/// <summary>
/// Run a set of iterations and return the resuts.
/// </summary>
/// <param name="nIterations">Specifies the number of iterations to run.</param>
/// <param name="type">Returns the data type contained in the byte stream.</param>
/// <returns>The results of the run containing the action are returned as a byte stream.</returns>
public byte[] Run(int nIterations, out string type)
{
IxTrainerCallbackRNN icallback = m_icallback as IxTrainerCallbackRNN;
if (icallback == null)
throw new Exception("The Run method requires an IxTrainerCallbackRNN interface to convert the results into the native format!");
StateBase s = getData(Phase.RUN, -1);
int nIteration = 0;
List<float> rgResults = new List<float>();
while (!m_brain.Cancel.WaitOne(0) && (nIterations == -1 || nIteration < nIterations))
{
// Preprocess the observation.
SimpleDatum x = m_brain.Preprocess(s, m_bUseRawInput);
// Forward the policy network and sample an action.
float[] rgfAprob;
int action = m_brain.act(x, s.Clip, out rgfAprob);
rgResults.Add(s.Data.TimeStamp.ToFileTime());
rgResults.Add((float)s.Data.GetDataAtF(0));
rgResults.Add(action);
// Take the next step using the action
StateBase s_ = getData(Phase.RUN, action);
nIteration++;
}
ConvertOutputArgs args = new ConvertOutputArgs(nIterations, rgResults.ToArray());
icallback.OnConvertOutput(args);
type = args.RawType;
return args.RawOutput;
}
/// <summary>
/// The Run method provides the main 'actor' that runs data through the trained network.
/// </summary>
/// <param name="nN">specifies the number of samples to run.</param>
/// <param name="type">Returns the data type contained in the byte stream.</param>
/// <returns>The results of the run are returned in the native format used by the CustomQuery.</returns>
public byte[] Run(int nN, out string type)
{
float[] rgResults = m_brain.Run(nN);
ConvertOutputArgs args = new ConvertOutputArgs(nN, rgResults);
IxTrainerCallbackRNN icallback = m_icallback as IxTrainerCallbackRNN;
if (icallback == null)
{
throw new Exception("The Run method requires an IxTrainerCallbackRNN interface to convert the results into the native format!");
}
icallback.OnConvertOutput(args);
type = args.RawType;
return(args.RawOutput);
}
public Brain(MyCaffeControl <T> mycaffe, PropertySet properties, CryptoRandom random, IxTrainerCallbackRNN icallback, Phase phase, BucketCollection rgVocabulary, bool bUsePreloadData, string strRunProperties = null)
{
string strOutputBlob = null;
if (strRunProperties != null)
{
m_runProperties = new PropertySet(strRunProperties);
}
m_icallback = icallback;
m_mycaffe = mycaffe;
m_properties = properties;
m_random = random;
m_rgVocabulary = rgVocabulary;
m_bUsePreloadData = bUsePreloadData;
m_nSolverSequenceLength = m_properties.GetPropertyAsInt("SequenceLength", -1);
m_bDisableVocabulary = m_properties.GetPropertyAsBool("DisableVocabulary", false);
m_nThreads = m_properties.GetPropertyAsInt("Threads", 1);
m_dfScale = m_properties.GetPropertyAsDouble("Scale", 1.0);
if (m_nThreads > 1)
{
m_dataPool.Initialize(m_nThreads, icallback);
}
if (m_runProperties != null)
{
m_dfTemperature = Math.Abs(m_runProperties.GetPropertyAsDouble("Temperature", 0));
if (m_dfTemperature > 1.0)
{
m_dfTemperature = 1.0;
}
string strPhaseOnRun = m_runProperties.GetProperty("PhaseOnRun", false);
switch (strPhaseOnRun)
{
case "RUN":
m_phaseOnRun = Phase.RUN;
break;
case "TEST":
m_phaseOnRun = Phase.TEST;
break;
case "TRAIN":
m_phaseOnRun = Phase.TRAIN;
break;
}
if (phase == Phase.RUN && m_phaseOnRun != Phase.NONE)
{
if (m_phaseOnRun != Phase.RUN)
{
m_mycaffe.Log.WriteLine("Warning: Running on the '" + m_phaseOnRun.ToString() + "' network.");
}
strOutputBlob = m_runProperties.GetProperty("OutputBlob", false);
if (strOutputBlob == null)
{
throw new Exception("You must specify the 'OutputBlob' when Running with a phase other than RUN.");
}
strOutputBlob = Utility.Replace(strOutputBlob, '~', ';');
phase = m_phaseOnRun;
}
}
m_net = mycaffe.GetInternalNet(phase);
if (m_net == null)
{
mycaffe.Log.WriteLine("WARNING: Test net does not exist, set test_iteration > 0. Using TRAIN phase instead.");
m_net = mycaffe.GetInternalNet(Phase.TRAIN);
}
// Find the first LSTM layer to determine how to load the data.
