The TensorRT API version 1 namespace. More...

Classes
class	Dims
	structure to define the dimensions of a tensor More...

class	DimsHW
	descriptor for two-dimensional spatial data More...

class	DimsCHW
	descriptor for data with one channel dimension and two spatial dimensions More...

class	DimsNCHW
	descriptor for data with one index dimension, one channel dimension and two spatial dimensions More...

class	Weights
	an array of weights used as a layer parameter More...

class	IHostMemory
	class to handle library allocated memory that is accessible to the user. More...

class	ITensor
	a tensor in a network definition More...

class	ILayer
	base class for all layer classes in a network definition More...

class	IConvolutionLayer
	a convolution layer in a network definition More...

class	IFullyConnectedLayer
	a fully connected layer in a network definition More...

class	IActivationLayer
	an Activation layer in a network definition More...

class	IPoolingLayer
	a Pooling layer in a network definition More...

class	ILRNLayer
	a LRN layer in a network definition More...

class	IScaleLayer
	a Scale layer in a network definition More...

class	ISoftMaxLayer
	a Softmax layer in a network definition More...

class	IConcatenationLayer
	a concatenation layer in a network definition More...

class	IDeconvolutionLayer
	a deconvolution layer in a network definition More...

class	IElementWiseLayer
	a elementwise layer in a network definition More...

class	IRNNLayer
	a RNN layer in a network definition More...

class	IOutputDimensionsFormula
	application-implemented inteface to compute layer output sizes More...

class	IPlugin
	plugin class for user-implemented layers More...

class	IPluginLayer
	layer type for plugins More...

class	IUnaryLayer
	layer that represents a unary operation More...

class	IPaddingLayer
	layer that represents a padding operation More...

struct	Permutation

class	IShuffleLayer
	layer type for shuffling data More...

class	INetworkDefinition
	a network definition for input to the builder More...

class	IProfiler
	application-implemented interface for profiling More...

class	IExecutionContext
	context for executing inference using an engine More...

class	ICudaEngine
	an engine for executing inference on a built network More...

class	IInt8Calibrator
	application-implemented interface for calibration More...

class	IInt8EntropyCalibrator

class	IInt8LegacyCalibrator

class	IBuilder
	builds an engine from a network definition More...

class	IPluginFactory
	plugin factory for deserialization More...

class	IRuntime
	allows a serialized engine to be deserialized More...

class	ILogger
	application-implemented logging interface for the builder, engine and runtime. More...

Enumerations
enum	DataType : int { DataType::kFLOAT = 0, DataType::kHALF = 1, DataType::kINT8 = 2 }
	the type of weights and tensors. More...

enum	DimensionType : int { DimensionType::kSPATIAL = 0, DimensionType::kCHANNEL = 1, DimensionType::kINDEX = 2, DimensionType::kSEQUENCE = 3 }
	the type of data encoded across this dimension More...

enum	LayerType : int { LayerType::kCONVOLUTION = 0, LayerType::kFULLY_CONNECTED = 1, LayerType::kACTIVATION = 2, LayerType::kPOOLING = 3, LayerType::kLRN = 4, LayerType::kSCALE = 5, LayerType::kSOFTMAX = 6, LayerType::kDECONVOLUTION = 7, LayerType::kCONCATENATION = 8, LayerType::kELEMENTWISE = 9, LayerType::kPLUGIN = 10, LayerType::kRNN = 11, LayerType::kUNARY = 12, LayerType::kPADDING = 13, LayerType::kSHUFFLE = 14 }
	the type values of layer classes More...

enum	ActivationType : int { ActivationType::kRELU = 0, ActivationType::kSIGMOID = 1, ActivationType::kTANH = 2 }
	enumerates the types of activation to perform in an activation layer. More...

enum	PoolingType : int { kMAX = 0, kAVERAGE = 1, kMAX_AVERAGE_BLEND = 2 }
	the type of pooling to perform in a pooling layer.

enum	ScaleMode : int { ScaleMode::kUNIFORM = 0, ScaleMode::kCHANNEL = 1, ScaleMode::kELEMENTWISE = 2 }
	controls how scale is applied in a Scale layer More...

enum	ElementWiseOperation : int { ElementWiseOperation::kSUM = 0, ElementWiseOperation::kPROD = 1, ElementWiseOperation::kMAX = 2, ElementWiseOperation::kMIN = 3, ElementWiseOperation::kSUB = 4, ElementWiseOperation::kDIV = 5, ElementWiseOperation::kPOW = 6 }
	enumerates the binary operations that may be performed by an ElementWise layer More...

