MyCaffe  1.11.8.27
Deep learning software for Windows C# programmers.
MyCaffe.common Namespace Reference

The MyCaffe.common namespace contains common MyCaffe classes. More...

Classes

class  ActionStateArgs
 The ActionStateArgs are sent to the DoWork event when fired from the InternalThreadEntry. More...
 
class  BackwardArgs
 The BackwardArgs are passed to the OnBackward event of the EventLayer. More...
 
class  BatchInput
 The BatchInput class stores the mini-batch index and input data. More...
 
class  BBoxUtility
 The BBox class processes the NormalizedBBox data used with SSD. More...
 
class  BeamSearch
 The BeamSearch uses the softmax output from the network and continually runs the net on each output (using the output as input) until the end of token is reached. The beam-search tree is returned. More...
 
class  Blob
 The Blob is the main holder of data that moves through the Layers of the Net. More...
 
class  BlobCollection
 The BlobCollection contains a list of Blobs. More...
 
class  BlobDebugInformation
 The BlobDebugInformation describes debug information relating to a given Blob in a given Layer. More...
 
class  BlobName
 The BlobName class is used to build unique blob names. More...
 
class  BlockingQueue
 The BlockingQueue is used for synchronized Queue operations. More...
 
class  Cache
 The Cache class is used to cache blobs over time. More...
 
class  ComputeGraph
 The ComputeGraph class provides a simple computation graph of operations used in a forward pass that are stored in an array on each call and then unwound with calls that calculate the gradients on the backward pass. More...
 
class  CudaDnn
 The CudaDnn object is the main interface to the Low-Level Cuda C++ DLL. More...
 
class  CudaDnnMemoryTracker
 The CudaDnnMemoryTracker is used for diagnostics in that it helps estimate the amount of memory that a Net will use. More...
 
class  CustomForwardBackArgs
 The CustomForwardBackArgs provide the arguments to the OnCustomForwardBack event within the Solver Step function. More...
 
class  DebugInformation
 The DebugInformation contains information used to help debug the Layers of a Net while it is training. More...
 
class  ForwardArgs
 The ForwardArgs are passed to the OnForward event of the EventLayer. More...
 
class  GetBytesArgs
 The GetBytesArgs is passed along to the SnapshotArgs::OnGetWeights and SnapshotArgs::OnGetState events. More...
 
class  GetConversionBlobArgs
 The GetConversionBlobArgs are passed to the Layer::OnGetConversionBlobs event which fires each time a blob needs to be converted form half to base or back. More...
 
class  GetIterationArgs
 The GetIterationArgs is sent bubbled up to the solver when a layer needs to know the curret training iteration. More...
 
class  GetWorkBlobArgs
 The GetWorkBlobArgs are passed to the Layer::OnGetWorkBlob event which is supported for debugging only. More...
 
class  GPUParams
 The GPUParams contains the connection to the low-level Cuda, and the stream associated with this instance. More...
 
class  GradientsReadyArgs
 The GradientsReadyArgs is sent to the Solver::OnGradientsReady event which fires at the end of each Solver::Step. More...
 
class  HostBuffer
 The HostBuffer helps manage host memory, often used when implementing CPU versions of a function or layer. More...
 
class  InternalThread
 The InternalThread manages an internal thread used for Parallel and data collection operations. More...
 
interface  IXDebugData
 The IXDebugData interface is implemented by the DebugLayer to give access to the debug information managed by the layer. More...
 
interface  IXMyCaffe
 The IXMyCaffe interface contains functions used to perform MyCaffe operations that work with the MyCaffeImageDatabase. More...
 
interface  IXMyCaffeExtension
 The IXMyCaffeExtension interface allows for easy extension management of the low-level software that interacts directly with CUDA. More...
 
interface  IXMyCaffeNoDb
 The IXMyCaffeNoDb interface contains functions used to perform MyCaffe operations that run in a light-weight manner without the MyCaffeImageDatabase. More...
 
interface  IXMyCaffeState
 The IXMyCaffeState interface contains functions related to the MyCaffeComponent state. More...
 
interface  IXPersist
 The IXPersist interface is used by the CaffeControl to load and save weights. More...
 
class  LayerDebugInformation
 The LayerDebugInformation describes debug information relating to a given Layer in the Net. More...
 
