A network definition for input to the builder. More...

Collaboration diagram for nvinfer1::INetworkDefinition:

[legend]

Public Member Functions
virtual ITensor *	addInput (const char *name, DataType type, Dims dimensions)=0
	Add an input tensor to the network. More...

virtual void	markOutput (ITensor &tensor)=0
	Mark a tensor as a network output. More...

	__attribute__ ((deprecated)) virtual IConvolutionLayer *addConvolution(ITensor &input
	Add a convolution layer to the network. More...

virtual IFullyConnectedLayer *	addFullyConnected (ITensor &input, int32_t nbOutputs, Weights kernelWeights, Weights biasWeights)=0
	Add a fully connected layer to the network. More...

virtual IActivationLayer *	addActivation (ITensor &input, ActivationType type)=0
	Add an activation layer to the network. More...

	__attribute__ ((deprecated)) virtual IPoolingLayer *addPooling(ITensor &input
	Add a pooling layer to the network. More...

virtual ILRNLayer *	addLRN (ITensor &input, int32_t window, float alpha, float beta, float k)=0
	Add a LRN layer to the network. More...

virtual IScaleLayer *	addScale (ITensor &input, ScaleMode mode, Weights shift, Weights scale, Weights power)=0
	Add a Scale layer to the network. More...

virtual ISoftMaxLayer *	addSoftMax (ITensor &input)=0
	Add a SoftMax layer to the network. More...

virtual IConcatenationLayer *	addConcatenation (ITensor const inputs, int32_t nbInputs)=0
	Add a concatenation layer to the network. More...

	__attribute__ ((deprecated)) virtual IDeconvolutionLayer *addDeconvolution(ITensor &input
	Add a deconvolution layer to the network. More...

virtual IElementWiseLayer *	addElementWise (ITensor &input1, ITensor &input2, ElementWiseOperation op)=0
	Add an elementwise layer to the network. More...

	__attribute__ ((deprecated)) virtual IRNNLayer *addRNN(ITensor &inputs
	Add an `layerCount` deep RNN layer to the network with a sequence length of `maxSeqLen` and `hiddenSize` internal state per layer. More...

	__attribute__ ((deprecated)) virtual IPluginLayer addPlugin(ITensor const *inputs
	Add a plugin layer to the network. More...

virtual IUnaryLayer *	addUnary (ITensor &input, UnaryOperation operation)=0
	Add a unary layer to the network. More...

	__attribute__ ((deprecated)) virtual IPaddingLayer *addPadding(ITensor &input
	Add a padding layer to the network. More...

virtual IShuffleLayer *	addShuffle (ITensor &input)=0
	Add a shuffle layer to the network. More...

	__attribute__ ((deprecated)) virtual void setPoolingOutputDimensionsFormula(IOutputDimensionsFormula *formula)=0
	Set the pooling output dimensions formula. More...

	__attribute__ ((deprecated)) virtual IOutputDimensionsFormula &getPoolingOutputDimensionsFormula() const =0
	Get the pooling output dimensions formula. More...

	__attribute__ ((deprecated)) virtual void setConvolutionOutputDimensionsFormula(IOutputDimensionsFormula *formula)=0
	Set the convolution output dimensions formula. More...

	__attribute__ ((deprecated)) virtual IOutputDimensionsFormula &getConvolutionOutputDimensionsFormula() const =0
	Get the convolution output dimensions formula. More...

	__attribute__ ((deprecated)) virtual void setDeconvolutionOutputDimensionsFormula(IOutputDimensionsFormula *formula)=0
	Set the deconvolution output dimensions formula. More...

	__attribute__ ((deprecated)) virtual IOutputDimensionsFormula &getDeconvolutionOutputDimensionsFormula() const =0
	Get the deconvolution output dimensions formula. More...

virtual int32_t	getNbLayers () const =0
	Get the number of layers in the network. More...

virtual ILayer *	getLayer (int32_t index) const =0
	Get the layer specified by the given index. More...

virtual int32_t	getNbInputs () const =0
	Get the number of inputs in the network. More...

virtual ITensor *	getInput (int32_t index) const =0
	Get the input tensor specified by the given index. More...

virtual int32_t	getNbOutputs () const =0
	Get the number of outputs in the network. More...

virtual ITensor *	getOutput (int32_t index) const =0
	Get the output tensor specified by the given index. More...

virtual void	destroy ()=0
	Destroy this INetworkDefinition object. More...

virtual IReduceLayer *	addReduce (ITensor &input, ReduceOperation operation, uint32_t reduceAxes, bool keepDimensions)=0
	Add a reduce layer to the network. More...

virtual ITopKLayer *	addTopK (ITensor &input, TopKOperation op, int32_t k, uint32_t reduceAxes)=0
	Add a TopK layer to the network. More...

virtual IGatherLayer *	addGather (ITensor &data, ITensor &indices, int32_t axis)=0
	Add a gather layer to the network. More...

virtual IRaggedSoftMaxLayer *	addRaggedSoftMax (ITensor &input, ITensor &bounds)=0
	Add a RaggedSoftMax layer to the network. More...

virtual IMatrixMultiplyLayer *	addMatrixMultiply (ITensor &input0, MatrixOperation op0, ITensor &input1, MatrixOperation op1)=0
	Add a MatrixMultiply layer to the network. More...

