An RNN layer in a network definition, version 2. More...

#include <NvInfer.h>

Inheritance diagram for nvinfer1::IRNNv2Layer:

Public Member Functions
virtual int32_t	getLayerCount () const =0

virtual int32_t	getHiddenSize () const =0

virtual int32_t	getMaxSeqLength () const =0

virtual int32_t	getDataLength () const =0

virtual void	setSequenceLengths (ITensor &seqLengths)=0
	Specify individual sequence lengths in the batch with the ITensor pointed to by `seqLengths`. More...

virtual ITensor *	getSequenceLengths () const =0
	Get the sequence lengths specified for the RNN. More...

virtual void	setOperation (RNNOperation op)=0
	Set the operation of the RNN layer. More...

virtual RNNOperation	getOperation () const =0
	Get the operation of the RNN layer. More...

virtual void	setInputMode (RNNInputMode op)=0
	Set the input mode of the RNN layer. More...

virtual RNNInputMode	getInputMode () const =0
	Get the input mode of the RNN layer. More...

virtual void	setDirection (RNNDirection op)=0
	Set the direction of the RNN layer. More...

virtual RNNDirection	getDirection () const =0
	Get the direction of the RNN layer. More...

virtual void	setWeightsForGate (int layerIndex, RNNGateType gate, bool isW, Weights weights)=0
	Set the weight parameters for an individual gate in the RNN. More...

virtual Weights	getWeightsForGate (int layerIndex, RNNGateType gate, bool isW) const =0
	Get the weight parameters for an individual gate in the RNN. More...

virtual void	setBiasForGate (int layerIndex, RNNGateType gate, bool isW, Weights bias)=0
	Set the bias parameters for an individual gate in the RNN. More...

virtual Weights	getBiasForGate (int layerIndex, RNNGateType gate, bool isW) const =0
	Get the bias parameters for an individual gate in the RNN. More...

virtual void	setHiddenState (ITensor &hidden)=0
	Set the initial hidden state of the RNN with the provided `hidden` ITensor. More...

virtual ITensor *	getHiddenState () const =0
	Get the initial hidden state of the RNN. More...

virtual void	setCellState (ITensor &cell)=0
	Set the initial cell state of the LSTM with the provided `cell` ITensor. More...

virtual ITensor *	getCellState () const =0
	Get the initial cell state of the RNN. More...

Public Member Functions inherited from nvinfer1::ILayer
virtual LayerType	getType () const =0
	Return the type of a layer. More...

virtual void	setName (const char *name)=0
	Set the name of a layer. More...

virtual const char *	getName () const =0
	Return the name of a layer. More...

virtual int	getNbInputs () const =0
	Get the number of inputs of a layer.

virtual ITensor *	getInput (int index) const =0
	Get the layer input corresponding to the given index. More...

virtual int	getNbOutputs () const =0
	Get the number of outputs of a layer.

virtual ITensor *	getOutput (int index) const =0
	Get the layer output corresponding to the given index. More...

virtual void	setInput (int index, ITensor &tensor)=0
	Replace an input of this layer with a specific tensor. More...

virtual void	setPrecision (DataType dataType)=0
	Set the computational precision of this layer. More...

virtual DataType	getPrecision () const =0
	get the computational precision of this layer More...

virtual bool	precisionIsSet () const =0
	whether the computational precision has been set for this layer More...

virtual void	resetPrecision ()=0
	reset the computational precision for this layer More...

virtual void	setOutputType (int index, DataType dataType)=0
	Set the output type of this layer. More...

virtual DataType	getOutputType (int index) const =0
	get the output type of this layer More...

virtual bool	outputTypeIsSet (int index) const =0
	whether the output type has been set for this layer More...

virtual void	resetOutputType (int index)=0
	reset the output type for this layer More...

Detailed Description

An RNN layer in a network definition, version 2.

This layer supersedes IRNNLayer.

Warning: Do not inherit from this class, as doing so will break forward-compatibility of the API and ABI.

Member Function Documentation

virtual Weights nvinfer1::IRNNv2Layer::getBiasForGate	(	int	layerIndex,
		RNNGateType	gate,
		bool	isW
	)		const

pure virtual

Get the bias parameters for an individual gate in the RNN.

See also: setBiasForGate()

virtual ITensor* nvinfer1::IRNNv2Layer::getCellState ( ) const

pure virtual

Get the initial cell state of the RNN.

See also: setCellState()

virtual RNNDirection nvinfer1::IRNNv2Layer::getDirection ( ) const

pure virtual

Get the direction of the RNN layer.

See also: setDirection(), RNNDirection

virtual ITensor* nvinfer1::IRNNv2Layer::getHiddenState ( ) const

pure virtual

Get the initial hidden state of the RNN.

See also: setHiddenState()

virtual RNNInputMode nvinfer1::IRNNv2Layer::getInputMode ( ) const

pure virtual

Get the input mode of the RNN layer.

See also: setInputMode(), RNNInputMode

virtual RNNOperation nvinfer1::IRNNv2Layer::getOperation ( ) const

pure virtual

Get the operation of the RNN layer.