class  NCCL
 The NCCL class manages the multi-GPU operations using the low-level NCCL functionality provided by the low-level Cuda Dnn DLL. More...
 
class  Net
 Connects Layer's together into a direct acrylic graph (DAG) specified by a NetParameter More...
 
class  Params
 The Params contains the base parameters used in multi-GPU training. More...
 
class  PersistCaffe
 The PersistCaffe class is used to load and save weight files in the .caffemodel format. More...
 
class  Property
 The Property class stores both a numeric and text value. More...
 
class  PropertyTree
 The PropertyTree class implements a simple property tree similar to the ptree in Boost. More...
 
class  ResultCollection
 The ResultCollection contains the result of a given CaffeControl::Run. More...
 
class  SnapshotArgs
 The SnapshotArgs is sent to the Solver::OnSnapshot event which fires each time the Solver::Snapshot method is called. More...
 
class  SolverInfo
 The SolverInfo defines the user supplied arguments passed to each Worker. More...
 
class  SsdSampler
 The SsdSampler is used by the SSD algorithm to sample BBoxes. More...
 
class  SyncedMemory
 The SyncedMemory manages the low-level connection between the GPU and host memory. More...
 
class  TestArgs
 The TestArgs are passed to the Solver::OnTest event. More...
 
class  TestingIterationArgs
 Specifies the TestingIterationArgs sent to the Solver::OnTestingIteration, which is called at the end of a testing cycle. More...
 
class  TestResultArgs
 The TestResultArgs are passed to the Solver::OnTestResults event. More...
 
class  TrainingIterationArgs
 The TrainingIterationArgs is sent to the Solver::OnTrainingIteration event that fires at the end of a training cycle. More...
 
class  TransferInput
 The TransferInput class is used to transfer get and set input data.
More...
 
class  WeightInfo
 The WeightInfo class describes the weights of a given weight set including the blob names and sizes of the weights. More...
 
class  Worker
 The Worker manages each 'non' root sover running, where each Worker operates on a different GPU. More...
 
class  WorkspaceArgs
 The WorkspaceArgs are passed to both the Layer::OnSetWorkspace and Layer::OnGetWorkspace events. More...
 

Enumerations

enum  DIR { FWD = 0 , BWD = 1 }
 Defines the direction of data flow. More...
 
enum  MEAN_ERROR { MSE = 1 , MAE = 2 }
 Defines the type of Mean Error to use. More...
 
enum  MATH_FUNCTION {
  NOP = 0 , ACOS = 1 , ACOSH = 2 , COS = 3 ,
  COSH = 4 , ASIN = 10 , ASINH = 11 , SIN = 12 ,
  SINH = 13 , ATAN = 20 , ATANH = 21 , TAN = 22 ,
  TANH = 23 , CEIL = 30 , FLOOR = 31 , NEG = 32 ,
  SIGN = 33 , SQRT = 34
}
 Defines the mathematical function to run. More...
 
enum  DistanceMethod { HAMMING = 0 , EUCLIDEAN = 1 }
 Specifies the distance method used when calculating batch distances. More...
 
enum  PoolingMethod { MAX = 0 , AVE = 1 }
 Specifies the pooling method used by the cuDnn function SetPoolingDesc. More...
 
enum  DataType { DOUBLE , FLOAT }
 Specifies the base datatype corresponding the the template type 'T'. Currently, only More...
 
enum  DEVINIT { NONE = 0x0000 , CUBLAS = 0x0001 , CURAND = 0x0002 , SETSEED = 0x0004 }
 Specifies the initialization flags used when initializing CUDA. More...
 
enum  BATCHNORM_MODE { PER_ACTIVATION = 0 , SPATIAL = 1 , SPATIAL_PERSISTENT = 2 }
 Specifies the cuDnn batch norm mode to use. More...
 
enum  CONV_FWD_ALGO {
  NONE = -1 , IMPLICIT_GEMM = 0 , IMPLICIT_PRECOMP_GEMM = 1 , ALGO_GEMM = 2 ,
  ALGO_DIRECT = 3 , ALGO_FFT = 4 , ALGO_FFT_TILING = 5 , ALGO_WINOGRAD = 6 ,
  ALGO_WINOGRAD_NONFUSED = 7
}
 Specifies the cuDnn convolution forward algorithm to use. More...
 