	__attribute__ ((deprecated)) virtual IMatrixMultiplyLayer *addMatrixMultiply(ITensor &input0
	Add a MatrixMultiply layer to the network. More...

virtual IConstantLayer *	addConstant (Dims dimensions, Weights weights)=0
	Add a constant layer to the network. More...

	__attribute__ ((deprecated)) virtual IRNNv2Layer *addRNNv2(ITensor &input
	Add an `layerCount` deep RNN layer to the network with `hiddenSize` internal states that can take a batch with fixed or variable sequence lengths. More...

	__attribute__ ((deprecated)) virtual IPluginLayer addPluginExt(ITensor const *inputs
	Add a plugin layer to the network using an IPluginExt interface. More...

virtual IIdentityLayer *	addIdentity (ITensor &input)=0
	Add an identity layer. More...

virtual void	removeTensor (ITensor &tensor)=0
	remove a tensor from the network definition. More...

virtual void	unmarkOutput (ITensor &tensor)=0
	unmark a tensor as a network output. More...

virtual IPluginV2Layer *	addPluginV2 (ITensor const inputs, int32_t nbInputs, IPluginV2 &plugin)=0
	Add a plugin layer to the network using the IPluginV2 interface. More...

virtual ISliceLayer *	addSlice (ITensor &input, Dims start, Dims size, Dims stride)=0
	Add a slice layer to the network. More...

virtual void	setName (const char *name)=0
	Sets the name of the network. More...

virtual const char *	getName () const =0
	Returns the name associated with the network. More...

virtual IShapeLayer *	addShape (ITensor &input)=0
	Add a shape layer to the network. More...

virtual bool	hasImplicitBatchDimension () const =0
	Query whether the network was created with an implicit batch dimension. More...

virtual bool	markOutputForShapes (ITensor &tensor)=0
	Enable tensor's value to be computed by IExecutionContext::getShapeBinding. More...

virtual bool	unmarkOutputForShapes (ITensor &tensor)=0
	Undo markOutputForShapes. More...

virtual IParametricReLULayer *	addParametricReLU (ITensor &input, ITensor &slope) noexcept=0
	Add a parametric ReLU layer to the network. More...

virtual IConvolutionLayer *	addConvolutionNd (ITensor &input, int32_t nbOutputMaps, Dims kernelSize, Weights kernelWeights, Weights biasWeights)=0
	Add a multi-dimension convolution layer to the network. More...

virtual IPoolingLayer *	addPoolingNd (ITensor &input, PoolingType type, Dims windowSize)=0
	Add a multi-dimension pooling layer to the network. More...

virtual IDeconvolutionLayer *	addDeconvolutionNd (ITensor &input, int32_t nbOutputMaps, Dims kernelSize, Weights kernelWeights, Weights biasWeights)=0
	Add a multi-dimension deconvolution layer to the network. More...

virtual IScaleLayer *	addScaleNd (ITensor &input, ScaleMode mode, Weights shift, Weights scale, Weights power, int32_t channelAxis)=0
	Add a multi-dimension scale layer to the network. More...

virtual IResizeLayer *	addResize (ITensor &input)=0
	Add a resize layer to the network. More...

virtual bool	hasExplicitPrecision () const =0
	True if network is an explicit precision network. More...

virtual ILoop *	addLoop () noexcept=0
	Add a loop to the network. More...

virtual ISelectLayer *	addSelect (ITensor &condition, ITensor &thenInput, ITensor &elseInput)=0
	Add a select layer to the network. More...

virtual IFillLayer *	addFill (Dims dimensions, FillOperation op) noexcept=0
	Add a fill layer to the network. More...

virtual IPaddingLayer *	addPaddingNd (ITensor &input, Dims prePadding, Dims postPadding)=0
	Add a padding layer to the network. More...

Public Attributes
int32_t	nbOutputMaps

int32_t DimsHW	kernelSize

int32_t DimsHW Weights	kernelWeights

int32_t DimsHW Weights Weights	biasWeights = 0

PoolingType	type

PoolingType DimsHW	windowSize = 0

int32_t	layerCount

int32_t std::size_t	hiddenSize

int32_t std::size_t int32_t	maxSeqLen

int32_t std::size_t int32_t RNNOperation	op

int32_t std::size_t int32_t RNNOperation RNNInputMode	mode

int32_t std::size_t int32_t RNNOperation RNNInputMode RNNDirection	dir

int32_t std::size_t int32_t RNNOperation RNNInputMode RNNDirection Weights	weights

int32_t std::size_t int32_t RNNOperation RNNInputMode RNNDirection Weights Weights	bias = 0

int32_t	nbInputs

int32_t IPlugin &	plugin = 0

DimsHW	prePadding

DimsHW DimsHW	postPadding = 0

bool	transpose0

bool ITensor &	input1

bool ITensor bool	transpose1 = 0

int32_t int32_t	hiddenSize

int32_t int32_t int32_t	maxSeqLen

int32_t int32_t int32_t RNNOperation	op = 0

int32_t IPluginExt &	plugin = 0

Protected Member Functions
virtual	~INetworkDefinition ()

Detailed Description

A network definition for input to the builder.

