/// <summary>
/// The BaseDataLayer constructor.
/// </summary>
/// <param name="cuda">Specifies the CudaDnn connection to Cuda.</param>
/// <param name="log">Specifies the Log for output.</param>
/// <param name="p">Specifies the LayerParameter</param>
/// <param name="db">Specifies the external database to use.</param>
public BaseDataLayer(CudaDnn <T> cuda, Log log, LayerParameter p, IXImageDatabase db)
: base(cuda, log, p)
{
if (db != null)
{
m_imgdb = db;
if (p.type == LayerParameter.LayerType.DATA ||
p.type == LayerParameter.LayerType.TRIPLET_DATA)
{
m_src = m_imgdb.GetSourceByName(p.data_param.source);
}
else if (p.type == LayerParameter.LayerType.BATCHDATA)
{
m_src = m_imgdb.GetSourceByName(p.batch_data_param.source);
}
if (p.transform_param.use_imagedb_mean)
{
if (db != null)
{
m_imgMean = db.GetImageMean(m_src.ID);
}
else
{
m_log.WriteLine("WARNING: The image database is NULL, and therefore no mean image can not be acquired.");
}
}
}
}
/// <summary>
/// The Cursor constructor.
/// </summary>
/// <param name="db">Specifies the underlying database.</param>
/// <param name="strSrc">Specifies the name of the data source to use.</param>
public Cursor(IXImageDatabase db, string strSrc)
{
m_db = db;
SourceDescriptor src = m_db.GetSourceByName(strSrc);
m_nSrcID = src.ID;
m_nCount = src.ImageCount;
}
/// <summary>
/// The DataLayer constructor.
/// </summary>
/// <param name="cuda">Specifies the CudaDnn connection to Cuda.</param>
/// <param name="log">Specifies the Log for output.</param>
/// <param name="p">Specifies the LayerParameter with the batchdata_param.</param>
/// <param name="db">Specifies the external database to use.</param>
/// <param name="evtCancel">Specifies the CancelEvent used to cancel any pre-fetching operations.</param>
/// <param name="fnSet">Specifies the delegate used to set the input data.</param>
public BatchDataLayer(CudaDnn <T> cuda, Log log, LayerParameter p, IXImageDatabase db, CancelEvent evtCancel, TransferInput.fnSetInputData fnSet)
: base(cuda, log, p, db)
{
log.CHECK(p.type == LayerParameter.LayerType.BATCHDATA, "The layer type should be BATCHDATA.");
m_type = LayerParameter.LayerType.BATCHDATA;
m_evtCancel = evtCancel;
m_fnSetInput = fnSet;
m_batch = new layers.Batch <T>(m_cuda, m_log);
m_internalThread = new common.InternalThread <T>();
m_internalThread.DoWork += m_internalThread_DoWork;
}
/// <summary>
/// The AnnotatedDataLayer constructor.
/// </summary>
/// <param name="cuda">Specifies the CudaDnn connection to Cuda.</param>
/// <param name="log">Specifies the Log for output.</param>
/// <param name="p">provides LayerParameter annotated_data_param.</param>
/// <param name="db">Specifies the external database to use.</param>
/// <param name="evtCancel">Specifies the CancelEvent used to cancel any pre-fetching operations.</param>
public AnnotatedDataLayer(CudaDnn <T> cuda, Log log, LayerParameter p, IXImageDatabase db, CancelEvent evtCancel)
: base(cuda, log, p, db, evtCancel)
{
m_type = LayerParameter.LayerType.ANNOTATED_DATA;
Tuple <IMGDB_LABEL_SELECTION_METHOD, IMGDB_IMAGE_SELECTION_METHOD> kvSel = db.GetSelectionMethod();
IMGDB_IMAGE_SELECTION_METHOD imgSel = kvSel.Item2;
if (m_param.data_param.enable_pair_selection.HasValue)
{
if (m_param.data_param.enable_pair_selection.Value)
{
imgSel |= IMGDB_IMAGE_SELECTION_METHOD.PAIR;
}
else
{
imgSel &= (~IMGDB_IMAGE_SELECTION_METHOD.PAIR);
}
}
if (m_param.data_param.enable_random_selection.HasValue)
{
if (m_param.data_param.enable_random_selection.Value)
{
imgSel |= IMGDB_IMAGE_SELECTION_METHOD.RANDOM;
}
else
{
imgSel &= (~IMGDB_IMAGE_SELECTION_METHOD.RANDOM);
}
}
if (!db.GetLoadImageDataCriteria())
{
m_log.WriteError(new Exception("The 'Load Image Data Criteria' must be set to TRUE in order to load the Annotation data."));
}
db.SetSelectionMethod(null, imgSel);
m_db = new data.DB(db);
m_db.Open(p.data_param.source);
m_cursor = m_db.NewCursor();
if (p.data_param.display_timing)
{
m_swTimerBatch = new Stopwatch();
m_swTimerTransaction = new Stopwatch();
}
m_sampler = new SsdSampler <T>(cuda, log);
}
/// <summary>
/// The DataLayer constructor.