enum	RNNOperation : int { RNNOperation::kRELU = 0, RNNOperation::kTANH = 1, RNNOperation::kLSTM = 2, RNNOperation::kGRU = 3 }
	enumerates the RNN operations that may be performed by an RNN layer More...

enum	RNNDirection : int { RNNDirection::kUNIDIRECTION = 0, RNNDirection::kBIDIRECTION = 1 }
	enumerates the RNN direction that may be performed by an RNN layer More...

enum	RNNInputMode : int { RNNInputMode::kLINEAR = 0, RNNInputMode::kSKIP = 1 }
	enumerates the RNN input modes that may occur with an RNN layer More...

enum	UnaryOperation : int { UnaryOperation::kEXP = 0, UnaryOperation::kLOG = 1, UnaryOperation::kSQRT = 2, UnaryOperation::kRECIP = 3, UnaryOperation::kABS = 4, UnaryOperation::kNEG = 5 }
	enumerates the binary operations that may be performed by an ElementWise layer More...

enum	CalibrationAlgoType : int { kLEGACY_CALIBRATION = 0, kENTROPY_CALIBRATION = 1 }
	version of calibration algorithm to use More...

enum	PluginType : int { PluginType::kFASTERRCNN = 0, PluginType::kNORMALIZE = 1, PluginType::kPERMUTE = 2, PluginType::kPRIORBOX = 3, PluginType::kSSDDETECTIONOUTPUT = 4, PluginType::kCONCAT = 5, PluginType::kPRELU = 6, PluginType::kYOLOREORG = 7, PluginType::kYOLOREGION = 8 }
	the type values for the various plugins More...

Functions
template<typename T >
int	EnumMax ()
	maximum number of elements in an enumeration type

template<>
int	EnumMax< DataType > ()
	maximum number of elements in DataType enum. More...

template<>
int	EnumMax< DimensionType > ()
	maximum number of elements in DimensionType enum. More...

template<>
int	EnumMax< LayerType > ()
	maximum number of elements in LayerType enum. More...

template<>
int	EnumMax< ActivationType > ()
	maximum number of elements in ActivationType enum. More...

template<>
int	EnumMax< PoolingType > ()
	maximum number of elements in PoolingType enum. More...

template<>
int	EnumMax< ScaleMode > ()
	maximum number of elements in ScaleMode enum. More...

template<>
int	EnumMax< ElementWiseOperation > ()
	maximum number of elements in ElementWiseOperation enum. More...

template<>
int	EnumMax< RNNOperation > ()
	maximum number of elements in RNNOperation enum. More...

template<>
int	EnumMax< RNNDirection > ()
	maximum number of elements in RNNDirection enum. More...

template<>
int	EnumMax< RNNInputMode > ()
	maximum number of elements in RNNInputMode enum. More...

template<>
int	EnumMax< UnaryOperation > ()
	maximum number of elements in ElementWiseOperation enum. More...

template<>
int	EnumMax< CalibrationAlgoType > ()
	maximum number of elements in CalibrationAlgoType enum. More...

template<>
int	EnumMax< ILogger::Severity > ()
	maximum number of elements in DataType enum. More...

template<>
int	EnumMax< PluginType > ()
	maximum number of elements in PluginType enum. More...

Detailed Description

The TensorRT API version 1 namespace.

Enumeration Type Documentation

enum nvinfer1::ActivationType : int

strong

enumerates the types of activation to perform in an activation layer.

Enumerator
kRELU	rectified linear activation
kSIGMOID	sigmoid activation
kTANH	TanH activation.

enum nvinfer1::CalibrationAlgoType : int

strong

version of calibration algorithm to use

enum CalibrationAlgoType

enum nvinfer1::DataType : int

strong

the type of weights and tensors.