// NOTE: Only LSTM has a special loading order, other layers use the standard N, C, H, W ordering.
LSTMLayer <T> lstmLayer = null;
LSTMSimpleLayer <T> lstmSimpleLayer = null;
foreach (Layer <T> layer1 in m_net.layers)
{
if (layer1.layer_param.type == LayerParameter.LayerType.LSTM)
{
lstmLayer = layer1 as LSTMLayer <T>;
m_lstmType = LayerParameter.LayerType.LSTM;
break;
}
else if (layer1.layer_param.type == LayerParameter.LayerType.LSTM_SIMPLE)
{
lstmSimpleLayer = layer1 as LSTMSimpleLayer <T>;
m_lstmType = LayerParameter.LayerType.LSTM_SIMPLE;
break;
}
}
if (lstmLayer == null && lstmSimpleLayer == null)
{
throw new Exception("Could not find the required LSTM or LSTM_SIMPLE layer!");
}
if (m_phaseOnRun != Phase.NONE && m_phaseOnRun != Phase.RUN && strOutputBlob != null)
{
if ((m_blobOutput = m_net.FindBlob(strOutputBlob)) == null)
{
throw new Exception("Could not find the 'Output' layer top named '" + strOutputBlob + "'!");
}
}
if ((m_blobData = m_net.FindBlob("data")) == null)
{
throw new Exception("Could not find the 'Input' layer top named 'data'!");
}
if ((m_blobClip = m_net.FindBlob("clip")) == null)
{
throw new Exception("Could not find the 'Input' layer top named 'clip'!");
}
Layer <T> layer = m_net.FindLastLayer(LayerParameter.LayerType.INNERPRODUCT);
m_mycaffe.Log.CHECK(layer != null, "Could not find an ending INNERPRODUCT layer!");
if (!m_bDisableVocabulary)
{
m_nVocabSize = (int)layer.layer_param.inner_product_param.num_output;
if (rgVocabulary != null)
{
m_mycaffe.Log.CHECK_EQ(m_nVocabSize, rgVocabulary.Count, "The vocabulary count = '" + rgVocabulary.Count.ToString() + "' and last inner product output count = '" + m_nVocabSize.ToString() + "' - these do not match but they should!");
}
}
if (m_lstmType == LayerParameter.LayerType.LSTM)
{
m_nSequenceLength = m_blobData.shape(0);
m_nBatchSize = m_blobData.shape(1);
}
else
{
m_nBatchSize = (int)lstmSimpleLayer.layer_param.lstm_simple_param.batch_size;
m_nSequenceLength = m_blobData.shape(0) / m_nBatchSize;
if (phase == Phase.RUN)
{
m_nBatchSize = 1;
List <int> rgNewShape = new List <int>()
{
m_nSequenceLength, 1
};
m_blobData.Reshape(rgNewShape);
m_blobClip.Reshape(rgNewShape);
m_net.Reshape();
}
}
m_mycaffe.Log.CHECK_EQ(m_nSequenceLength, m_blobData.num, "The data num must equal the sequence lengh of " + m_nSequenceLength.ToString());
m_rgDataInput = new T[m_nSequenceLength * m_nBatchSize];
T[] rgClipInput = new T[m_nSequenceLength * m_nBatchSize];
m_mycaffe.Log.CHECK_EQ(rgClipInput.Length, m_blobClip.count(), "The clip count must equal the sequence length * batch size: " + rgClipInput.Length.ToString());
m_tZero = (T)Convert.ChangeType(0, typeof(T));