/// </summary>
/// <param name="cuda">Specifies the CudaDnn connection to Cuda.</param>
/// <param name="log">Specifies the Log for output.</param>
/// <param name="p">Specifies the LayerParameter data_param</param>
/// <param name="db">Specifies the external database to use.</param>
/// <param name="evtCancel">Specifies the CancelEvent used to cancel any pre-fetching operations.</param>
public DataLayer(CudaDnn <T> cuda, Log log, LayerParameter p, IXImageDatabase db, CancelEvent evtCancel)
: base(cuda, log, p, db, evtCancel)
{
m_type = LayerParameter.LayerType.DATA;
if (p.data_param.synchronize_target)
{
m_rgBatchLabels = new LabelCollection();
}
Tuple <IMGDB_LABEL_SELECTION_METHOD, IMGDB_IMAGE_SELECTION_METHOD> kvSel = db.GetSelectionMethod();
IMGDB_IMAGE_SELECTION_METHOD imgSel = kvSel.Item2;
if (m_param.data_param.enable_pair_selection.HasValue)
{
if (m_param.data_param.enable_pair_selection.Value)
{
imgSel |= IMGDB_IMAGE_SELECTION_METHOD.PAIR;
}
else
{
imgSel &= (~IMGDB_IMAGE_SELECTION_METHOD.PAIR);
}
}
if (m_param.data_param.enable_random_selection.HasValue)
{
if (m_param.data_param.enable_random_selection.Value)
{
imgSel |= IMGDB_IMAGE_SELECTION_METHOD.RANDOM;
}
else
{
imgSel &= (~IMGDB_IMAGE_SELECTION_METHOD.RANDOM);
}
}
db.SetSelectionMethod(null, imgSel);
m_db = new data.DB(db);
m_db.Open(p.data_param.source);
m_cursor = m_db.NewCursor();
if (p.data_param.display_timing)
{
m_swTimerBatch = new Stopwatch();
m_swTimerTransaction = new Stopwatch();
}
}
/// <summary>
/// The BaseDataLayer constructor.
/// </summary>
/// <param name="cuda">Specifies the CudaDnn connection to Cuda.</param>
/// <param name="log">Specifies the Log for output.</param>
/// <param name="p">Specifies the LayerParameter</param>
/// <param name="db">Specifies the external database to use.</param>
/// <param name="evtCancel">Specifies the CancelEvent used to cancel any pre-fetching operations.</param>
public BasePrefetchingDataLayer(CudaDnn <T> cuda, Log log, LayerParameter p, IXImageDatabase db, CancelEvent evtCancel)
: base(cuda, log, p, db)
{
m_evtCancel = evtCancel;
m_rgPrefetch = new Batch <T> [p.data_param.prefetch];
m_rgPrefetchFree = new BlockingQueue <Batch <T> >(m_evtCancel);
m_rgPrefetchFull = new BlockingQueue <Batch <T> >(m_evtCancel);
m_internalThread = new InternalThread <T>();
m_internalThread.DoWork += new EventHandler <ActionStateArgs <T> >(m_internalThread_DoWork);
for (int i = 0; i < m_rgPrefetch.Length; i++)
{
m_rgPrefetch[i] = new Batch <T>(cuda, log);
m_rgPrefetchFree.Push(m_rgPrefetch[i]);
}
}
/// <summary>
/// The DB Constructor.