A network definition defines the structure of the network, and combined with a IBuilderConfig, is built into an engine using an IBuilder. An INetworkDefinition can either have an implicit batch dimensions, specified at runtime, or all dimensions explicit, full dims mode, in the network definition. When a network has been created using createNetwork(), only implicit batch size mode is supported. The function hasImplicitBatchSize() is used to query the mode of the network.

A network with implicit batch dimensions returns the dimensions of a layer without the implicit dimension, and instead the batch is specified at execute/enqueue time. If the network has all dimensions specified, then the first dimension follows elementwise broadcast rules: if it is 1 for some inputs and is some value N for all other inputs, then the first dimension of each outut is N, and the inputs with 1 for the first dimension are broadcast. Having divergent batch sizes across inputs to a layer is not supported.

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

Constructor & Destructor Documentation

◆ ~INetworkDefinition()

virtual nvinfer1::INetworkDefinition::~INetworkDefinition ( )

inlineprotectedvirtual

Member Function Documentation

◆ addInput()

virtual ITensor* nvinfer1::INetworkDefinition::addInput	(	const char *	name,
		DataType	type,
		Dims	dimensions
	)

pure virtual

Add an input tensor to the network.

The name of the input tensor is used to find the index into the buffer array for an engine built from the network. The volume of the dimensions must be less than 2^30 elements. For networks with an implicit batch dimension, this volume includes the batch dimension with its length set to the maximum batch size. For networks with all explicit dimensions and with wildcard dimensions, the volume is based on the maxima specified by an IOptimizationProfile.Dimensions are normally non-negative integers. The exception is that in networks with all explicit dimensions, -1 can be used as a wildcard for a dimension to be specified at runtime. Input tensors with such a wildcard must have a corresponding entry in the IOptimizationProfiles indicating the permitted extrema, and the input dimensions must be set by IExecutionContext::setBindingDimensions. Different IExecutionContext instances can have different dimensions. Wildcard dimensions are only supported for EngineCapability::kDEFAULT. They are not supported in safety contexts. DLA does not support Wildcard dimensions.

Tensor dimensions are specified independent of format. For example, if a tensor is formatted in "NHWC" or a vectorized format, the dimensions are still specified in the order{N, C, H, W}. For 2D images with a channel dimension, the last three dimensions are always {C,H,W}. For 3D images with a channel dimension, the last four dimensions are always {C,D,H,W}.

Parameters

name	The name of the tensor.
type	The type of the data held in the tensor.
dimensions	The dimensions of the tensor.

Warning: It is an error to specify a wildcard value on a dimension that is determined by trained parameters.; If run on DLA with explicit dimensions, only leading dimension can be a wildcard. And provided profile must have same minimum, optimum, and maximum dimensions.

See also: ITensor

Returns: The new tensor or nullptr if there is an error.

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◆ markOutput()

virtual void nvinfer1::INetworkDefinition::markOutput ( ITensor & tensor )

pure virtual

Mark a tensor as a network output.

Parameters

tensor The tensor to mark as an output tensor.

Warning: It is an error to mark a network input as an output.

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◆ attribute() [1/15]

nvinfer1::INetworkDefinition::__attribute__ ( (deprecated) ) &

Add a convolution layer to the network.

Parameters

input	The input tensor to the convolution.
nbOutputMaps	The number of output feature maps for the convolution.
kernelSize	The HW-dimensions of the convolution kernel.
kernelWeights	The kernel weights for the convolution.
biasWeights	The optional bias weights for the convolution.

See also: IConvolutionLayer

Warning: It is an error to specify a wildcard value for the 'C' dimension of the input tensor.; Int32 tensors are not valid input tensors.

Returns: The new convolution layer, or nullptr if it could not be created.

Deprecated:: Superseded by addConvolutionNd and will be removed in TensorRT 9.0.

◆ addFullyConnected()

virtual IFullyConnectedLayer* nvinfer1::INetworkDefinition::addFullyConnected	(	ITensor &	input,
		int32_t	nbOutputs,
		Weights	kernelWeights,
		Weights	biasWeights
	)

pure virtual

Add a fully connected layer to the network.

Parameters

input	The input tensor to the layer.
nbOutputs	The number of outputs of the layer.
kernelWeights	The kernel weights for the fully connected layer.
biasWeights	The optional bias weights for the fully connected layer.

See also: IFullyConnectedLayer

Warning: It is an error to specify a wildcard value for the 'C' dimension of the input tensor.; Int32 tensors are not valid input tensors.

Returns: The new fully connected layer, or nullptr if it could not be created.

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◆ addActivation()

virtual IActivationLayer* nvinfer1::INetworkDefinition::addActivation	(	ITensor &	input,
		ActivationType	type
	)

pure virtual

Add an activation layer to the network.

Parameters

input	The input tensor to the layer.
type	The type of activation function to apply.

Note that the setAlpha() and setBeta() methods must be used on the output for activations that require these parameters.

