/// <feature>http://tizen.org/feature/machine_learning.inference</feature>
/// <exception cref="ArgumentException">Thrown when the method failed due to an invalid parameter.</exception>
/// <exception cref="NotSupportedException">Thrown when the feature is not supported.</exception>
/// <since_tizen> 6 </since_tizen>
public int[] GetDimension(int idx)
{
NNStreamer.CheckNNStreamerSupport();
CheckIndexBoundary(idx);
return(_infoList[idx].Dimension);
}
/// <summary>
/// Gets the tensor name with given index.
/// </summary>
/// <param name="idx">The index of the tensor.</param>
/// <returns>The tensor name</returns>
/// <feature>http://tizen.org/feature/machine_learning.inference</feature>
/// <exception cref="ArgumentException">Thrown when the method failed due to an invalid parameter.</exception>
/// <exception cref="NotSupportedException">Thrown when the feature is not supported.</exception>
/// <since_tizen> 6 </since_tizen>
public string GetTensorName(int idx)
{
NNStreamer.CheckNNStreamerSupport();
CheckIndexBoundary(idx);
return(_infoList[idx].Name);
}
/// <summary>
/// Gets the tensor type with given index.
/// </summary>
/// <param name="idx">The index of the tensor.</param>
/// <returns>The tensor type</returns>
/// <feature>http://tizen.org/feature/machine_learning.inference</feature>
/// <exception cref="ArgumentException">Thrown when the method failed due to an invalid parameter.</exception>
/// <exception cref="NotSupportedException">Thrown when the feature is not supported.</exception>
/// <since_tizen> 6 </since_tizen>
public TensorType GetTensorType(int idx)
{
NNStreamer.CheckNNStreamerSupport();
CheckIndexBoundary(idx);
return(_infoList[idx].Type);
}
/// <summary>
/// Gets the property value for the given model.
/// </summary>
/// <param name="name">The property name</param>
/// <returns>The property value</returns>
/// <feature>http://tizen.org/feature/machine_learning.inference</feature>
/// <exception cref="NotSupportedException">Thrown when the feature is not supported, or given property is not available.</exception>
/// <exception cref="ArgumentException">Thrown when the method failed due to an invalid parameter.</exception>
/// <since_tizen> 8 </since_tizen>
public string GetValue(string name)
{
NNStreamerError ret = NNStreamerError.None;
IntPtr val = IntPtr.Zero;
NNStreamer.CheckNNStreamerSupport();
/* Check the argument */
if (string.IsNullOrEmpty(name))
{
throw NNStreamerExceptionFactory.CreateException(NNStreamerError.InvalidParameter, "The property name is invalid");
}
ret = Interop.SingleShot.GetValue(_handle, name, out val);
if (ret != NNStreamerError.None)
{
if (ret == NNStreamerError.NotSupported)
{
NNStreamer.CheckException(ret, "Failed to to get the property, the property name is not available.");
}
else
{
NNStreamer.CheckException(ret, "Failed to to get the property, the property value is invalid.");
}
}
return(Interop.Util.IntPtrToString(val));
}
/// <summary>
/// Make TensorsInfo object from Native handle
/// </summary>
/// <param name="handle">Handle of TensorsInfo object</param>
/// <returns>TensorsInfo object</returns>
internal static TensorsInfo ConvertTensorsInfoFromHandle(IntPtr handle)
{
TensorsInfo retInfo = null;
NNStreamerError ret = NNStreamerError.None;
int count;
ret = Interop.Util.GetTensorsCount(handle, out count);
NNStreamer.CheckException(ret, "Fail to get Tensors' count");
retInfo = new TensorsInfo();
for (int i = 0; i < count; ++i)
{
string name;
TensorType type;
uint[] dim = new uint[Tensor.RankLimit];
ret = Interop.Util.GetTensorName(handle, i, out name);
NNStreamer.CheckException(ret, "Fail to get Tensor's name");
ret = Interop.Util.GetTensorType(handle, i, out type);
NNStreamer.CheckException(ret, "Fail to get Tensor's type");
ret = Interop.Util.GetTensorDimension(handle, i, dim);
NNStreamer.CheckException(ret, "Fail to get Tensor's dimension");
retInfo.AddTensorInfo(name, type, (int[])(object)dim);
}
return(retInfo);
}
/// <summary>
/// Gets the normal node instance with given node name.