/// </summary>
/// <param name="imgDb">The underlying database - the CaffeImageDatabase.</param>
public DB(IXImageDatabase imgDb)
{
m_db = imgDb;
}
/// <summary>
/// The AdamSolver constructor.
/// </summary>
/// <param name="cuda">Specifies the instance of CudaDnn to use.</param>
/// <param name="log">Specifies the Log for output.</param>
/// <param name="p">Specifies teh SolverParameter.</param>
/// <param name="evtCancel">Specifies a CancelEvent used to cancel the current operation (e.g. training, testing) for which the Solver is performing.</param>
/// <param name="evtForceSnapshot">Specifies an automatic reset event that causes the Solver to perform a Snapshot when set.</param>
/// <param name="evtForceTest">Specifies an automatic reset event that causes teh Solver to run a testing cycle when set.</param>
/// <param name="imgDb">Specifies the CaffeImageDatabase.</param>
/// <param name="persist">Specifies the peristence used for loading and saving weights.</param>
/// <param name="nSolverCount">Specifies the number of Solvers participating in a multi-GPU session.</param>
/// <param name="nSolverRank">Specifies the rank of this Solver in a multi-GPU session.</param>
public AdamSolver(CudaDnn <T> cuda, Log log, SolverParameter p, CancelEvent evtCancel, AutoResetEvent evtForceSnapshot, AutoResetEvent evtForceTest, IXImageDatabase imgDb, IXPersist <T> persist, int nSolverCount = 1, int nSolverRank = 0)
: base(cuda, log, p, evtCancel, evtForceSnapshot, evtForceTest, imgDb, persist, nSolverCount, nSolverRank)
{
AdamPreSolve();
}
/// <summary>
/// Create a new Layer based on the LayerParameter.
/// </summary>
/// <param name="cuda">Specifies the CudaDnn connection to Cuda.</param>
/// <param name="log">Specifies the Log for output.</param>
/// <param name="p">Specifies the LayerParameter that contains the LayerType to create.</param>
/// <param name="evtCancel">Specifies the CancelEvent used by some Layers when created.</param>
/// <param name="imgDb">Optionally, specifies the CaffeImageDatabase used by data Layers.</param>
/// <param name="trxinput">Optionally, specifies the transfer input object used by some of the data Layers.</param>
/// <returns></returns>
public static Layer <T> Create(CudaDnn <T> cuda, Log log, LayerParameter p, CancelEvent evtCancel, IXImageDatabase imgDb = null, TransferInput trxinput = null)
{
switch (p.type)
{
case LayerParameter.LayerType.ABSVAL:
return(new AbsValLayer <T>(cuda, log, p));
case LayerParameter.LayerType.ACCURACY:
return(new AccuracyLayer <T>(cuda, log, p));
case LayerParameter.LayerType.ARGMAX:
return(new ArgMaxLayer <T>(cuda, log, p));
case LayerParameter.LayerType.BATCHDATA:
return(new BatchDataLayer <T>(cuda, log, p, imgDb, evtCancel, (trxinput != null) ? trxinput.Set : null));
case LayerParameter.LayerType.BATCHNORM:
return(new BatchNormLayer <T>(cuda, log, p));
case LayerParameter.LayerType.BATCHREINDEX:
return(new BatchReindexLayer <T>(cuda, log, p));
case LayerParameter.LayerType.BNLL:
return(new BNLLLayer <T>(cuda, log, p));
case LayerParameter.LayerType.BIAS:
return(new BiasLayer <T>(cuda, log, p));
case LayerParameter.LayerType.BINARYHASH:
return(new BinaryHashLayer <T>(cuda, log, p));
case LayerParameter.LayerType.CONCAT:
return(new ConcatLayer <T>(cuda, log, p));
case LayerParameter.LayerType.CONTRASTIVE_LOSS:
return(new ContrastiveLossLayer <T>(cuda, log, p));
case LayerParameter.LayerType.CONVOLUTION:
return(new ConvolutionLayer <T>(cuda, log, p));
case LayerParameter.LayerType.CROP:
return(new CropLayer <T>(cuda, log, p));
case LayerParameter.LayerType.DECONVOLUTION:
return(new DeconvolutionLayer <T>(cuda, log, p));
case LayerParameter.LayerType.IM2COL:
return(new Im2colLayer <T>(cuda, log, p));
case LayerParameter.LayerType.DATA:
return(new DataLayer <T>(cuda, log, p, imgDb, evtCancel));
case LayerParameter.LayerType.DEBUG:
return(new DebugLayer <T>(cuda, log, p));
case LayerParameter.LayerType.DROPOUT:
return(new DropoutLayer <T>(cuda, log, p));
case LayerParameter.LayerType.DUMMYDATA:
return(new DummyDataLayer <T>(cuda, log, p));
case LayerParameter.LayerType.ELTWISE:
return(new EltwiseLayer <T>(cuda, log, p));
case LayerParameter.LayerType.ELU:
return(new ELULayer <T>(cuda, log, p));
case LayerParameter.LayerType.EMBED:
return(new EmbedLayer <T>(cuda, log, p));
case LayerParameter.LayerType.EUCLIDEAN_LOSS:
return(new EuclideanLossLayer <T>(cuda, log, p));
case LayerParameter.LayerType.EXP:
return(new ExpLayer <T>(cuda, log, p));
case LayerParameter.LayerType.FILTER:
return(new FilterLayer <T>(cuda, log, p));
case LayerParameter.LayerType.FLATTEN:
return(new FlattenLayer <T>(cuda, log, p));
case LayerParameter.LayerType.GRN:
return(new GRNLayer <T>(cuda, log, p));
case LayerParameter.LayerType.GRADIENTSCALER:
return(new GradientScaleLayer <T>(cuda, log, p));
case LayerParameter.LayerType.HINGE_LOSS:
return(new HingeLossLayer <T>(cuda, log, p));
case LayerParameter.LayerType.INFOGAIN_LOSS:
return(new InfogainLossLayer <T>(cuda, log, p));
case LayerParameter.LayerType.INNERPRODUCT:
return(new InnerProductLayer <T>(cuda, log, p));
case LayerParameter.LayerType.KNN:
return(new KnnLayer <T>(cuda, log, p));
case LayerParameter.LayerType.LOG:
return(new LogLayer <T>(cuda, log, p));
case LayerParameter.LayerType.LABELMAPPING:
return(new LabelMappingLayer <T>(cuda, log, p, imgDb));
case LayerParameter.LayerType.LRN:
return(new LRNLayer <T>(cuda, log, p));
case LayerParameter.LayerType.MEMORYDATA:
return(new MemoryDataLayer <T>(cuda, log, p));
case LayerParameter.LayerType.MULTINOMIALLOGISTIC_LOSS:
return(new MultinomialLogisticLossLayer <T>(cuda, log, p));
case LayerParameter.LayerType.MVN:
return(new MVNLayer <T>(cuda, log, p));
case LayerParameter.LayerType.NORMALIZATION:
return(new NormalizationLayer <T>(cuda, log, p));
case LayerParameter.LayerType.POOLING:
return(new PoolingLayer <T>(cuda, log, p));
case LayerParameter.LayerType.UNPOOLING1:
return(new UnPoolingLayer1 <T>(cuda, log, p));
case LayerParameter.LayerType.UNPOOLING2:
return(new UnPoolingLayer2 <T>(cuda, log, p));
case LayerParameter.LayerType.POWER:
return(new PowerLayer <T>(cuda, log, p));
case LayerParameter.LayerType.PRELU:
return(new PReLULayer <T>(cuda, log, p));
case LayerParameter.LayerType.REDUCTION:
return(new ReductionLayer <T>(cuda, log, p));
case LayerParameter.LayerType.REINFORCEMENT_LOSS:
return(new ReinforcementLossLayer <T>(cuda, log, p, (trxinput != null) ? trxinput.Get : null));
case LayerParameter.LayerType.RELU:
return(new ReLULayer <T>(cuda, log, p));
case LayerParameter.LayerType.RESHAPE:
return(new ReshapeLayer <T>(cuda, log, p));
case LayerParameter.LayerType.SCALE:
return(new ScaleLayer <T>(cuda, log, p));
case LayerParameter.LayerType.SIGMOID:
return(new SigmoidLayer <T>(cuda, log, p));
case LayerParameter.LayerType.SIGMOIDCROSSENTROPY_LOSS:
return(new SigmoidCrossEntropyLossLayer <T>(cuda, log, p));
case LayerParameter.LayerType.SILENCE:
return(new SilenceLayer <T>(cuda, log, p));
case LayerParameter.LayerType.SLICE:
return(new SliceLayer <T>(cuda, log, p));
case LayerParameter.LayerType.SOFTMAX:
return(new SoftmaxLayer <T>(cuda, log, p));
case LayerParameter.LayerType.SOFTMAXWITH_LOSS:
return(new SoftmaxLossLayer <T>(cuda, log, p));
case LayerParameter.LayerType.SPLIT:
return(new SplitLayer <T>(cuda, log, p));
case LayerParameter.LayerType.SPP:
return(new SPPLayer <T>(cuda, log, p));
case LayerParameter.LayerType.SWISH:
return(new SwishLayer <T>(cuda, log, p));
case LayerParameter.LayerType.TANH:
return(new TanhLayer <T>(cuda, log, p));
case LayerParameter.LayerType.THRESHOLD:
return(new ThresholdLayer <T>(cuda, log, p));
case LayerParameter.LayerType.TRIPLET_LOSS_SIMPLE:
return(new TripletLossSimpleLayer <T>(cuda, log, p));
case LayerParameter.LayerType.TRIPLET_LOSS:
return(new TripletLossLayer <T>(cuda, log, p));
case LayerParameter.LayerType.TRIPLET_SELECT:
return(new TripletSelectLayer <T>(cuda, log, p));
case LayerParameter.LayerType.TRIPLET_DATA:
return(new TripletDataLayer <T>(cuda, log, p, imgDb, evtCancel));
case LayerParameter.LayerType.TILE:
return(new TileLayer <T>(cuda, log, p));
case LayerParameter.LayerType.LSTM_SIMPLE:
return(new LSTMSimpleLayer <T>(cuda, log, p));
case LayerParameter.LayerType.RNN:
return(new RNNLayer <T>(cuda, log, p, evtCancel));
case LayerParameter.LayerType.LSTM:
return(new LSTMLayer <T>(cuda, log, p, evtCancel));
case LayerParameter.LayerType.LSTM_UNIT:
return(new LSTMUnitLayer <T>(cuda, log, p));
case LayerParameter.LayerType.INPUT:
return(new InputLayer <T>(cuda, log, p));
default:
log.FAIL("Unknown layer type: " + p.type.ToString());
break;
}
throw new NotImplementedException("The layer type: " + p.type.ToString() + " is not implemented yet.");
}
/// <summary>
/// The SGDSolver constructor.