See also: IActivationLayer ActivationType

Warning: Int32 tensors are not valid input tensors.

Returns: The new activation layer, or nullptr if it could not be created.

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◆ attribute() [2/15]

nvinfer1::INetworkDefinition::__attribute__ ( (deprecated) ) &

Add a pooling layer to the network.

Parameters

input	The input tensor to the layer.
type	The type of pooling to apply.
windowSize	The size of the pooling window.

See also: IPoolingLayer PoolingType

Warning: Int32 tensors are not valid input tensors.

Returns: The new pooling layer, or nullptr if it could not be created.

Deprecated:: Superseded by addPoolingNd and will be removed in TensorRT 9.0.

◆ addLRN()

virtual ILRNLayer* nvinfer1::INetworkDefinition::addLRN	(	ITensor &	input,
		int32_t	window,
		float	alpha,
		float	beta,
		float	k
	)

pure virtual

Add a LRN layer to the network.

Parameters

input	The input tensor to the layer.
window	The size of the window.
alpha	The alpha value for the LRN computation.
beta	The beta value for the LRN computation.
k	The k value for the LRN computation.

See also: ILRNLayer

Warning: Int32 tensors are not valid input tensors.

Returns: The new LRN layer, or nullptr if it could not be created.

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◆ addScale()

virtual IScaleLayer* nvinfer1::INetworkDefinition::addScale	(	ITensor &	input,
		ScaleMode	mode,
		Weights	shift,
		Weights	scale,
		Weights	power
	)

pure virtual

Add a Scale layer to the network.

Parameters

input	The input tensor to the layer. This tensor is required to have a minimum of 3 dimensions.
mode	The scaling mode.
shift	The shift value.
scale	The scale value.
power	The power value.

If the weights are available, then the size of weights are dependent on the ScaleMode. For ::kUNIFORM, the number of weights equals 1. For ::kCHANNEL, the number of weights equals the channel dimension. For ::kELEMENTWISE, the number of weights equals the product of the last three dimensions of the input.

See also: addScaleNd; IScaleLayer

Warning: Int32 tensors are not valid input tensors.

Returns: The new Scale layer, or nullptr if it could not be created.

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◆ addSoftMax()

virtual ISoftMaxLayer* nvinfer1::INetworkDefinition::addSoftMax ( ITensor & input )

pure virtual

Add a SoftMax layer to the network.

See also: ISoftMaxLayer

Warning: Int32 tensors are not valid input tensors.

Returns: The new SoftMax layer, or nullptr if it could not be created.

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◆ addConcatenation()

virtual IConcatenationLayer* nvinfer1::INetworkDefinition::addConcatenation	(	ITensor const	inputs,
		int32_t	nbInputs
	)

pure virtual

Add a concatenation layer to the network.

Parameters

inputs	The input tensors to the layer.
nbInputs	The number of input tensors.

See also: IConcatenationLayer

Returns: The new concatenation layer, or nullptr if it could not be created.

Warning: All tensors must have the same dimensions for all dimensions except for channel.

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◆ attribute() [3/15]

nvinfer1::INetworkDefinition::__attribute__ ( (deprecated) ) &

Add a deconvolution layer to the network.

Parameters

input	The input tensor to the layer.
nbOutputMaps	The number of output feature maps.
kernelSize	The HW-dimensions of the deconvolution kernel.
kernelWeights	The kernel weights for the deconvolution.
biasWeights	The optional bias weights for the deconvolution.

See also: IDeconvolutionLayer

Warning: It is an error to specify a wildcard value for the 'C' dimension of the input tensor.; Int32 tensors are not valid input tensors.

Returns: The new deconvolution layer, or nullptr if it could not be created.

Deprecated:: Superseded by addDeconvolutionNd and will be removed in TensorRT 9.0.

◆ addElementWise()

virtual IElementWiseLayer* nvinfer1::INetworkDefinition::addElementWise	(	ITensor &	input1,
		ITensor &	input2,
		ElementWiseOperation	op
	)

pure virtual

Add an elementwise layer to the network.

Parameters

input1	The first input tensor to the layer.
input2	The second input tensor to the layer.
op	The binary operation that the layer applies.

The input tensors must have the same number of dimensions. For each dimension, their lengths must match, or one of them must be one. In the latter case, the tensor is broadcast along that axis.

The output tensor has the same number of dimensions as the inputs. For each dimension, its length is the maximum of the lengths of the corresponding input dimension.

See also: IElementWiseLayer

Warning: For shape tensors, ElementWiseOperation::kPOW is not a valid op.

Returns: The new elementwise layer, or nullptr if it could not be created.

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◆ attribute() [4/15]

nvinfer1::INetworkDefinition::__attribute__ ( (deprecated) ) &

Add an layerCount deep RNN layer to the network with a sequence length of maxSeqLen and hiddenSize internal state per layer.

Parameters

inputs	The input tensor to the layer.
layerCount	The number of layers in the RNN.
hiddenSize	The size of the internal hidden state for each layer.
maxSeqLen	The maximum length of the time sequence.
op	The type of RNN to execute.
mode	The input mode for the RNN.
dir	The direction to run the RNN.
weights	The weights for the weight matrix parameters of the RNN.
bias	The weights for the bias vectors parameters of the RNN.

