/// <summary>
/// Pack the data in an ordering expected by the RNN layer used. This method is only called when using Data/Clip inputs.
/// </summary>
/// <param name="sender">Specifies the sender of the event, which is the MemoryDataLayer.</param>
/// <param name="e">Specifies the event parameters.</param>
/// <remarks>
/// This method is responsible for packing the datum received into the data blob within the event arguments and do so in the
/// expected LSTM ordering.
/// </remarks>
private void memData_OnDataPack(object sender, MemoryDataLayerPackDataArgs<T> e)
{
List<int> rgDataShape = e.Data.shape();
List<int> rgClipShape = e.Clip.shape();
List<int> rgLabelShape = e.Label.shape();
int nBatch = e.DataItems.Count;
int nSeqLen = rgDataShape[0];
e.Data.Log.CHECK_GT(nSeqLen, 0, "The sequence lenth must be greater than zero!");
e.Data.Log.CHECK_EQ(nBatch, e.ClipItems.Count, "The data and clip should have the same number of items.");
e.Data.Log.CHECK_EQ(nSeqLen, rgClipShape[0], "The data and clip should have the same sequence count.");
rgDataShape[1] = nBatch; // LSTM uses sizing: seq, batch, data1, data2
rgClipShape[1] = nBatch;
rgLabelShape[1] = nBatch;
e.Data.Reshape(rgDataShape);
e.Clip.Reshape(rgClipShape);
e.Label.Reshape(rgLabelShape);
T[] rgRawData = new T[e.Data.count()];
T[] rgRawClip = new T[e.Clip.count()];
T[] rgRawLabel = new T[e.Label.count()];
int nDataSize = e.Data.count(2);
T[] rgDataItem = new T[nDataSize];
T dfClip;
int nIdx;
for (int i = 0; i < nBatch; i++)
{
Datum data = e.DataItems[i];
Datum clip = e.ClipItems[i];
T[] rgLabel = unpackLabel(data);
for (int j = 0; j < nSeqLen; j++)
{
dfClip = clip.GetDataAt<T>(j);
for (int k = 0; k < nDataSize; k++)
{
rgDataItem[k] = data.GetDataAt<T>(j * nDataSize + k);
}
// LSTM: Create input data, the data must be in the order
// seq1_val1, seq2_val1, ..., seqBatch_Size_val1, seq1_val2, seq2_val2, ..., seqBatch_Size_valSequence_Length
if (e.LstmType == LayerParameter.LayerType.LSTM)
nIdx = nBatch * j + i;
// LSTM_SIMPLE: Create input data, the data must be in the order
// seq1_val1, seq1_val2, ..., seq1_valBatchSize, seq2_val1, seq2_val2, ..., seqSequenceLength_valBatchSize
else
nIdx = i * nBatch + j;
Array.Copy(rgDataItem, 0, rgRawData, nIdx * nDataSize, nDataSize);
rgRawClip[nIdx] = dfClip;
if (rgLabel != null)
{
if (rgLabel.Length == nSeqLen)
rgRawLabel[nIdx] = rgLabel[j];
else if (rgLabel.Length == 1)
{
if (j == nSeqLen - 1)
rgRawLabel[0] = rgLabel[0];
}
else
{
throw new Exception("The Solver SequenceLength parameter does not match the actual sequence length! The label length '" + rgLabel.Length.ToString() + "' must be either '1' for SINGLE labels, or the sequence length of '" + nSeqLen.ToString() + "' for MULTI labels. Stopping training.");
}
}
}
}
e.Data.mutable_cpu_data = rgRawData;
e.Clip.mutable_cpu_data = rgRawClip;
e.Label.mutable_cpu_data = rgRawLabel;
m_nRecurrentSequenceLength = nSeqLen;
}