/// </summary>
/// <param name="cuda">Specifies the instance of CudaDnn to use.</param>
/// <param name="log">Specifies the Log for output.</param>
/// <param name="p">Specifies teh SolverParameter.</param>
/// <param name="evtCancel">Specifies a CancelEvent used to cancel the current operation (e.g. training, testing) for which the Solver is performing.</param>
/// <param name="evtForceSnapshot">Specifies an automatic reset event that causes the Solver to perform a Snapshot when set.</param>
/// <param name="evtForceTest">Specifies an automatic reset event that causes teh Solver to run a testing cycle when set.</param>
/// <param name="imgDb">Specifies the CaffeImageDatabase.</param>
/// <param name="persist">Specifies the peristence used for loading and saving weights.</param>
/// <param name="nSolverCount">Specifies the number of Solvers participating in a multi-GPU session.</param>
/// <param name="nSolverRank">Specifies the rank of this Solver in a multi-GPU session.</param>
/// <param name="shareNet">Optionally, specifies the net to share when creating the training network (default = null, meaning no share net is used).</param>
/// <param name="getws">Optionally, specifies the handler for getting the workspace.</param>
/// <param name="setws">Optionally, specifies the handler for setting the workspace.</param>
public AdaDeltaSolver(CudaDnn <T> cuda, Log log, SolverParameter p, CancelEvent evtCancel, AutoResetEvent evtForceSnapshot, AutoResetEvent evtForceTest, IXImageDatabase imgDb, IXPersist <T> persist, int nSolverCount = 1, int nSolverRank = 0, Net <T> shareNet = null, onGetWorkspace getws = null, onSetWorkspace setws = null)
: base(cuda, log, p, evtCancel, evtForceSnapshot, evtForceTest, imgDb, persist, nSolverCount, nSolverRank, shareNet, getws, setws)
{
AdaDeltaPreSolve();
}
/// <summary>
/// The NesterovSolver constructor.
/// </summary>
/// <param name="cuda">Specifies the instance of CudaDnn to use.</param>
/// <param name="log">Specifies the Log for output.</param>
/// <param name="p">Specifies teh SolverParameter.</param>
/// <param name="evtCancel">Specifies a CancelEvent used to cancel the current operation (e.g. training, testing) for which the Solver is performing.</param>
/// <param name="evtForceSnapshot">Specifies an automatic reset event that causes the Solver to perform a Snapshot when set.</param>
/// <param name="evtForceTest">Specifies an automatic reset event that causes teh Solver to run a testing cycle when set.</param>
/// <param name="imgDb">Specifies the CaffeImageDatabase.</param>
/// <param name="persist">Specifies the peristence used for loading and saving weights.</param>
/// <param name="nSolverCount">Specifies the number of Solvers participating in a multi-GPU session.</param>
/// <param name="nSolverRank">Specifies the rank of this Solver in a multi-GPU session.</param>
public AdaGradSolver(CudaDnn <T> cuda, Log log, SolverParameter p, CancelEvent evtCancel, AutoResetEvent evtForceSnapshot, AutoResetEvent evtForceTest, IXImageDatabase imgDb, IXPersist <T> persist, int nSolverCount = 1, int nSolverRank = 0)
: base(cuda, log, p, evtCancel, evtForceSnapshot, evtForceTest, imgDb, persist, nSolverCount, nSolverRank)
{
m_log.CHECK_EQ(0, m_param.momentum, "Momentum cannot be used with AdaGrad.");
}
/// <summary>
/// The InnerProductLayer constructor.
/// </summary>
/// <param name="cuda">Specifies the CudaDnn connection to Cuda.</param>
/// <param name="log">Specifies the Log for output.</param>
/// <param name="p">provides LayerParameter labelmapping_param, with options:
/// - mapping. Defines the mappings from original label to new label.
///
/// - update_database (/b optional, default = false). Whether or not to update the database with the new mapping, otherwise mappings are used online.
///
/// - reset_database_labels (/b optional, default = false). Whether or not to reset the database to its original labels or not. This setting requires 'update_database' = true.
///
/// - label_boosts (/b optional, default = ""). A string that defines which labels to boost, giving them a higher probability of being selected.
/// </param>
/// <param name="db">Specifies the CaffeImageDatabase.</param>
public LabelMappingLayer(CudaDnn <T> cuda, Log log, LayerParameter p, IXImageDatabase db)
: base(cuda, log, p)
{
m_db = db;
m_type = LayerParameter.LayerType.LABELMAPPING;
}
/// <summary>
/// The RmsPropSolver constructor.