The inputs tensor must be of the type DataType::kFLOAT or DataType::kHALF, and have non-zero volume.

See IRNNLayer::setWeights() and IRNNLayer::setBias() for details on the required input format for weights and bias.

The layout for the input tensor should be {1, S_max, N, E}, where:

S_max is the maximum allowed sequence length (number of RNN iterations)
N is the batch size
E specifies the embedding length (unless ::kSKIP is set, in which case it should match getHiddenSize()).

The first output tensor is the output of the final RNN layer across all timesteps, with dimensions {S_max, N, H}:

S_max is the maximum allowed sequence length (number of RNN iterations)
N is the batch size
H is an output hidden state (equal to getHiddenSize() or 2x getHiddenSize())

The second tensor is the final hidden state of the RNN across all layers, and if the RNN is an LSTM (i.e. getOperation() is ::kLSTM), then the third tensor is the final cell state of the RNN across all layers. Both the second and third output tensors have dimensions {L, N, H}:

L is equal to getLayerCount() if getDirection is ::kUNIDIRECTION, and 2*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.
N is the batch size
H is getHiddenSize().

Note that in bidirectional RNNs, the full "hidden state" for a layer l is the concatenation of its forward hidden state and its backward hidden state, and its size is 2*H.

Deprecated:: Superseded by addRNNv2 and will be removed in TensorRT 8.0.

See also: IRNNLayer

Warning: This layer does not support wildcard dimensions or explicit batch size networks.; Int32 tensors are not valid input tensors.

Returns: The new RNN layer, or nullptr if it could not be created.

◆ attribute() [5/15]

nvinfer1::INetworkDefinition::__attribute__ ( (deprecated) ) const

Add a plugin layer to the network.

Parameters

inputs	The input tensors to the layer.
nbInputs	The number of input tensors.
plugin	The layer plugin.

See also: IPluginLayer

Deprecated:: Superseded by addPluginV2 and will be removed in TensorRT 8.0.

Warning: Plugin inputs do not support wildcard dimensions or explicit batch size networks.; Int32 tensors are not valid input tensors.

Returns: the new plugin layer, or nullptr if it could not be created.

◆ addUnary()

virtual IUnaryLayer* nvinfer1::INetworkDefinition::addUnary	(	ITensor &	input,
		UnaryOperation	operation
	)

pure virtual

Add a unary layer to the network.

Parameters

input	The input tensor to the layer.
operation	The operation to apply.

See also: IUnaryLayer

Warning: Int32 tensors are not valid input tensors.; Shape tensors are not supported as outputs.

Returns: The new unary layer, or nullptr if it could not be created

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◆ attribute() [6/15]

nvinfer1::INetworkDefinition::__attribute__ ( (deprecated) ) &

Add a padding layer to the network.

Parameters

input	The input tensor to the layer.
prePadding	The padding to apply to the start of the tensor.
postPadding	The padding to apply to the end of the tensor.

See also: IPaddingLayer

Returns: The new padding layer, or nullptr if it could not be created.

Deprecated:: Superseded by addPaddingNd and will be removed in TensorRT 9.0.

◆ addShuffle()

virtual IShuffleLayer* nvinfer1::INetworkDefinition::addShuffle ( ITensor & input )

pure virtual

Add a shuffle layer to the network.

Parameters

input The input tensor to the layer.

See also: IShuffleLayer

Returns: The new shuffle layer, or nullptr if it could not be created.

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◆ attribute() [7/15]

nvinfer1::INetworkDefinition::__attribute__ ( (deprecated) )

pure virtual

Set the pooling output dimensions formula.

Deprecated:: This method does not currently work reliably and will be removed in TensorRT 8.0.

Parameters

formula The formula from computing the pooling output dimensions. If null is passed, the default formula is used.

The default formula in each dimension is (inputDim + padding * 2 - kernelSize) / stride + 1.

Warning: Custom output dimensions formulas are not supported with wildcard dimensions.

See also: IOutputDimensionsFormula getPoolingOutputDimensionsFormula()

◆ attribute() [8/15]

nvinfer1::INetworkDefinition::__attribute__ ( (deprecated) ) const &

pure virtual

Get the pooling output dimensions formula.

Deprecated:: This method does not currently work reliably and will be removed in TensorRT 8.0.

Returns: The formula from computing the pooling output dimensions.

Warning: Custom output dimensions formulas are not supported with wildcard dimensions.

See also: IOutputDimensionsFormula setPoolingOutputDimensionsFormula()

◆ attribute() [9/15]

nvinfer1::INetworkDefinition::__attribute__ ( (deprecated) )

pure virtual

Set the convolution output dimensions formula.

Deprecated:: This method does not currently work reliably and will be removed in TensorRT 8.0.

Parameters

formula The formula from computing the convolution output dimensions. If null is passed, the default formula is used.

The default formula in each dimension is (inputDim + padding * 2 - kernelSize) / stride + 1.