enum  CONV_BWD_FILTER_ALGO { ALGO_0 = 0 , ALGO_1 = 1 , ALGO_FFT = 2 , ALGO_3 = 3 }
 Specifies the cuDnn convolution backward filter algorithm to use. More...
 
enum  CONV_BWD_DATA_ALGO { ALGO_0 = 0 , ALGO_1 = 1 , ALGO_FFT = 2 }
 Specifies the cuDnn convolution backward data algorithm to use. More...
 
enum  POOLING_METHOD { MAX = 0 , AVE = 1 , STO_TRAIN = 2 , STO_TEST = 3 }
 Specifies the pooling method to use when using the Caffe pooling (instead of the pooling from NVIDIA's cuDnn). More...
 
enum  RNN_MODE { RNN_RELU = 0 , RNN_TANH = 1 , LSTM = 2 , GRU = 3 }
 Specifies the RNN mode to use with the Recurrent Layer when using the cuDNN engine. More...
 
enum  RNN_DATALAYOUT { RNN_SEQ_MAJOR = 0 , RNN_BATCH_MAJOR = 2 }
 Specifies the RNN data layout of the data input. More...
 
enum  RNN_DIRECTION { RNN_UNIDIRECTIONAL , RNN_BIDIRECTIONAL }
 Specifies the RNN directional used. More...
 
enum  DEVPROP { DEVICECOUNT = 1 , NAME = 2 , MULTIGPUBOARDGROUPID = 3 }
 Specifies certain device properties to query from Cuda. More...
 
enum  MEMTEST_TYPE { MOV_INV_8 = 1 }
 Specifies the memory test to perform. More...
 
enum  NCCL_REDUCTION_OP { SUM = 0 , PROD = 1 , MAX = 2 , MIN = 3 }
 Specifies the reduction operation to use with 'Nickel' NCCL. More...
 
enum  SSD_MINING_TYPE { NONE = 0 , MAX_NEGATIVE = 1 , HARD_EXAMPLE = 2 }
 Defines the mining type used during SSD cuda training. More...
 
enum  SSD_MATCH_TYPE { BIPARTITE , PER_PREDICTION }
 Defines the matching method used during SSD cuda training. More...
 
enum  SSD_CODE_TYPE { CORNER = 1 , CENTER_SIZE = 2 , CORNER_SIZE = 3 }
 Defines the encode/decode type used during SSD cuda training. More...
 
enum  SSD_CONF_LOSS_TYPE { SOFTMAX , LOGISTIC }
 Defines the confidence loss types used during SSD cuda training. More...
 
enum  SSD_LOC_LOSS_TYPE { L2 , SMOOTH_L1 }
 Defines the location loss types used during SSD cuda training. More...
 
enum  ORIENTATION { COL = 0 , ROW = 1 }
 Specifies the orientation of a matrix. More...
 
enum  TRANSPOSE_OPERATION { ADD = 0 , MUL = 1 , DIV = 2 }
 Specifies the type of operation to perform along with a matrix transposition. More...
 
enum  AGGREGATIONS { SUM = 0 , MAX = 1 , MIN = 2 }
 Specifies different aggregation operations. More...
 
enum  WEIGHT_TARGET { NONE , WEIGHTS , BIAS , BOTH }
 Defines the type of weight to target in re-initializations. More...
 
enum  BLOB_TYPE {
  UNKNOWN , DATA , IP_WEIGHT , WEIGHT ,
  LOSS , ACCURACY , CLIP , MULTIBBOX ,
  INTERNAL
}
 Defines the tpe of data held by a given Blob. More...
 
enum  TRAIN_STEP { NONE = 0x0000 , FORWARD = 0x0001 , BACKWARD = 0x0002 , BOTH = 0x0003 }
 Defines the training stepping method (if any). More...
 

Functions

delegate void onSetWorkspace (object sender, WorkspaceArgs e)
 Delegate used to set the OnSetworkspace event. More...
 
delegate void onGetWorkspace (object sender, WorkspaceArgs e)
 Delegate used to set the OnGetworkspace event. More...
 

Detailed Description

The MyCaffe.common namespace contains common MyCaffe classes.

Enumeration Type Documentation

◆ AGGREGATIONS

Specifies different aggregation operations.

Enumerator
SUM 

Sum the values.

MAX 

Return the maximum value.

MIN 

Return the minimum value.

Definition at line 608 of file CudaDnn.cs.

◆ BATCHNORM_MODE

Specifies the cuDnn batch norm mode to use.