m_tOne = (T)Convert.ChangeType(1, typeof(T));
for (int i = 0; i < rgClipInput.Length; i++)
{
if (m_lstmType == LayerParameter.LayerType.LSTM)
{
rgClipInput[i] = (i < m_nBatchSize) ? m_tZero : m_tOne;
}
else
{
rgClipInput[i] = (i % m_nSequenceLength == 0) ? m_tZero : m_tOne;
}
}
m_blobClip.mutable_cpu_data = rgClipInput;
if (phase != Phase.RUN)
{
m_solver = mycaffe.GetInternalSolver();
m_solver.OnStart += m_solver_OnStart;
m_solver.OnTestStart += m_solver_OnTestStart;
m_solver.OnTestingIteration += m_solver_OnTestingIteration;
m_solver.OnTrainingIteration += m_solver_OnTrainingIteration;
if ((m_blobLabel = m_net.FindBlob("label")) == null)
{
throw new Exception("Could not find the 'Input' layer top named 'label'!");
}
m_nSequenceLengthLabel = m_blobLabel.count(0, 2);
m_rgLabelInput = new T[m_nSequenceLengthLabel];
m_mycaffe.Log.CHECK_EQ(m_rgLabelInput.Length, m_blobLabel.count(), "The label count must equal the label sequence length * batch size: " + m_rgLabelInput.Length.ToString());
m_mycaffe.Log.CHECK(m_nSequenceLengthLabel == m_nSequenceLength * m_nBatchSize || m_nSequenceLengthLabel == 1, "The label sqeuence length must be 1 or equal the length of the sequence: " + m_nSequenceLength.ToString());
}
}
public Brain(MyCaffeControl <T> mycaffe, PropertySet properties, CryptoRandom random, IxTrainerCallbackRNN icallback, Phase phase, BucketCollection rgVocabulary, string strRunProperties = null)
{
string strOutputBlob = null;
if (strRunProperties != null)
{
m_runProperties = new PropertySet(strRunProperties);
}
m_icallback = icallback;
m_mycaffe = mycaffe;
m_properties = properties;
m_random = random;
m_rgVocabulary = rgVocabulary;
if (m_runProperties != null)
{
m_dfTemperature = m_runProperties.GetPropertyAsDouble("Temperature", 0);
string strPhaseOnRun = m_runProperties.GetProperty("PhaseOnRun", false);
switch (strPhaseOnRun)
{
case "RUN":
m_phaseOnRun = Phase.RUN;
break;
case "TEST":
m_phaseOnRun = Phase.TEST;
break;
case "TRAIN":
m_phaseOnRun = Phase.TRAIN;
break;
}
if (phase == Phase.RUN && m_phaseOnRun != Phase.NONE)
{
if (m_phaseOnRun != Phase.RUN)
{
m_mycaffe.Log.WriteLine("Warning: Running on the '" + m_phaseOnRun.ToString() + "' network.");
}
strOutputBlob = m_runProperties.GetProperty("OutputBlob", false);
if (strOutputBlob == null)
{
throw new Exception("You must specify the 'OutputBlob' when Running with a phase other than RUN.");
}
strOutputBlob = Utility.Replace(strOutputBlob, '~', ';');
phase = m_phaseOnRun;
}
}
m_net = mycaffe.GetInternalNet(phase);
// Find the first LSTM layer to determine how to load the data.
// NOTE: Only LSTM has a special loading order, other layers use the standard N, C, H, W ordering.