See also: setOperation(), RNNOperation

virtual ITensor* nvinfer1::IRNNv2Layer::getSequenceLengths ( ) const

pure virtual

Get the sequence lengths specified for the RNN.

Returns: nullptr if no sequence lengths were specified, the sequence length data otherwise.

See also: setSequenceLengths()

virtual Weights nvinfer1::IRNNv2Layer::getWeightsForGate	(	int	layerIndex,
		RNNGateType	gate,
		bool	isW
	)		const

pure virtual

Get the weight parameters for an individual gate in the RNN.

See also: setWeightsForGate()

virtual void nvinfer1::IRNNv2Layer::setBiasForGate	(	int	layerIndex,
		RNNGateType	gate,
		bool	isW,
		Weights	bias
	)

pure virtual

Set the bias parameters for an individual gate in the RNN.

Parameters

layerIndex	The index of the layer that contains this gate. See the section Order of weight matrices in IRNNLayer::setWeights() for a description of the layer index.
gate	The name of the gate within the RNN layer. The gate name must correspond to one of the gates used by this layer's RNNOperation.
isW	True if the bias parameters are for the input bias Wb[g] and false if they are for the recurrent input bias Rb[g]. See RNNOperation for equations showing how these bias vectors are used in the RNN gate.
bias	The weight structure holding the bias parameters, which should be an array of size getHiddenSize().

virtual void nvinfer1::IRNNv2Layer::setCellState ( ITensor & cell )

pure virtual

Set the initial cell state of the LSTM with the provided cell ITensor.

The cell ITensor should have the dimensions {N1, ..., Np, L, H}, where:

N1..Np are the index dimensions specified by the input tensor
L is the number of layers in the RNN, equal to getLayerCount() if getDirection is kUNIDIRECTION, and 2x getLayerCount() if getDirection is kBIDIRECTION. In the bi-directional case, layer l's final forward hidden state is stored in L = 2*l, and final backward hidden state is stored in L= 2*l + 1.
H is the hidden state for each layer, equal to getHiddenSize().

It is an error to call setCellState() on an RNN layer that is not configured with RNNOperation::kLSTM.

virtual void nvinfer1::IRNNv2Layer::setDirection ( RNNDirection op )

pure virtual

Set the direction of the RNN layer.

See also: getDirection(), RNNDirection

virtual void nvinfer1::IRNNv2Layer::setHiddenState ( ITensor & hidden )

pure virtual

Set the initial hidden state of the RNN with the provided hidden ITensor.

The hidden ITensor should have the dimensions {N1, ..., Np, L, H}, where:

N1..Np are the index dimensions specified by the input tensor
L is the number of layers in the RNN, equal to getLayerCount() if getDirection is kUNIDIRECTION, and 2x getLayerCount() if getDirection is kBIDIRECTION. In the bi-directional case, layer l's final forward hidden state is stored in L = 2*l, and final backward hidden state is stored in L= 2*l + 1.
H is the hidden state for each layer, equal to getHiddenSize().

virtual void nvinfer1::IRNNv2Layer::setInputMode ( RNNInputMode op )

pure virtual

Set the input mode of the RNN layer.

See also: getInputMode(), RNNInputMode

virtual void nvinfer1::IRNNv2Layer::setOperation ( RNNOperation op )

pure virtual

Set the operation of the RNN layer.

See also: getOperation(), RNNOperation

virtual void nvinfer1::IRNNv2Layer::setSequenceLengths ( ITensor & seqLengths )

pure virtual

Specify individual sequence lengths in the batch with the ITensor pointed to by seqLengths.

The seqLengths ITensor should be a {N1, ..., Np} tensor, where N1..Np are the index dimensions of the input tensor to the RNN.

If this is not specified, then the RNN layer assumes all sequences are size getMaxSeqLength().

All sequence lengths in seqLengths should be in the range [1, getMaxSeqLength()]. Zero-length sequences are not supported.

This tensor must be of type DataType::kINT32.

virtual void nvinfer1::IRNNv2Layer::setWeightsForGate	(	int	layerIndex,
		RNNGateType	gate,
		bool	isW,
		Weights	weights
	)

pure virtual

Set the weight parameters for an individual gate in the RNN.

Parameters

layerIndex	The index of the layer that contains this gate. See the section Order of weight matrices in IRNNLayer::setWeights() for a description of the layer index.
gate	The name of the gate within the RNN layer. The gate name must correspond to one of the gates used by this layer's RNNOperation.
isW	True if the weight parameters are for the input matrix W[g] and false if they are for the recurrent input matrix R[g]. See RNNOperation for equations showing how these matrices are used in the RNN gate.
weights	The weight structure holding the weight parameters, which are stored as a row-major 2D matrix. See the layout of elements within a weight matrix in IRNNLayer::setWeights() for documentation on the expected dimensions of this matrix.

The documentation for this class was generated from the following file:

NvInfer.h

Public Member Functions

Detailed Description

Member Function Documentation