/// <summary>
/// Constructor: model location relative to .exe, mlcontext, model settings of input name and output name
/// </summary>
/// <param name="inputModelLocation"></param>
/// <param name="inputMlContext"></param>
/// <param name="inputModelSettings"></param>
public OnnxRNNScorer(string inputModelLocation, MLContext inputMlContext, ModelSettings inputModelSettings)
{
modelLocation = inputModelLocation;
mlContext = inputMlContext;
modelSettings = inputModelSettings;
pipeline = mlContext.Transforms.ApplyOnnxModel(modelFile: modelLocation, outputColumnNames: new[] { modelSettings.modelOutput }, inputColumnNames: new[] { modelSettings.modelInput });
}
public HeadposeRNN(Pipeline pipeline, string modelNameFile, ModelSettings modelSetting)
{
// Create the receiver.
In = pipeline.CreateReceiver <Pose>(this, ReceiveData, nameof(In));
// Create the emitter.
Out = pipeline.CreateEmitter <float>(this, nameof(Out));
// location of the model relative to .exe
modelFilePath = OnnxUtility.ModelAbsoluteFilename(modelNameFile);
// create a scorer which has the model and setting
mlContext = new MLContext();
modelScorer = new OnnxRNNScorer(modelFilePath, mlContext, modelSetting);
List <float> zerosList = new List <float>(new float[NUM_OF_FEATURES]);
for (int i = 0; i < NUM_OF_FRAME; i++)
{
dataQueueTransDiff.AddLast(zerosList);
dataQueueTTRRDiffDiff2.AddLast(zerosList);
}
lastInputTRTRDiffDiff2 = zerosList;
}