See also
NVIDIA cuDnn documenation for more details.
Enumerator
PER_ACTIVATION 

Specifies to use the per-activation batch normalization mode.

SPATIAL 

Specifies to use the spatial batch normalization mode.

SPATIAL_PERSISTENT 

Specifies to use the spatial persistent batch normalization mode.

Definition at line 209 of file CudaDnn.cs.

◆ BLOB_TYPE

Defines the tpe of data held by a given Blob.

Enumerator
UNKNOWN 

The blob is an unknown type.

DATA 

The Blob holds Data.

IP_WEIGHT 

The Blob holds an inner product weight.

WEIGHT 

The Blob holds a general weight.

LOSS 

The Blob holds Loss Data.

ACCURACY 

The Blob holds Accuracy Data.

CLIP 

The blob holds Clip data.

MULTIBBOX 

The blob holds multi-boundingbox data.

The multi-box data ordering is as follows: [0] index of num. [1] label [2] score [3] bbox xmin [4] bbox ymin [5] bbox xmax [6] bbox ymax

continues for each of the top 'n' bboxes output.

INTERNAL 

The blob is an internal blob used within the layer.

Definition at line 61 of file Interfaces.cs.

◆ CONV_BWD_DATA_ALGO

Specifies the cuDnn convolution backward data algorithm to use.

See also
NVIDIA cuDnn documenation for more details.
Enumerator
ALGO_0 

Specifies to use algorithm 0 - which is non-deterministic.

ALGO_1 

Specifies to use algorithm 1.

ALGO_FFT 

Specifies to use the fft algorithm.

Definition at line 303 of file CudaDnn.cs.

◆ CONV_BWD_FILTER_ALGO

Specifies the cuDnn convolution backward filter algorithm to use.

See also
NVIDIA cuDnn documenation for more details.
Enumerator
ALGO_0 

Specifies to use algorithm 0 - which is non-deterministic.

ALGO_1 

Specifies to use algorithm 1.

ALGO_FFT 

Specifies to use the fft algorithm.

ALGO_3 

Specifies to use algorithm 0 with a workspace - which is non-deterministic.

Definition at line 277 of file CudaDnn.cs.

◆ CONV_FWD_ALGO

Specifies the cuDnn convolution forward algorithm to use.

See also
NVIDIA cuDnn documenation for more details.
Enumerator
NONE 

Specifies to not use a forward algorithm.

IMPLICIT_GEMM 

Specifies to use the implicit gemm algorithm.

IMPLICIT_PRECOMP_GEMM 

Specifies to use the implicit pre-computation gemm algorithm.

ALGO_GEMM 

Specifies to use the gemm algorithm.

ALGO_DIRECT 

Specifies to use the direct algorithm.

ALGO_FFT 

Specifies to use the fft algorithm.

ALGO_FFT_TILING 

Specifies to use the fft tiling algorithm.

ALGO_WINOGRAD 

Specifies to use the winograd algorithm.

ALGO_WINOGRAD_NONFUSED 

Specifies to use the non-fused winograd algorithm.

Definition at line 231 of file CudaDnn.cs.

◆ DataType

Specifies the base datatype corresponding the the template type 'T'. Currently, only

double and float types are supported.

Enumerator
DOUBLE 

Specifies the double type.

FLOAT 

Specifies the single type.

Definition at line 164 of file CudaDnn.cs.

◆ DEVINIT

Specifies the initialization flags used when initializing CUDA.

Enumerator
NONE 

No flag specified.

CUBLAS 

Initialize cuBlas. This should be initialized for cuBlas is used for many of the math operations.

CURAND 

Initialize cuRand. This should be initialized for cuRand is used for most of the random operations.

SETSEED 

Set the cuRand random number generator seed - typically only used when testing to ensure that random numbers are generated in a predictable ordering.

Definition at line 179 of file CudaDnn.cs.

◆ DEVPROP

Specifies certain device properties to query from Cuda.

Enumerator
DEVICECOUNT 

Query the number of devices (gpu's) installed.

NAME 

Query the name of a given GPU.

MULTIGPUBOARDGROUPID 

Query a GPU board group ID.

Definition at line 403 of file CudaDnn.cs.

◆ DIR

Defines the direction of data flow.

Enumerator
FWD 

Specifies data is moving forward.

BWD 

Specifies data is moving backward.