Warning: Custom output dimensions formulas are not supported with wildcard dimensions.

See also: IOutputDimensionsFormula getConvolutionOutputDimensionsFormula()

◆ attribute() [10/15]

nvinfer1::INetworkDefinition::__attribute__ ( (deprecated) ) const &

pure virtual

Get the convolution output dimensions formula.

Deprecated:: This method does not currently work reliably and will be removed in TensorRT 8.0.

Returns: The formula from computing the convolution output dimensions.

Warning: Custom output dimensions formulas are not supported with wildcard dimensions.

See also: IOutputDimensionsFormula setConvolutionOutputDimensionsFormula()

◆ attribute() [11/15]

nvinfer1::INetworkDefinition::__attribute__ ( (deprecated) )

pure virtual

Set the deconvolution output dimensions formula.

Deprecated:: This method does not currently work reliably and will be removed in TensorRT 8.0.

Parameters

formula The formula from computing the deconvolution output dimensions. If null is passed, the default! formula is used.

The default formula in each dimension is (inputDim - 1) * stride + kernelSize - 2 * padding.

Warning: Custom output dimensions formulas are not supported with wildcard dimensions.

See also: IOutputDimensionsFormula getDevonvolutionOutputDimensionsFormula()

◆ attribute() [12/15]

nvinfer1::INetworkDefinition::__attribute__ ( (deprecated) ) const &

pure virtual

Get the deconvolution output dimensions formula.

Returns: The formula from computing the deconvolution output dimensions.

Deprecated:: This method does not currently work reliably and will be removed in TensorRT 8.0.

Warning: Custom output dimensions formulas are not supported with wildcard dimensions.

See also: IOutputDimensionsFormula setDeconvolutionOutputDimensionsFormula()

◆ getNbLayers()

virtual int32_t nvinfer1::INetworkDefinition::getNbLayers ( ) const

pure virtual

Get the number of layers in the network.

Returns: The number of layers in the network.

See also: getLayer()

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◆ getLayer()

virtual ILayer* nvinfer1::INetworkDefinition::getLayer ( int32_t index ) const

pure virtual

Get the layer specified by the given index.

Parameters

index The index of the layer.

Returns: The layer, or nullptr if the index is out of range.

See also: getNbLayers()

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◆ getNbInputs()

virtual int32_t nvinfer1::INetworkDefinition::getNbInputs ( ) const

pure virtual

Get the number of inputs in the network.

Returns: The number of inputs in the network.

See also: getInput()

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◆ getInput()

virtual ITensor* nvinfer1::INetworkDefinition::getInput ( int32_t index ) const

pure virtual

Get the input tensor specified by the given index.

Parameters

index The index of the input tensor.

Returns: The input tensor, or nullptr if the index is out of range.

See also: getNbInputs()

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◆ getNbOutputs()

virtual int32_t nvinfer1::INetworkDefinition::getNbOutputs ( ) const

pure virtual

Get the number of outputs in the network.

The outputs include those marked by markOutput or markOutputForShapes.

Returns: The number of outputs in the network.

See also: getOutput()

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◆ getOutput()

virtual ITensor* nvinfer1::INetworkDefinition::getOutput ( int32_t index ) const

pure virtual

Get the output tensor specified by the given index.

Parameters

index The index of the output tensor.

Returns: The output tensor, or nullptr if the index is out of range.

See also: getNbOutputs()

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◆ destroy()

virtual void nvinfer1::INetworkDefinition::destroy ( )

pure virtual

Destroy this INetworkDefinition object.

◆ addReduce()

virtual IReduceLayer* nvinfer1::INetworkDefinition::addReduce	(	ITensor &	input,
		ReduceOperation	operation,
		uint32_t	reduceAxes,
		bool	keepDimensions
	)

pure virtual

Add a reduce layer to the network.

Parameters

input	The input tensor to the layer.
operation	The reduction operation to perform.
reduceAxes	The reduction dimensions. The bit in position i of bitmask reduceAxes corresponds to explicit dimension i if result. E.g., the least significant bit corresponds to the first explicit dimension and the next to least significant bit corresponds to the second explicit dimension.
keepDimensions	The boolean that specifies whether or not to keep the reduced dimensions in the output of the layer.

The reduce layer works by performing an operation specified by operation to reduce the tensor input across the axes specified by reduceAxes.

See also: IReduceLayer

Warning: If output is a shape tensor, ReduceOperation::kAVG is unsupported.

Returns: The new reduce layer, or nullptr if it could not be created.

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◆ addTopK()

virtual ITopKLayer* nvinfer1::INetworkDefinition::addTopK	(	ITensor &	input,
		TopKOperation	op,
		int32_t	k,
		uint32_t	reduceAxes
	)

pure virtual

Add a TopK layer to the network.

The TopK layer has two outputs of the same dimensions. The first contains data values, the second contains index positions for the values. Output values are sorted, largest first for operation kMAX and smallest first for operation kMIN.

Currently only values of K up to 1024 are supported.

Parameters

input	The input tensor to the layer.
op	Operation to perform.
k	Number of elements to keep.
reduceAxes	The reduction dimensions. The bit in position i of bitmask reduceAxes corresponds to explicit dimension i of the result. E.g., the least significant bit corresponds to the first explicit dimension and the next to least significant bit corresponds to the second explicit dimension.