/// </summary>
/// <param name="name">The name of normal node.</param>
/// <returns>The normal node instance</returns>
/// <feature>http://tizen.org/feature/machine_learning.inference</feature>
/// <exception cref="NotSupportedException">Thrown when the feature is not supported.</exception>
/// <exception cref="ArgumentException">Thrown when the method failed due to an invalid parameter.</exception>
/// <since_tizen> 8 </since_tizen>
public Node GetNormal(string name)
{
NNStreamer.CheckNNStreamerSupport();
/* Check the parameter */
if (string.IsNullOrEmpty(name))
{
throw NNStreamerExceptionFactory.CreateException(NNStreamerError.InvalidParameter, "Node name is invalid");
}
Node node;
if (_nodeList.ContainsKey(name))
{
if (_nodeList[name].Type != NodeType.Normal)
{
throw NNStreamerExceptionFactory.CreateException(NNStreamerError.InvalidParameter, name + " is not a normal node");
}
node = (Node)_nodeList[name];
}
else
{
node = new Node(name, this);
_nodeList.Add(name, node);
}
return(node);
}
/// <summary>
/// Gets the sink node instance with given node name.
/// </summary>
/// <param name="name">The name of sink node</param>
/// <returns>The sink node instance</returns>
/// <feature>http://tizen.org/feature/machine_learning.inference</feature>
/// <exception cref="NotSupportedException">Thrown when the feature is not supported.</exception>
/// <exception cref="ArgumentException">Thrown when the method failed due to an invalid parameter.</exception>
/// <since_tizen> 8 </since_tizen>
public SinkNode GetSink(string name)
{
NNStreamer.CheckNNStreamerSupport();
/* Check the argument */
if (string.IsNullOrEmpty(name))
{
throw NNStreamerExceptionFactory.CreateException(NNStreamerError.InvalidParameter, "Node name is invalid");
}
SinkNode node;
if (_nodeList.ContainsKey(name))
{
if (_nodeList[name].Type != NodeType.Sink)
{
throw NNStreamerExceptionFactory.CreateException(NNStreamerError.InvalidParameter, name + " is not a sink node");
}
node = (SinkNode)_nodeList[name];
}
else
{
node = new SinkNode(name, this);
_nodeList.Add(name, node);
}
return(node);
}
/// <summary>
/// Sets the string of node's property in NNStreamer pipelines.
/// </summary>
/// <param name="propertyName">The property name.</param>
/// <param name="value">The string of given property.</param>
/// <feature>http://tizen.org/feature/machine_learning.inference</feature>
/// <exception cref="NotSupportedException">Thrown when the feature is not supported.</exception>
/// <exception cref="ArgumentException">Thrown when the method failed due to an invalid parameter.</exception>
/// <since_tizen> 8 </since_tizen>
public void SetValue(string propertyName, string value)
{
CheckSetParam(propertyName, value);
NNStreamerError ret = Interop.Pipeline.SetPropertyString(Handle, propertyName, value);
NNStreamer.CheckException(ret, string.Format("Failed to set {0} property.", propertyName));
}
/// <summary>
/// Gets a tensor data to given index.
/// </summary>
/// <param name="index">The index of the tensor.</param>
/// <returns>Raw tensor data</returns>
/// <feature>http://tizen.org/feature/machine_learning.inference</feature>
/// <exception cref="ArgumentException">Thrown when the method failed due to an invalid parameter.</exception>
/// <exception cref="NotSupportedException">Thrown when the feature is not supported.</exception>
/// <since_tizen> 6 </since_tizen>
public byte[] GetTensorData(int index)
{
NNStreamer.CheckNNStreamerSupport();
CheckIndex(index);
return((byte[])_dataList[index]);
}
/// <summary>
/// Stops the pipeline, asynchronously. (The state would be changed to PipelineState.Paused)
/// </summary>
/// <feature>http://tizen.org/feature/machine_learning.inference</feature>
/// <exception cref="NotSupportedException">Thrown when the feature is not supported.</exception>
/// <exception cref="InvalidOperationException">Thrown when failed to stop the pipeline.</exception>
/// <since_tizen> 8 </since_tizen>
public void Stop()
{
NNStreamer.CheckNNStreamerSupport();
NNStreamerError ret = Interop.Pipeline.Stop(_handle);
NNStreamer.CheckException(ret, "Failed to stop the pipeline because of internal error");
}
/// <summary>
/// Get the value of node's property in NNStreamer pipelines.