/// </summary>
/// <param name="cuda">Specifies the instance of CudaDnn to use.</param>
/// <param name="log">Specifies the Log for output.</param>
/// <param name="p">Specifies teh SolverParameter.</param>
/// <param name="evtCancel">Specifies a CancelEvent used to cancel the current operation (e.g. training, testing) for which the Solver is performing.</param>
/// <param name="evtForceSnapshot">Specifies an automatic reset event that causes the Solver to perform a Snapshot when set.</param>
/// <param name="evtForceTest">Specifies an automatic reset event that causes teh Solver to run a testing cycle when set.</param>
/// <param name="imgDb">Specifies the CaffeImageDatabase.</param>
/// <param name="persist">Specifies the peristence used for loading and saving weights.</param>
/// <param name="nSolverCount">Specifies the number of Solvers participating in a multi-GPU session.</param>
/// <param name="nSolverRank">Specifies the rank of this Solver in a multi-GPU session.</param>
public RmsPropSolver(CudaDnn <T> cuda, Log log, SolverParameter p, CancelEvent evtCancel, AutoResetEvent evtForceSnapshot, AutoResetEvent evtForceTest, IXImageDatabase imgDb, IXPersist <T> persist, int nSolverCount = 1, int nSolverRank = 0)
: base(cuda, log, p, evtCancel, evtForceSnapshot, evtForceTest, imgDb, persist, nSolverCount, nSolverRank)
{
m_log.CHECK_EQ(0, m_param.momentum, "Momentum cannot be used with RmsProp.");
m_log.CHECK_GE(m_param.rms_decay, 0, "rms_decay should lie between 0 and 1.");
m_log.CHECK_LT(m_param.rms_decay, 1, "rms_decay should lie between 0 and 1.");
}
/// <summary>
/// The LBFGSSolver constructor.
/// </summary>
/// <param name="cuda">Specifies the instance of CudaDnn to use.</param>
/// <param name="log">Specifies the Log for output.</param>
/// <param name="p">Specifies teh SolverParameter.</param>
/// <param name="evtCancel">Specifies a CancelEvent used to cancel the current operation (e.g. training, testing) for which the Solver is performing.</param>
/// <param name="evtForceSnapshot">Specifies an automatic reset event that causes the Solver to perform a Snapshot when set.</param>
/// <param name="evtForceTest">Specifies an automatic reset event that causes teh Solver to run a testing cycle when set.</param>
/// <param name="imgDb">Specifies the CaffeImageDatabase.</param>
/// <param name="persist">Specifies the peristence used for loading and saving weights.</param>
/// <param name="nSolverCount">Specifies the number of Solvers participating in a multi-GPU session.</param>
/// <param name="nSolverRank">Specifies the rank of this Solver in a multi-GPU session.</param>
public LBFGSSolver(CudaDnn <T> cuda, Log log, SolverParameter p, CancelEvent evtCancel, AutoResetEvent evtForceSnapshot, AutoResetEvent evtForceTest, IXImageDatabase imgDb, IXPersist <T> persist, int nSolverCount = 1, int nSolverRank = 0)
: base(cuda, log, p, evtCancel, evtForceSnapshot, evtForceTest, imgDb, persist, nSolverCount, nSolverRank)
{
m_tZero = (T)Convert.ChangeType(0, typeof(T));
m_tOne = (T)Convert.ChangeType(1, typeof(T));
m_tMinusOne = (T)Convert.ChangeType(-1, typeof(T));
PreSolve();
}
/// <summary>
/// The NesterovSolver constructor.