Currently reduceAxes must specify exactly one dimension, and it must be one of the last four dimensions.

See also: ITopKLayer

Warning: Int32 tensors are not valid input tensors.

Returns: The new TopK layer, or nullptr if it could not be created.

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◆ addGather()

virtual IGatherLayer* nvinfer1::INetworkDefinition::addGather	(	ITensor &	data,
		ITensor &	indices,
		int32_t	axis
	)

pure virtual

Add a gather layer to the network.

Parameters

data	The tensor to gather values from.
indices	The tensor to get indices from to populate the output tensor.
axis	The axis in the data tensor to gather on.

See also: IGatherLayer

Returns: The new gather layer, or nullptr if it could not be created.

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◆ addRaggedSoftMax()

virtual IRaggedSoftMaxLayer* nvinfer1::INetworkDefinition::addRaggedSoftMax	(	ITensor &	input,
		ITensor &	bounds
	)

pure virtual

Add a RaggedSoftMax layer to the network.

Parameters

input	The ZxS input tensor.
bounds	The Zx1 bounds tensor.

See also: IRaggedSoftMaxLayer

Warning: The bounds tensor cannot have the last dimension be the wildcard character.; Int32 tensors are not valid input tensors.

Returns: The new RaggedSoftMax layer, or nullptr if it could not be created.

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◆ addMatrixMultiply()

virtual IMatrixMultiplyLayer* nvinfer1::INetworkDefinition::addMatrixMultiply	(	ITensor &	input0,
		MatrixOperation	op0,
		ITensor &	input1,
		MatrixOperation	op1
	)

pure virtual

Add a MatrixMultiply layer to the network.

Parameters

input0	The first input tensor (commonly A).
op0	The operation to apply to input0.
input1	The second input tensor (commonly B).
op1	The operation to apply to input1.

See also: IMatrixMultiplyLayer

Warning: Int32 tensors are not valid input tensors.

Returns: The new matrix multiply layer, or nullptr if it could not be created.

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◆ attribute() [13/15]

nvinfer1::INetworkDefinition::__attribute__ ( (deprecated) ) &

Add a MatrixMultiply layer to the network.

Parameters

input0	The first input tensor (commonly A).
transpose0	If true, op(input0)=transpose(input0), else op(input0)=input0.
input1	The second input tensor (commonly B).
transpose1	If true, op(input1)=transpose(input1), else op(input1)=input1.

See also: IMatrixMultiplyLayer

Returns: The new matrix multiply layer, or nullptr if it could not be created.

Warning: Int32 tensors are not valid input tensors.

Deprecated:: This interface is superseded by the overload that replaces bool with MatrixOperation and will be removed in TensorRT 8.0.

◆ addConstant()

virtual IConstantLayer* nvinfer1::INetworkDefinition::addConstant	(	Dims	dimensions,
		Weights	weights
	)

pure virtual

Add a constant layer to the network.

Parameters

dimensions	The dimensions of the constant.
weights	The constant value, represented as weights.

See also: IConstantLayer

Returns: The new constant layer, or nullptr if it could not be created.

If weights.type is DataType::kINT32, the output is a tensor of 32-bit indices. Otherwise the output is a tensor of real values and the output type will be follow TensorRT's normal precision rules.

If tensors in the network have an implicit batch dimension, the constant is broadcast over that dimension.

If a wildcard dimension is used, the volume of the runtime dimensions must equal the number of weights specified.

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◆ attribute() [14/15]

nvinfer1::INetworkDefinition::__attribute__ ( (deprecated) ) &

Add an layerCount deep RNN layer to the network with hiddenSize internal states that can take a batch with fixed or variable sequence lengths.

Parameters

input	The input tensor to the layer (see below).
layerCount	The number of layers in the RNN.
hiddenSize	Size of the internal hidden state for each layer.
maxSeqLen	Maximum sequence length for the input.
op	The type of RNN to execute.

By default, the layer is configured with RNNDirection::kUNIDIRECTION and RNNInputMode::kLINEAR. To change these settings, use IRNNv2Layer::setDirection() and IRNNv2Layer::setInputMode().

Weights and biases for the added layer should be set using IRNNv2Layer::setWeightsForGate() and IRNNv2Layer::setBiasForGate() prior to building an engine using this network.

The input tensors must be of the type DataType::kFLOAT or DataType::kHALF. The layout of the weights is row major and must be the same datatype as the input tensor. weights contain 8 matrices and bias contains 8 vectors.

See IRNNv2Layer::setWeightsForGate() and IRNNv2Layer::setBiasForGate() for details on the required input format for weights and bias.

The input ITensor should contain zero or more index dimensions {N1, ..., Np}, followed by two dimensions, defined as follows:

S_max is the maximum allowed sequence length (number of RNN iterations)
E specifies the embedding length (unless ::kSKIP is set, in which case it should match getHiddenSize()).

By default, all sequences in the input are assumed to be size maxSeqLen. To provide explicit sequence lengths for each input sequence in the batch, use IRNNv2Layer::setSequenceLengths().