/// </summary>
/// <typeparam name="T">The value type of given property.</typeparam>
/// <param name="propertyName">The property name.</param>
/// <returns>The value of given property.</returns>
/// <feature>http://tizen.org/feature/machine_learning.inference</feature>
/// <exception cref="NotSupportedException">Thrown when the feature is not supported.</exception>
/// <exception cref="ArgumentException">Thrown when the method failed due to an invalid parameter.</exception>
/// <since_tizen> 8 </since_tizen>
public T GetValue <T>(string propertyName)
{
NNStreamerError ret;
CheckGetParam(propertyName);
if (typeof(bool).IsAssignableFrom(typeof(T)))
{
ret = Interop.Pipeline.GetPropertyBool(Handle, propertyName, out int value);
NNStreamer.CheckException(ret, string.Format("Failed to get {0} property.", propertyName));
return((T)Convert.ChangeType(value == 0 ? false : true, typeof(T)));
}
else if (typeof(string).IsAssignableFrom(typeof(T)))
{
ret = Interop.Pipeline.GetPropertyString(Handle, propertyName, out string value);
NNStreamer.CheckException(ret, string.Format("Failed to get {0} property.", propertyName));
return((T)Convert.ChangeType(value, typeof(T)));
}
else if (typeof(int).IsAssignableFrom(typeof(T)))
{
ret = Interop.Pipeline.GetPropertyInt32(Handle, propertyName, out int value);
NNStreamer.CheckException(ret, string.Format("Failed to get {0} property.", propertyName));
return((T)Convert.ChangeType(value, typeof(T)));
}
else if (typeof(long).IsAssignableFrom(typeof(T)))
{
ret = Interop.Pipeline.GetPropertyInt64(Handle, propertyName, out long value);
NNStreamer.CheckException(ret, string.Format("Failed to get {0} property.", propertyName));
return((T)Convert.ChangeType(value, typeof(T)));
}
else if (typeof(uint).IsAssignableFrom(typeof(T)))
{
ret = Interop.Pipeline.GetPropertyUInt32(Handle, propertyName, out uint value);
NNStreamer.CheckException(ret, string.Format("Failed to get {0} property.", propertyName));
return((T)Convert.ChangeType(value, typeof(T)));
}
else if (typeof(ulong).IsAssignableFrom(typeof(T)))
{
ret = Interop.Pipeline.GetPropertyUInt64(Handle, propertyName, out ulong value);
NNStreamer.CheckException(ret, string.Format("Failed to get {0} property.", propertyName));
return((T)Convert.ChangeType(value, typeof(T)));
}
else if (typeof(double).IsAssignableFrom(typeof(T)))
{
ret = Interop.Pipeline.GetPropertyDouble(Handle, propertyName, out double value);
NNStreamer.CheckException(ret, string.Format("Failed to get {0} property.", propertyName));
return((T)Convert.ChangeType(value, typeof(T)));
}
throw new ArgumentException("The Input data type is not valid.");
}
private void CheckGetParam(string propertyName)
{
NNStreamer.CheckNNStreamerSupport();
if (string.IsNullOrEmpty(propertyName))
{
throw NNStreamerExceptionFactory.CreateException(NNStreamerError.InvalidParameter, "Property name is invalid");
}
}
/// <summary>
/// Sets a tensor data to given index.
