public float[] Run(int nN)
{
try
{
Stopwatch sw = new Stopwatch();
float[] rgPredictions = new float[nN];
sw.Start();
m_bIsDataReal = true;
if (m_rgVocabulary != null)
{
m_bIsDataReal = m_rgVocabulary.IsDataReal;
}
m_mycaffe.Log.Enable = false;
if (m_bIsDataReal && !m_bUsePreloadData)
{
string strSolverErr = "";
int nLookahead = 1;
if (m_nSolverSequenceLength >= 0 && m_nSolverSequenceLength < m_nSequenceLength)
{
nLookahead = m_nSequenceLength - m_nSolverSequenceLength;
}
rgPredictions = new float[nN * 2 * nLookahead];
for (int i = 0; i < nN; i++)
{
GetDataArgs e = getDataArgs(Phase.RUN, 0, 0, true);
m_icallback.OnGetData(e);
int nExpectedCount = m_blobData.count();
m_mycaffe.Log.CHECK_EQ(nExpectedCount, e.State.Data.ItemCount, strSolverErr + "The size of the data received ('" + e.State.Data.ItemCount.ToString() + "') does mot match the expected data count of '" + nExpectedCount.ToString() + "'!");
m_blobData.mutable_cpu_data = e.State.Data.GetData <T>();
if (m_blobLabel != null)
{
nExpectedCount = m_blobLabel.count();
m_mycaffe.Log.CHECK_EQ(nExpectedCount, e.State.Label.ItemCount, strSolverErr + "The size of the label received ('" + e.State.Label.ItemCount.ToString() + "') does not match the expected label count of '" + nExpectedCount.ToString() + "'!");
m_blobLabel.mutable_cpu_data = e.State.Label.GetData <T>();
}
double dfLoss;
BlobCollection <T> colResults = m_net.Forward(out dfLoss);
Blob <T> blobOutput = colResults[0];
if (m_blobOutput != null)
{
blobOutput = m_blobOutput;
}
float[] rgResults = Utility.ConvertVecF <T>(blobOutput.update_cpu_data());
for (int j = nLookahead; j > 0; j--)
{
float fPrediction = getLastPrediction(rgResults, m_rgVocabulary, j);
int nIdx = e.State.Label.ItemCount - j;
float fActual = (float)e.State.Label.GetDataAtF(nIdx);
int nIdx0 = ((nLookahead - j) * nN * 2);
int nIdx1 = nIdx0 + nN;
if (m_dfScale != 1.0 && m_dfScale > 0)
{
fActual /= (float)m_dfScale;
}
if (m_rgVocabulary == null || m_bDisableVocabulary)
{
if (m_dfScale != 1.0 && m_dfScale > 0)
{
fPrediction /= (float)m_dfScale;
}
rgPredictions[nIdx0 + i] = fPrediction;
rgPredictions[nIdx1 + i] = fActual;
}
else
{
rgPredictions[nIdx0 + i] = (float)m_rgVocabulary.GetValueAt((int)fPrediction, true);
rgPredictions[nIdx1 + i] = (float)m_rgVocabulary.GetValueAt((int)fActual, true);
}
}
if (sw.Elapsed.TotalMilliseconds > 1000)
{
double dfPct = (double)i / (double)nN;
m_mycaffe.Log.Enable = true;
m_mycaffe.Log.Progress = dfPct;
m_mycaffe.Log.WriteLine("Running at " + dfPct.ToString("P") + " complete...");
m_mycaffe.Log.Enable = false;
sw.Restart();
}
if (m_mycaffe.CancelEvent.WaitOne(0))
{
break;
}
}
}
else
{
int nIdx = 0;
List <T> rgInput = rgInput = getInitialInput(m_bIsDataReal);
for (int i = 0; i < nN; i++)
{
T[] rgInputVector = new T[m_blobData.count()];
for (int j = 0; j < m_nSequenceLength; j++)
{
// The batch is filled with 0 except for the first sequence which is the one we want to use for prediction.
nIdx = j * m_nBatchSize;
rgInputVector[nIdx] = rgInput[j];
}
m_blobData.mutable_cpu_data = rgInputVector;
double dfLoss;
BlobCollection <T> colResults = m_net.Forward(out dfLoss);
Blob <T> blobOutput = colResults[0];
if (m_blobOutput != null)
{
blobOutput = m_blobOutput;
}
float[] rgResults = Utility.ConvertVecF <T>(blobOutput.update_cpu_data());
float fPrediction = getLastPrediction(rgResults, m_rgVocabulary, 1);
//Add the new prediction and discard the oldest one
rgInput.Add((T)Convert.ChangeType(fPrediction, typeof(T)));
rgInput.RemoveAt(0);
if (m_rgVocabulary == null || m_bDisableVocabulary)
{
rgPredictions[i] = fPrediction;
}
else
{
rgPredictions[i] = (float)m_rgVocabulary.GetValueAt((int)fPrediction);
}
if (sw.Elapsed.TotalMilliseconds > 1000)
{
double dfPct = (double)i / (double)nN;
m_mycaffe.Log.Enable = true;
m_mycaffe.Log.Progress = dfPct;
m_mycaffe.Log.WriteLine("Running at " + dfPct.ToString("P") + " complete...");
m_mycaffe.Log.Enable = false;
sw.Restart();
}
if (m_mycaffe.CancelEvent.WaitOne(0))
{
break;
}
}
}
return(rgPredictions);
}
catch (Exception excpt)
{
throw excpt;
}
finally
{
m_mycaffe.Log.Enable = true;
}
}
public float[] Run(int nN)
{
m_bIsDataReal = false;
if (m_rgVocabulary != null)
{
m_bIsDataReal = m_rgVocabulary.IsDataReal;
}
int nIdx = 0;
Stopwatch sw = new Stopwatch();
float[] rgPredictions = new float[nN];
List <T> rgInput = getInitialInput(m_bIsDataReal);
sw.Start();
for (int i = 0; i < nN; i++)
{
T[] rgInputVector = new T[m_blobData.count()];
for (int j = 0; j < m_nSequenceLength; j++)
{
// The batch is filled with 0 except for the first sequence which is the one we want to use for prediction.
nIdx = j * m_nBatchSize;
rgInputVector[nIdx] = rgInput[j];
}
m_blobData.mutable_cpu_data = rgInputVector;
double dfLoss;
BlobCollection <T> colResults = m_net.Forward(out dfLoss);
float fPrediction = getLastPrediction(colResults[0], m_rgVocabulary);
//Add the new prediction and discard the oldest one
rgInput.Add((T)Convert.ChangeType(fPrediction, typeof(T)));
rgInput.RemoveAt(0);
if (m_rgVocabulary == null)
{
rgPredictions[i] = fPrediction;
}
else
{
rgPredictions[i] = (float)m_rgVocabulary.GetValueAt((int)fPrediction);
}
if (sw.Elapsed.TotalMilliseconds > 1000)
{
double dfPct = (double)i / (double)nN;
m_mycaffe.Log.Progress = dfPct;
m_mycaffe.Log.WriteLine("Running at " + dfPct.ToString("P") + " complete...");
sw.Restart();
}
if (m_mycaffe.CancelEvent.WaitOne(0))
{
break;
}
}
return(rgPredictions);
}