Definition at line 18 of file CudaDnn.cs.

◆ DistanceMethod

Specifies the distance method used when calculating batch distances.

Enumerator
HAMMING 

Specifies to calculate the hamming distance.

EUCLIDEAN 

Specifies to calculate the euclidean distance.

Definition at line 131 of file CudaDnn.cs.

◆ MATH_FUNCTION

Defines the mathematical function to run.

Enumerator
NOP 

Specifies to run a no operation.

ACOS 

Specifies to run the acos function.

ACOSH 

Specifies to run the acosh function.

COS 

Specifies to run the cos function.

COSH 

Specifies to run the cosh function.

ASIN 

Specifies to run the asin function.

ASINH 

Specifies to run the asinh function.

SIN 

Specifies to run the sin function.

SINH 

Specifies to run the sinh function.

ATAN 

Specifies to run the atan function.

ATANH 

Specifies to run the atanh function.

TAN 

Specifies to run the tan function.

TANH 

Specifies to run the tanh function.

CEIL 

Specifies to run the ceil function.

FLOOR 

Specifies to run the floor function.

NEG 

Specifies to flip the sign of the inputs.

SIGN 

Specifies to run the sign function.

SQRT 

Specifies to run the sqrt function.

Definition at line 48 of file CudaDnn.cs.

◆ MEAN_ERROR

Defines the type of Mean Error to use.

Enumerator
MSE 

Mean Squared Error (MSE) $ L(y, y\hat) = \frac{1}{N} \sum_{i=0}^{N} (y - \hat{y}{i})^2 $ where $ \hat{y} $ is the predicted value.

MAE 

Mean Absolute Error (MAE) $ L(y, y\hat) = \frac{1}{N} \sum_{i=0}^{N} |y - \hat{y}{i}| $ where $ \hat{y} $ is the predicted value.

Definition at line 33 of file CudaDnn.cs.

◆ MEMTEST_TYPE

Specifies the memory test to perform.

See also
CudaDnn::RunMemoryTest
Enumerator
MOV_INV_8 

Specifies the mov-inv-8 test.

Definition at line 425 of file CudaDnn.cs.

◆ NCCL_REDUCTION_OP

Specifies the reduction operation to use with 'Nickel' NCCL.

See also
CudaDnn::NcclAllReduce
Enumerator
SUM 

Sum the values.

PROD 

Multiply the values.

MAX 

Return the maximum value.

MIN 

Return the minimum value.

Definition at line 439 of file CudaDnn.cs.

◆ ORIENTATION

Specifies the orientation of a matrix.

See also
CudaDnn::matrix_add_vector
Enumerator
COL 

Specifies to add the vector to each column.

ROW 

Specifies to add the vector to each row.

Definition at line 571 of file CudaDnn.cs.

◆ POOLING_METHOD

Specifies the pooling method to use when using the Caffe pooling (instead of the pooling from NVIDIA's cuDnn).

See also
CudaDnn::pooling_fwd
Enumerator
MAX 

Select the maximum value from the kernel.

AVE 

Select the average of the values in the kernel.

STO_TRAIN 

Select the stochastic value in the kernel - used during a training pass.

STO_TEST 

Select the stochastic value in the kernel - used during a testing pass.

Definition at line 325 of file CudaDnn.cs.

◆ PoolingMethod

Specifies the pooling method used by the cuDnn function SetPoolingDesc.

See also
CudaDnn::SetPoolingDesc
Enumerator
MAX 

Specifies to use

CUDNN_POOLING_MAX in CUDA C++ code.

AVE 

Specifies to use

CUDNN_POOLING_AVERAGE_COUNT_INCLUDE_PADDING in CUDA C++ code.

Definition at line 149 of file CudaDnn.cs.

◆ RNN_DATALAYOUT

Specifies the RNN data layout of the data input.

Enumerator
RNN_SEQ_MAJOR 

Specifies ordering with sequence major ordering.

RNN_BATCH_MAJOR 

Specifies ordering with batch major ordering.

Definition at line 373 of file CudaDnn.cs.

◆ RNN_DIRECTION

Specifies the RNN directional used.

Enumerator
RNN_UNIDIRECTIONAL 

Specifies a single direction RNN (default)

RNN_BIDIRECTIONAL 

Specifies a bi-direction RNN where the output is concatinated at each layer.

Definition at line 388 of file CudaDnn.cs.