/// </summary>
/// <param name="index">The index of the tensor.</param>
/// <param name="buffer">Raw tensor data to be set.</param>
/// <feature>http://tizen.org/feature/machine_learning.inference</feature>
/// <exception cref="NotSupportedException">Thrown when the feature is not supported.</exception>
/// <exception cref="ArgumentException">Thrown when the data is not valid.</exception>
/// <since_tizen> 6 </since_tizen>
public void SetTensorData(int index, byte[] buffer)
{
NNStreamer.CheckNNStreamerSupport();
CheckIndex(index);
CheckDataBuffer(index, buffer);
_dataList[index] = buffer;
}
internal SwitchNode(string name, Pipeline pipe) : base(NodeType.Switch, name, pipe)
{
IntPtr handle = IntPtr.Zero;
NNStreamerError ret = Interop.Pipeline.GetSwitchHandle(pipe.GetHandle(), name, out _type, out handle);
NNStreamer.CheckException(ret, "Failed to get the switch node handle: " + name);
Handle = handle;
}
internal Node(string name, Pipeline pipe) : base(NodeType.Normal, name, pipe)
{
IntPtr handle = IntPtr.Zero;
NNStreamerError ret = Interop.Pipeline.GetElementHandle(pipe.GetHandle(), name, out handle);
NNStreamer.CheckException(ret, "Failed to get the pipeline node handle: " + name);
Handle = handle;
}
/// <summary>
/// Gets the boolean of node's property in NNStreamer pipelines.
/// </summary>
/// <param name="propertyName">The property name.</param>
/// <param name="retValue">On return, a boolean value.</param>
/// <feature>http://tizen.org/feature/machine_learning.inference</feature>
/// <exception cref="NotSupportedException">Thrown when the feature is not supported.</exception>
/// <exception cref="ArgumentException">Thrown when the method failed due to an invalid parameter.</exception>
/// <since_tizen> 8 </since_tizen>
public void GetValue(string propertyName, out bool retValue)
{
CheckGetParam(propertyName);
NNStreamerError ret = Interop.Pipeline.GetPropertyBool(Handle, propertyName, out int value);
NNStreamer.CheckException(ret, string.Format("Failed to get {0} property.", propertyName));
retValue = value == 0 ? false : true;
}
/// <summary>
/// Gets the floating-point value of node's property in NNStreamer pipelines.
/// </summary>
/// <param name="propertyName">The property name.</param>
/// <param name="retValue">On return, a floating-point value.</param>
/// <feature>http://tizen.org/feature/machine_learning.inference</feature>
/// <exception cref="NotSupportedException">Thrown when the feature is not supported.</exception>
/// <exception cref="ArgumentException">Thrown when the method failed due to an invalid parameter.</exception>
/// <since_tizen> 8 </since_tizen>
public void GetValue(string propertyName, out double retValue)
{
CheckGetParam(propertyName);
NNStreamerError ret = Interop.Pipeline.GetPropertyDouble(Handle, propertyName, out double value);
NNStreamer.CheckException(ret, string.Format("Failed to get {0} property.", propertyName));
retValue = value;
}
/// <summary>
/// Loads the neural network model and configures runtime environment
/// </summary>
/// <param name="modelAbsPath">Absolute path to the neural network model file.</param>
/// <param name="inTensorsInfo">Input TensorsInfo object</param>
/// <param name="outTensorsInfo">Output TensorsInfo object for inference result</param>
/// <feature>http://tizen.org/feature/machine_learning.inference</feature>
/// <exception cref="ArgumentException">Thrown when the method failed due to an invalid parameter.</exception>
/// <exception cref="NotSupportedException">Thrown when the feature is not supported.</exception>
/// <since_tizen> 6 </since_tizen>
public SingleShot(string modelAbsPath, TensorsInfo inTensorsInfo, TensorsInfo outTensorsInfo)
{
NNStreamer.CheckNNStreamerSupport();
if (inTensorsInfo == null || outTensorsInfo == null)
{
throw NNStreamerExceptionFactory.CreateException(NNStreamerError.InvalidParameter, "TensorsInfo is null");
}
CreateSingleShot(modelAbsPath, inTensorsInfo, outTensorsInfo, NNFWType.Any, HWType.Any, false);
}
/// <summary>
/// Controls the valve. Set the flag true to open (let the flow pass), false to close (stop the flow).