Enumerator
kFLOAT	FP32 format.
kHALF	FP16 format.
kINT8	INT8 format.

enum nvinfer1::DimensionType : int

strong

the type of data encoded across this dimension

Enumerator
kSPATIAL	elements correspond to different spatial data
kCHANNEL	elements correspond to different channels
kINDEX	elements correspond to different batch index
kSEQUENCE	elements correspond to different sequence values

enum nvinfer1::ElementWiseOperation : int

strong

enumerates the binary operations that may be performed by an ElementWise layer

See Also: IElementWiseLayer

Enumerator
kSUM	sum of the two elements
kPROD	product of the two elements
kMAX	maximum of the two elements
kMIN	minimum of the two elements
kSUB	substract the second element from the first
kDIV	divide the first element by the second
kPOW	the first element to the power of the second element

enum nvinfer1::LayerType : int

strong

the type values of layer classes

See Also: ILayer::getType()

Enumerator
kCONVOLUTION	Convolution layer.
kFULLY_CONNECTED	Fully connected layer.
kACTIVATION	Activation layer.
kPOOLING	Pooling layer.
kLRN	LRN layer.
kSCALE	Scale Layer.
kSOFTMAX	SoftMax layer.
kDECONVOLUTION	Deconvolution layer.
kCONCATENATION	Concatenation layer.
kELEMENTWISE	Elementwise layer.
kPLUGIN	Plugin layer.
kRNN	RNN Layer.
kUNARY	UnaryOp Operation Layer.
kPADDING	Padding Layer.
kSHUFFLE	Shuffle Layer.

enum nvinfer1::PluginType : int

strong

the type values for the various plugins

See Also: INvPlugin::getPluginType()

Enumerator
kFASTERRCNN	FasterRCNN fused plugin (RPN + ROI pooling)
kNORMALIZE	Normalize plugin.
kPERMUTE	Permute plugin.
kPRIORBOX	PriorBox plugin.
kSSDDETECTIONOUTPUT	SSD DetectionOutput plugin.
kCONCAT	Concat plugin.
kPRELU	YOLO PReLU Plugin.
kYOLOREORG	YOLO Reorg Plugin.
kYOLOREGION	YOLO Region Plugin.

enum nvinfer1::RNNDirection : int

strong

enumerates the RNN direction that may be performed by an RNN layer

See Also: IRNNLayer

Enumerator
kUNIDIRECTION	Network iterations from first input to last input.
kBIDIRECTION	Network iterates from first to last and vice versa and outputs concatenated.

enum nvinfer1::RNNInputMode : int

strong

enumerates the RNN input modes that may occur with an RNN layer

See Also: IRNNLayer

Enumerator
kLINEAR	Perform the normal matrix multiplication in the first recurrent layer.
kSKIP	No operation is performed on the first recurrent layer.

enum nvinfer1::RNNOperation : int

strong

enumerates the RNN operations that may be performed by an RNN layer

Equation definitions: i - input gate o - output gate f - forget gate z - update gate r - reset gate c - cell gate h - hidden gate t - time step(t-1 means previous time step) Xi - input tensor W[izrfcoh] - W parameter weight matrices for the corresponding gates R[izrfcoh] - U parameter weight matrices for the corresponding gates Wb[izrfcoh] - W parameter bias vectors for the corresponding gates Rb[izrfcoh] - U parameter bias vectors for the corresponding gates ReLU(X) - max(X, 0) tanh - hyperbolic tangent of X sigmoid(X) - 1 / (1 + e^-X) [C|H] - Cell/Hidden state

Equations: kRELU
- Ht = ReLU(WiXt + RiHt-1 + Wbi + Rbi) kTANH
- Ht = TANH(WiXt + RiHt-1 + Wbi + Rbi) kLSTM
- it = sigmoid(WiXt + RiHt-1 + Wbi + Rbi)
- ft = sigmoid(WfXt + RfHt-1 + Wbf + Rbf)
- ot = sigmoid(WoXt + RoHt-1 + Wbo + Rbo)
- ct = tanh(WcXt + RcHt-1 + Wbc + Rbc)
- C = f * Ct-1 + it * ct
- H = ot * tanh(C) kGRU
- zt = sigmoid(WzXt + RzHt-1 + Wbz + Rbz)
- rt = sigmoid(WrXt + RrHt-1 + Wbr + Rbr)
- ht = tanh(WhXt + rt *(RhHt-1 + Rbh) + Wbh)
- H = (1 - zt) * ht + it * Ht-1

See Also: IRNNLayer

Enumerator
kRELU	Single gate RNN w/ ReLU activation function.
kTANH	Single gate RNN w/ TANH activation function.
kLSTM	Four-gate LSTM network w/o peephole connections.
kGRU	Three-gate network consisting of Gated Recurrent Units.