public SeqLabel(SeqLabelOptions options, Vocab srcVocab = null, Vocab clsVocab = null)
: base(options.DeviceIds, options.ProcessorType, options.ModelFilePath, options.MemoryUsageRatio, options.CompilerOptions, options.ValidIntervalHours, updateFreq: options.UpdateFreq)
{
m_shuffleType = options.ShuffleType;
m_options = options;
// Model must exist if current task is not for training
if ((m_options.Task != ModeEnums.Train) && !File.Exists(m_options.ModelFilePath))
{
throw new FileNotFoundException($"Model '{m_options.ModelFilePath}' doesn't exist.");
}
if (File.Exists(m_options.ModelFilePath))
{
if (srcVocab != null || clsVocab != null)
{
throw new ArgumentException($"Model '{m_options.ModelFilePath}' exists and it includes vocabulary, so input vocabulary must be null.");
}
// Model file exists, so we load it from file.
m_modelMetaData = LoadModelImpl_WITH_CONVERT(CreateTrainableParameters);
//m_modelMetaData = LoadModelImpl();
//---LoadModel_As_BinaryFormatter( CreateTrainableParameters );
}
else
{
// Model doesn't exist, we create it and initlaize parameters
m_modelMetaData = new SeqLabelModel(options.HiddenSize, options.EmbeddingDim, options.EncoderLayerDepth, options.MultiHeadNum, options.EncoderType, srcVocab, clsVocab, options.MaxSegmentNum);
//Initializng weights in encoders and decoders
CreateTrainableParameters(m_modelMetaData);
}
m_modelMetaData.ShowModelInfo();
}
private static void Main(string[] args)
{
ShowOptions(args);
Logger.LogFile = $"{nameof(SeqLabelConsole)}_{Utils.GetTimeStamp(DateTime.Now)}.log";
//Parse command line
SeqLabelOptions opts = new SeqLabelOptions();
ArgParser argParser = new ArgParser(args, opts);
if (!opts.ConfigFilePath.IsNullOrEmpty())
{
Logger.WriteLine($"Loading config file from '{opts.ConfigFilePath}'");
opts = JsonConvert.DeserializeObject<SeqLabelOptions>(File.ReadAllText(opts.ConfigFilePath));
}
DecodingOptions decodingOptions = opts.CreateDecodingOptions();
SeqLabel sl = null;
//Parse device ids from options
int[] deviceIds = opts.DeviceIds.Split(',').Select(x => int.Parse(x)).ToArray();
if ( opts.Task == ModeEnums.Train )
{
// Load train corpus
SeqLabelingCorpus trainCorpus = new SeqLabelingCorpus(opts.TrainCorpusPath, opts.BatchSize, opts.ShuffleBlockSize, maxSentLength: opts.MaxTrainSentLength);
// Load valid corpus
List<SeqLabelingCorpus> validCorpusList = new List<SeqLabelingCorpus>();
if (!opts.ValidCorpusPaths.IsNullOrEmpty())
{
string[] validCorpusPathList = opts.ValidCorpusPaths.Split(';');
foreach (var validCorpusPath in validCorpusPathList)
{
validCorpusList.Add(new SeqLabelingCorpus(opts.ValidCorpusPaths, opts.BatchSize, opts.ShuffleBlockSize, maxSentLength: opts.MaxTestSentLength));
}
}
// Load or build vocabulary
Vocab srcVocab = null;
Vocab tgtVocab = null;
if (!opts.SrcVocab.IsNullOrEmpty() && !opts.TgtVocab.IsNullOrEmpty() )
{
// Vocabulary files are specified, so we load them
srcVocab = new Vocab(opts.SrcVocab);
tgtVocab = new Vocab(opts.TgtVocab);
}
else
{
// We don't specify vocabulary, so we build it from train corpus
(srcVocab, tgtVocab) = trainCorpus.BuildVocabs(opts.SrcVocabSize);
}
// Create learning rate
ILearningRate learningRate = new DecayLearningRate(opts.StartLearningRate, opts.WarmUpSteps, opts.WeightsUpdateCount);
// Create optimizer
IOptimizer optimizer = Misc.CreateOptimizer(opts);
// Create metrics
List<IMetric> metrics = new List<IMetric>();
foreach (string word in tgtVocab.Items)
{
if (BuildInTokens.IsPreDefinedToken(word) == false)
{
metrics.Add(new SequenceLabelFscoreMetric(word));
}
}
if (File.Exists(opts.ModelFilePath) == false)
{
//New training
sl = new SeqLabel(opts, srcVocab: srcVocab, clsVocab: tgtVocab);
}
else
{
//Incremental training
Logger.WriteLine($"Loading model from '{opts.ModelFilePath}'...");
sl = new SeqLabel(opts);
}
// Add event handler for monitoring
sl.StatusUpdateWatcher += Misc.Ss_StatusUpdateWatcher;
// Kick off training
sl.Train(maxTrainingEpoch: opts.MaxEpochNum, trainCorpus: trainCorpus, validCorpusList: validCorpusList.ToArray(), learningRate: learningRate, optimizer: optimizer, metrics: metrics, decodingOptions: decodingOptions);
}
else if ( opts.Task == ModeEnums.Valid )
{
Logger.WriteLine($"Evaluate model '{opts.ModelFilePath}' by valid corpus '{opts.ValidCorpusPaths}'");
// Load valid corpus
SeqLabelingCorpus validCorpus = new SeqLabelingCorpus(opts.ValidCorpusPaths, opts.BatchSize, opts.ShuffleBlockSize, opts.MaxTestSentLength);
(Vocab srcVocab, Vocab tgtVocab) = validCorpus.BuildVocabs();
// Create metrics
List<IMetric> metrics = new List<IMetric>();
foreach (string word in tgtVocab.Items)
{
if (BuildInTokens.IsPreDefinedToken(word) == false)
{
metrics.Add(new SequenceLabelFscoreMetric(word));
}
}
sl = new SeqLabel(opts);
sl.Valid(validCorpus: validCorpus, metrics: metrics, decodingOptions: decodingOptions);
}
else if ( opts.Task == ModeEnums.Test )
{
Logger.WriteLine($"Test model '{opts.ModelFilePath}' by input corpus '{opts.InputTestFile}'");
//Test trained model
sl = new SeqLabel(opts);
List<string> outputLines = new List<string>();
string[] data_sents_raw1 = File.ReadAllLines(opts.InputTestFile);
foreach (string line in data_sents_raw1)
{
var nrs = sl.Test<SeqLabelingCorpusBatch>(ConstructInputTokens(line.Trim().Split(' ').ToList()), null, decodingOptions: decodingOptions);
outputLines.AddRange(nrs[0].Output[0].Select(x => string.Join(" ", x)));
}
File.WriteAllLines(opts.OutputFile, outputLines);
}
else
{
argParser.Usage();
}
}