/// </summary>
/// <param name="open">The flag to control the flow</param>
/// <feature>http://tizen.org/feature/machine_learning.inference</feature>
/// <exception cref="NotSupportedException">Thrown when the feature is not supported.</exception>
/// <exception cref="InvalidOperationException">Thrown when the node is invalid.</exception>
/// <since_tizen> 8 </since_tizen>
public void Control(bool open)
{
if (!Valid)
{
NNStreamer.CheckException(NNStreamerError.InvalidOperation, "Current node is invalid: " + Name);
}
NNStreamerError ret = Interop.Pipeline.OpenValve(Handle, open);
NNStreamer.CheckException(ret, "Failed to set valve status: " + Name);
}
private void Unregister()
{
if (Handle != IntPtr.Zero)
{
/* Unregister the data callback from sink node */
NNStreamerError ret = Interop.Pipeline.UnregisterSinkCallback(Handle);
NNStreamer.CheckException(ret, "Failed to unregister sink node callback: " + Name);
Handle = IntPtr.Zero;
}
}
/// <summary>
/// Loads the neural network model and configures runtime environment with Neural Network Framework and HW information
/// </summary>
/// <param name="modelAbsPath">Absolute path to the neural network model file.</param>
/// <param name="inTensorsInfo">Input TensorsInfo object</param>
/// <param name="outTensorsInfo">Output TensorsInfo object for inference result</param>
/// <param name="fwType">Types of Neural Network Framework</param>
/// <param name="hwType">Types of hardware resources to be used for NNFWs</param>
/// <param name="isDynamicMode">Support Dynamic Mode</param>
/// <exception cref="ArgumentException">Thrown when the method failed due to an invalid parameter.</exception>
/// <exception cref="NotSupportedException">Thrown when the feature is not supported.</exception>
/// <since_tizen> 8 </since_tizen>
public SingleShot(string modelAbsPath,
TensorsInfo inTensorsInfo, TensorsInfo outTensorsInfo, NNFWType fwType, HWType hwType, bool isDynamicMode)
{
NNStreamer.CheckNNStreamerSupport();
if (inTensorsInfo == null || outTensorsInfo == null)
{
throw NNStreamerExceptionFactory.CreateException(NNStreamerError.InvalidParameter, "TensorsInfo is null");
}
CreateSingleShot(modelAbsPath, inTensorsInfo, outTensorsInfo, fwType, hwType, isDynamicMode);
}
internal void PrepareInvoke()
{
NNStreamerError ret = NNStreamerError.None;
int count = _dataList.Count;
for (int i = 0; i < count; ++i)
{
byte[] data = (byte[])_dataList[i];
ret = Interop.Util.SetTensorData(_handle, i, data, data.Length);
NNStreamer.CheckException(ret, "unable to set the buffer of TensorsData: " + i.ToString());
}
}
/// <summary>
/// Invokes the model with the given input data.
/// </summary>
/// <param name="inTensorsData">The input data to be inferred.</param>
/// <returns>TensorsData instance which contains the inferred result.</returns>
/// <feature>http://tizen.org/feature/machine_learning.inference</feature>
/// <exception cref="ArgumentException">Thrown when the method failed due to an invalid parameter.</exception>
/// <exception cref="IOException">Thrown when failed to push an input data into source element.</exception>
/// <exception cref="TimeoutException">Thrown when failed to get the result from sink element.</exception>
/// <exception cref="NotSupportedException">Thrown when the feature is not supported.</exception>
/// <since_tizen> 6 </since_tizen>
public TensorsData Invoke(TensorsData inTensorsData)
{
TensorsData out_data;
IntPtr out_ptr;
NNStreamerError ret = NNStreamerError.None;
ret = Interop.SingleShot.InvokeSingle(_handle, inTensorsData.Handle, out out_ptr);
NNStreamer.CheckException(ret, "fail to invoke the single inference engine");
out_data = TensorsData.CreateFromNativeHandle(out_ptr);
return(out_data);
}