private static void loadBatch(DataBatchArgs e, List <Tuple <byte[], int> > rgData, ref int nIdx)
{
string strData;
List <int> rgDataDim = e.GetInputAt(0, out strData);
string strLabel;
List <int> rgLabelDim = e.GetInputAt(1, out strLabel);
float[] rgRawData = new float[rgData[0].Item1.Length * e.BatchSize];
float[] rgRawLabels = new float[e.BatchSize * rgLabelDim[0]];
List <int> rgDimData = new List <int>()
{
e.BatchSize
};
List <int> rgDimLabels = new List <int>()
{
e.BatchSize
};
int nDataOffset = 0;
int nLabelOffset = 0;
rgDimData.AddRange(rgDataDim);
rgDimLabels.AddRange(rgLabelDim);
for (int i = 0; i < e.BatchSize; i++)
{
int nLabel = rgData[nIdx].Item2;
rgRawLabels[nLabelOffset + nLabel] = 1;
for (int j = 0; j < rgData[nIdx].Item1.Length; j++)
{
float fVal = rgData[nIdx].Item1[j];
// Binarize data
if (fVal > 0)
{
fVal = 1.0f;
}
rgRawData[nDataOffset + j] = fVal;
}
nDataOffset += rgData[nIdx].Item1.Length;
nLabelOffset += 10;
nIdx++;
if (nIdx == rgData.Count)
{
nIdx = 0;
}
}
DenseTensor <float> tensorData = new DenseTensor <float>(rgRawData, rgDimData.ToArray());
DenseTensor <float> tensorLabels = new DenseTensor <float>(rgRawLabels, rgDimLabels.ToArray());
e.Values.Add(NamedOnnxValue.CreateFromTensor <float>(strData, tensorData));
e.Values.Add(NamedOnnxValue.CreateFromTensor <float>(strLabel, tensorLabels));
}
private static void OnGetTrainingDataBatch(object sender, DataBatchArgs e)
{
loadBatch(e, m_rgTestingData, ref m_nTestingDataIdx);
}
