/// <summary>
/// Given input sentence and generate output sentence by seq2seq model with beam search
/// </summary>
/// <param name="input"></param>
/// <param name="beamSearchSize"></param>
/// <param name="maxOutputLength"></param>
/// <returns></returns>
public List <List <string> > Predict(List <string> input, int beamSearchSize = 1, int maxOutputLength = 100)
{
(IEncoder encoder, IDecoder decoder, IWeightTensor srcEmbedding, IWeightTensor tgtEmbedding) = GetNetworksOnDeviceAt(-1);
List <List <string> > inputSeqs = ParallelCorpus.ConstructInputTokens(input);
int batchSize = 1; // For predict with beam search, we currently only supports one sentence per call
IComputeGraph g = CreateComputGraph(m_defaultDeviceId, needBack: false);
AttentionDecoder rnnDecoder = decoder as AttentionDecoder;
encoder.Reset(g.GetWeightFactory(), batchSize);
rnnDecoder.Reset(g.GetWeightFactory(), batchSize);
// Construct beam search status list
List <BeamSearchStatus> bssList = new List <BeamSearchStatus>();
BeamSearchStatus bss = new BeamSearchStatus();
bss.OutputIds.Add((int)SENTTAGS.START);
bss.CTs = rnnDecoder.GetCTs();
bss.HTs = rnnDecoder.GetHTs();
bssList.Add(bss);
IWeightTensor encodedWeightMatrix = Encode(g, inputSeqs, encoder, srcEmbedding, null, null);
AttentionPreProcessResult attPreProcessResult = rnnDecoder.PreProcess(encodedWeightMatrix, batchSize, g);
List <BeamSearchStatus> newBSSList = new List <BeamSearchStatus>();
bool finished = false;
int outputLength = 0;
while (finished == false && outputLength < maxOutputLength)
{
finished = true;
for (int i = 0; i < bssList.Count; i++)
{
bss = bssList[i];
if (bss.OutputIds[bss.OutputIds.Count - 1] == (int)SENTTAGS.END)
{
newBSSList.Add(bss);
}
else if (bss.OutputIds.Count > maxOutputLength)
{
newBSSList.Add(bss);
}
else
{
finished = false;
int ix_input = bss.OutputIds[bss.OutputIds.Count - 1];
rnnDecoder.SetCTs(bss.CTs);
rnnDecoder.SetHTs(bss.HTs);
IWeightTensor x = g.PeekRow(tgtEmbedding, ix_input);
IWeightTensor eOutput = rnnDecoder.Decode(x, attPreProcessResult, batchSize, g);
using (IWeightTensor probs = g.Softmax(eOutput))
{
List <int> preds = probs.GetTopNMaxWeightIdx(beamSearchSize);
for (int j = 0; j < preds.Count; j++)
{
BeamSearchStatus newBSS = new BeamSearchStatus();
newBSS.OutputIds.AddRange(bss.OutputIds);
newBSS.OutputIds.Add(preds[j]);
newBSS.CTs = rnnDecoder.GetCTs();
newBSS.HTs = rnnDecoder.GetHTs();
float score = probs.GetWeightAt(preds[j]);
newBSS.Score = bss.Score;
newBSS.Score += (float)(-Math.Log(score));
//var lengthPenalty = Math.Pow((5.0f + newBSS.OutputIds.Count) / 6, 0.6);
//newBSS.Score /= (float)lengthPenalty;
newBSSList.Add(newBSS);
}
}
}
}
bssList = BeamSearch.GetTopNBSS(newBSSList, beamSearchSize);
newBSSList.Clear();
outputLength++;
}
// Convert output target word ids to real string
List <List <string> > results = new List <List <string> >();
for (int i = 0; i < bssList.Count; i++)
{
results.Add(m_modelMetaData.Vocab.ConvertTargetIdsToString(bssList[i].OutputIds));
}
return(results);
}
public List <List <string> > Predict(List <string> input, int beamSearchSize = 1)
{
var biEncoder = m_biEncoder[m_defaultDeviceId];
var srcEmbedding = m_srcEmbedding[m_defaultDeviceId];
var tgtEmbedding = m_tgtEmbedding[m_defaultDeviceId];
var decoder = m_decoder[m_defaultDeviceId];
var decoderFFLayer = m_decoderFFLayer[m_defaultDeviceId];
List <BeamSearchStatus> bssList = new List <BeamSearchStatus>();
var g = CreateComputGraph(m_defaultDeviceId, false);
Reset();
List <string> inputSeq = new List <string>();
inputSeq.Add(m_START);
inputSeq.AddRange(input);
inputSeq.Add(m_END);
var inputSeqs = new List <List <string> >();
inputSeqs.Add(inputSeq);
IWeightMatrix encodedWeightMatrix = Encode(g, inputSeqs, biEncoder, srcEmbedding);
var attPreProcessResult = decoder.PreProcess(encodedWeightMatrix, g);
BeamSearchStatus bss = new BeamSearchStatus();
bss.OutputIds.Add((int)SENTTAGS.START);
bss.CTs = decoder.GetCTs();
bss.HTs = decoder.GetHTs();
bssList.Add(bss);
List <BeamSearchStatus> newBSSList = new List <BeamSearchStatus>();
bool finished = false;
while (finished == false)
{
finished = true;
for (int i = 0; i < bssList.Count; i++)
{
bss = bssList[i];
if (bss.OutputIds[bss.OutputIds.Count - 1] == (int)SENTTAGS.END || bss.OutputIds.Count > m_maxWord)
{
newBSSList.Add(bss);
}
else
{
finished = false;
var ix_input = bss.OutputIds[bss.OutputIds.Count - 1];
decoder.SetCTs(bss.CTs);
decoder.SetHTs(bss.HTs);
var x = g.PeekRow(tgtEmbedding, ix_input);
var eOutput = decoder.Decode(x, attPreProcessResult, g);
var o = decoderFFLayer.Process(eOutput, g);
var probs = g.Softmax(o, false);
var preds = probs.GetTopNMaxWeightIdx(beamSearchSize);
for (int j = 0; j < preds.Count; j++)
{
BeamSearchStatus newBSS = new BeamSearchStatus();
newBSS.OutputIds.AddRange(bss.OutputIds);
newBSS.OutputIds.Add(preds[j]);
newBSS.CTs = decoder.GetCTs();
newBSS.HTs = decoder.GetHTs();
var score = probs.GetWeightAt(preds[j]);
newBSS.Score = bss.Score;
newBSS.Score += (float)(-Math.Log(score));
//var lengthPenalty = Math.Pow((5.0f + newBSS.OutputIds.Count) / 6, 0.6);
//newBSS.Score /= (float)lengthPenalty;
newBSSList.Add(newBSS);
}
}
}
bssList = GetTopNBSS(newBSSList, beamSearchSize);
newBSSList.Clear();
}
List <List <string> > results = new List <List <string> >();
for (int i = 0; i < bssList.Count; i++)
{
results.Add(PrintString(bssList[i].OutputIds));
}
return(results);
}