◆ RNN_MODE

Specifies the RNN mode to use with the Recurrent Layer when using the cuDNN engine.

Enumerator
RNN_RELU 

Specifies to use a single RelU gate Recurrent Learning unit.

RNN_TANH 

Specifies to use a single TanH gate Recurrent Learning unit.

LSTM 

Specifies to use a 4 gate LSTM Recurrent Learning unit.

GRU 

Specifies to use the GRU RNN where $ h' = tanh(r * Uh(t-1) + Wx) $ and $ h = (1 - z) * h' + z * h(t-1) $

Definition at line 348 of file CudaDnn.cs.

◆ SSD_CODE_TYPE

Defines the encode/decode type used during SSD cuda training.

This enum matches the values of the PriorBoxParameter.CodeType with the values supported in the low level CudaDnnDll.

Enumerator
CORNER 

Encode the corner.

CENTER_SIZE 

Encode the center size.

CORNER_SIZE 

Encode the corner size.

Definition at line 511 of file CudaDnn.cs.

◆ SSD_CONF_LOSS_TYPE

Defines the confidence loss types used during SSD cuda training.

This enum matches the values of the MultiboxLossParameter.ConfLossType with the values supported in the low level CudaDnnDll.

Enumerator
SOFTMAX 

Specifies to use softmax.

LOGISTIC 

Specifies to use logistic.

Definition at line 534 of file CudaDnn.cs.

◆ SSD_LOC_LOSS_TYPE

Defines the location loss types used during SSD cuda training.

This enum matches the values of the MultiboxLossParameter.LocLossType with the values supported in the low level CudaDnnDll.

Enumerator
L2 

Specifies to use L2 loss.

SMOOTH_L1 

Specifies to use smooth L1 loss.

Definition at line 553 of file CudaDnn.cs.

◆ SSD_MATCH_TYPE

Defines the matching method used during SSD cuda training.

This enum matches the values of the MultiBoxLossParameter.MatchType with the values supported in the low level CudaDnnDll.

Enumerator
BIPARTITE 

Specifies to use Bi-Partite.

PER_PREDICTION 

Specifies to use per-prediction matching.

Definition at line 492 of file CudaDnn.cs.

◆ SSD_MINING_TYPE

Defines the mining type used during SSD cuda training.

This enum matches the values of the MultiBoxLossParameter.MiningType with the values supported in the low level CudaDnnDll.

Enumerator
NONE 

Use all negatives.

MAX_NEGATIVE 

Select negatives based on the score.

HARD_EXAMPLE 

Select hard examples based on Shrivastava et. al. method.

See also
Training Region-based Object Detectors with Online Hard Example Mining by Abhinav Shrivastava, Abhinav Gupta, Ross Girshick, 2016.

Definition at line 466 of file CudaDnn.cs.

◆ TRAIN_STEP

Defines the training stepping method (if any).

Enumerator
NONE 

No stepping.

FORWARD 

Step only in the forward direction.

BACKWARD 

Step only in the backward direction.

BOTH 

Step in both directions (one forward and one backward).

Definition at line 118 of file Interfaces.cs.

◆ TRANSPOSE_OPERATION

Specifies the type of operation to perform along with a matrix transposition.

See also
CudaDnn::matrix_transpose_operation
Enumerator
ADD 

Add the matrix values after transposing.

MUL 

Multiply the matrix values after transposing.

DIV 

Divide the matrix values after transposing.

Definition at line 589 of file CudaDnn.cs.

◆ WEIGHT_TARGET

Defines the type of weight to target in re-initializations.

Enumerator
NONE 

No weights are targeted.

WEIGHTS 

Generic weights are targeted.

BIAS 

Bias weights are targeted.

BOTH 

Both weights and bias are targeted.

Definition at line 37 of file Interfaces.cs.

Function Documentation

◆ onGetWorkspace()

delegate void MyCaffe.common.onGetWorkspace ( object  sender,
WorkspaceArgs  e 
)

Delegate used to set the OnGetworkspace event.

Parameters
senderSpecifies the sender.
eSpecifies the arguments.

◆ onSetWorkspace()

delegate void MyCaffe.common.onSetWorkspace ( object  sender,
WorkspaceArgs  e 
)

Delegate used to set the OnSetworkspace event.

Parameters
senderSpecifies the sender.
eSpecifies the arguments.