LSTMLayer <T> lstmLayer = null;
LSTMSimpleLayer <T> lstmSimpleLayer = null;
foreach (Layer <T> layer1 in m_net.layers)
{
if (layer1.layer_param.type == LayerParameter.LayerType.LSTM)
{
lstmLayer = layer1 as LSTMLayer <T>;
m_lstmType = LayerParameter.LayerType.LSTM;
break;
}
else if (layer1.layer_param.type == LayerParameter.LayerType.LSTM_SIMPLE)
{
lstmSimpleLayer = layer1 as LSTMSimpleLayer <T>;
m_lstmType = LayerParameter.LayerType.LSTM_SIMPLE;
break;
}
}
if (lstmLayer == null && lstmSimpleLayer == null)
{
throw new Exception("Could not find the required LSTM or LSTM_SIMPLE layer!");
}
if (m_phaseOnRun != Phase.NONE && m_phaseOnRun != Phase.RUN && strOutputBlob != null)
{
if ((m_blobOutput = m_net.FindBlob(strOutputBlob)) == null)
{
throw new Exception("Could not find the 'Output' layer top named '" + strOutputBlob + "'!");
}
}
if ((m_blobData = m_net.FindBlob("data")) == null)
{
throw new Exception("Could not find the 'Input' layer top named 'data'!");
}
if ((m_blobClip = m_net.FindBlob("clip")) == null)
{
throw new Exception("Could not find the 'Input' layer top named 'clip'!");
}
Layer <T> layer = m_net.FindLastLayer(LayerParameter.LayerType.INNERPRODUCT);
m_mycaffe.Log.CHECK(layer != null, "Could not find an ending INNERPRODUCT layer!");
m_nVocabSize = (int)layer.layer_param.inner_product_param.num_output;
if (rgVocabulary != null)
{
m_mycaffe.Log.CHECK_EQ(m_nVocabSize, rgVocabulary.Count, "The vocabulary count and last inner product output count should match!");
}
if (m_lstmType == LayerParameter.LayerType.LSTM)
{
m_nSequenceLength = m_blobData.shape(0);
m_nBatchSize = m_blobData.shape(1);
}
else
{
m_nBatchSize = (int)lstmSimpleLayer.layer_param.lstm_simple_param.batch_size;
m_nSequenceLength = m_blobData.shape(0) / m_nBatchSize;
if (phase == Phase.RUN)
{
m_nBatchSize = 1;
List <int> rgNewShape = new List <int>()
{
m_nSequenceLength, 1
};
m_blobData.Reshape(rgNewShape);
m_blobClip.Reshape(rgNewShape);
m_net.Reshape();
}
}
m_mycaffe.Log.CHECK_EQ(m_blobData.count(), m_blobClip.count(), "The data and clip blobs must have the same count!");
m_rgDataInput = new T[m_nSequenceLength * m_nBatchSize];
T[] rgClipInput = new T[m_nSequenceLength * m_nBatchSize];
m_tZero = (T)Convert.ChangeType(0, typeof(T));
m_tOne = (T)Convert.ChangeType(1, typeof(T));
for (int i = 0; i < rgClipInput.Length; i++)
{
if (m_lstmType == LayerParameter.LayerType.LSTM)
{
rgClipInput[i] = (i < m_nBatchSize) ? m_tZero : m_tOne;
}
else
{
rgClipInput[i] = (i % m_nSequenceLength == 0) ? m_tZero : m_tOne;
}
}
m_blobClip.mutable_cpu_data = rgClipInput;
if (phase != Phase.RUN)
{
m_solver = mycaffe.GetInternalSolver();
m_solver.OnStart += m_solver_OnStart;
m_solver.OnTestStart += m_solver_OnTestStart;
m_solver.OnTestingIteration += m_solver_OnTestingIteration;
m_solver.OnTrainingIteration += m_solver_OnTrainingIteration;
if ((m_blobLabel = m_net.FindBlob("label")) == null)
{
throw new Exception("Could not find the 'Input' layer top named 'label'!");
}
m_rgLabelInput = new T[m_nSequenceLength * m_nBatchSize];
m_mycaffe.Log.CHECK_EQ(m_blobData.count(), m_blobLabel.count(), "The data and label blobs must have the same count!");
}
}