/// </summary>
/// <param name="cuda">Specifies the instance of CudaDnn to use.</param>
/// <param name="log">Specifies the Log for output.</param>
/// <param name="p">Specifies teh SolverParameter.</param>
/// <param name="evtCancel">Specifies a CancelEvent used to cancel the current operation (e.g. training, testing) for which the Solver is performing.</param>
/// <param name="evtForceSnapshot">Specifies an automatic reset event that causes the Solver to perform a Snapshot when set.</param>
/// <param name="evtForceTest">Specifies an automatic reset event that causes teh Solver to run a testing cycle when set.</param>
/// <param name="imgDb">Specifies the CaffeImageDatabase.</param>
/// <param name="persist">Specifies the peristence used for loading and saving weights.</param>
/// <param name="nSolverCount">Specifies the number of Solvers participating in a multi-GPU session.</param>
/// <param name="nSolverRank">Specifies the rank of this Solver in a multi-GPU session.</param>
/// <param name="shareNet">Optionally, specifies the net to share when creating the training network (default = null, meaning no share net is used).</param>
public NesterovSolver(CudaDnn <T> cuda, Log log, SolverParameter p, CancelEvent evtCancel, AutoResetEvent evtForceSnapshot, AutoResetEvent evtForceTest, IXImageDatabase imgDb, IXPersist <T> persist, int nSolverCount = 1, int nSolverRank = 0, Net <T> shareNet = null)
: base(cuda, log, p, evtCancel, evtForceSnapshot, evtForceTest, imgDb, persist, nSolverCount, nSolverRank, shareNet)
{
}
/// <summary>
/// The TripletDataLayer constructor.
/// </summary>
/// <param name="cuda">Specifies the CudaDnn connection to Cuda.</param>
/// <param name="log">Specifies the Log for output.</param>
/// <param name="p">Specifies the LayerParameter of type TRIPLET_DATA with parameter triplet_data_param.
/// <param name="db">Specifies the image database.</param>
/// <param name="evtCancel">Specifies the cancel event to cancel data loading operations.</param>
/// </param>
public TripletDataLayer(CudaDnn <T> cuda, Log log, LayerParameter p, IXImageDatabase db, CancelEvent evtCancel)
: base(cuda, log, p, db, evtCancel)
{
log.CHECK(p.type == LayerParameter.LayerType.TRIPLET_DATA, "The layer type should be TRIPLET_DATA.");
m_type = LayerParameter.LayerType.TRIPLET_DATA;
}
/// <summary>
/// The NesterovSolver constructor.
/// </summary>
/// <param name="cuda">Specifies the instance of CudaDnn to use.</param>
/// <param name="log">Specifies the Log for output.</param>
/// <param name="p">Specifies teh SolverParameter.</param>
/// <param name="evtCancel">Specifies a CancelEvent used to cancel the current operation (e.g. training, testing) for which the Solver is performing.</param>
/// <param name="evtForceSnapshot">Specifies an automatic reset event that causes the Solver to perform a Snapshot when set.</param>
/// <param name="evtForceTest">Specifies an automatic reset event that causes teh Solver to run a testing cycle when set.</param>
/// <param name="imgDb">Specifies the CaffeImageDatabase.</param>
/// <param name="persist">Specifies the peristence used for loading and saving weights.</param>
/// <param name="nSolverCount">Specifies the number of Solvers participating in a multi-GPU session.</param>
/// <param name="nSolverRank">Specifies the rank of this Solver in a multi-GPU session.</param>
/// <param name="shareNet">Optionally, specifies the net to share when creating the training network (default = null, meaning no share net is used).</param>
/// <param name="getws">Optionally, specifies the handler for getting the workspace.</param>
/// <param name="setws">Optionally, specifies the handler for setting the workspace.</param>
public AdaGradSolver(CudaDnn <T> cuda, Log log, SolverParameter p, CancelEvent evtCancel, AutoResetEvent evtForceSnapshot, AutoResetEvent evtForceTest, IXImageDatabase imgDb, IXPersist <T> persist, int nSolverCount = 1, int nSolverRank = 0, Net <T> shareNet = null, onGetWorkspace getws = null, onSetWorkspace setws = null)
: base(cuda, log, p, evtCancel, evtForceSnapshot, evtForceTest, imgDb, persist, nSolverCount, nSolverRank, shareNet, getws, setws)
{
m_log.CHECK_EQ(0, m_param.momentum, "Momentum cannot be used with AdaGrad.");
}