The RNN layer outputs up to three tensors.

The first output tensor is the output of the final RNN layer across all timesteps, with dimensions {N1, ..., Np, S_max, H}:

N1..Np are the index dimensions specified by the input tensor
S_max is the maximum allowed sequence length (number of RNN iterations)
H is an output hidden state (equal to getHiddenSize() or 2x getHiddenSize())

The second tensor is the final hidden state of the RNN across all layers, and if the RNN is an LSTM (i.e. getOperation() is ::kLSTM), then the third tensor is the final cell state of the RNN across all layers. Both the second and third output tensors have dimensions {N1, ..., Np, L, H}:

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().

See also: IRNNv2Layer

Deprecated:: Superseded by ILoop::addLoop and will be removed in TensorRT 9.0.

Warning: RNN inputs do not support wildcard dimensions or explicit batch size networks.; Int32 tensors are not valid input tensors, only for sequence lengths.

Returns: The new RNN layer, or nullptr if it could not be created.

◆ attribute() [15/15]

nvinfer1::INetworkDefinition::__attribute__ ( (deprecated) ) const

Add a plugin layer to the network using an IPluginExt interface.

Parameters

inputs	The input tensors to the layer.
nbInputs	The number of input tensors.
plugin	The layer plugin.

See also: IPluginLayer

Deprecated:: Superseded by addPluginV2 and will be removed in TensorRT 8.0.

Warning: Plugin inputs do not support wildcard dimensions or explicit batch size networks.; Int32 tensors are not valid input tensors.

Returns: The new plugin layer, or nullptr if it could not be created.

◆ addIdentity()

virtual IIdentityLayer* nvinfer1::INetworkDefinition::addIdentity ( ITensor & input )

pure virtual

Add an identity layer.

Parameters

input The input tensor to the layer.

See also: IIdentityLayer

Warning: Int32 tensors are not valid input tensors.

Returns: The new identity layer, or nullptr if it could not be created.

◆ removeTensor()

virtual void nvinfer1::INetworkDefinition::removeTensor ( ITensor & tensor )

pure virtual

remove a tensor from the network definition.

Parameters

tensor the tensor to remove

It is illegal to remove a tensor that is the input or output of a layer. if this method is called with such a tensor, a warning will be emitted on the log and the call will be ignored. Its intended use is to remove detached tensors after e.g. concatenating two networks with Layer::setInput().

◆ unmarkOutput()

virtual void nvinfer1::INetworkDefinition::unmarkOutput ( ITensor & tensor )

pure virtual

unmark a tensor as a network output.

Parameters

tensor The tensor to unmark as an output tensor.

see markOutput()

◆ addPluginV2()

virtual IPluginV2Layer* nvinfer1::INetworkDefinition::addPluginV2	(	ITensor const	inputs,
		int32_t	nbInputs,
		IPluginV2 &	plugin
	)

pure virtual

Add a plugin layer to the network using the IPluginV2 interface.

Parameters

inputs	The input tensors to the layer.
nbInputs	The number of input tensors.
plugin	The layer plugin.

See also: IPluginV2Layer

Warning: Dimension wildcard are only supported with IPluginV2DynamicExt or IPluginV2IOExt plugins.; Int32 tensors are not valid input tensors.

Returns: The new plugin layer, or nullptr if it could not be created.

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◆ addSlice()

virtual ISliceLayer* nvinfer1::INetworkDefinition::addSlice	(	ITensor &	input,
		Dims	start,
		Dims	size,
		Dims	stride
	)

pure virtual

Add a slice layer to the network.

Parameters

input	The input tensor to the layer.
start	The start offset
size	The output dimension
stride	The slicing stride

Positive, negative, zero stride values, and combinations of them in different dimensions are allowed.

See also: ISliceLayer

Returns: The new slice layer, or nullptr if it could not be created.

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◆ setName()

virtual void nvinfer1::INetworkDefinition::setName ( const char * name )

pure virtual

Sets the name of the network.

Parameters

name	The name to assign to this network.

Set the name of the network so that it can be associated with a built engine. The name must be a zero delimited C-style string of length no greater than 128 characters. TensorRT makes no use of this string except storing it as part of the engine so that it may be retrieved at runtime. A name unique to the builder will be generated by default.

This method copies the name string.

See also: INetworkDefinition::getName(), ISafeCudaEngine::getName()

Returns: none

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◆ getName()

virtual const char* nvinfer1::INetworkDefinition::getName ( ) const

pure virtual

Returns the name associated with the network.

The memory pointed to by getName() is owned by the INetworkDefinition object.

See also: INetworkDefinition::setName()

Returns: A zero delimited C-style string representing the name of the network.

◆ addShape()

virtual IShapeLayer* nvinfer1::INetworkDefinition::addShape ( ITensor & input )

pure virtual

Add a shape layer to the network.

Parameters

input The input tensor to the layer.

See also: IShapeLayer

Warning: addShape is only supported when hasImplicitBatchDimensions is false.; input to addShape cannot contain wildcard dimension values.

Returns: The new shape layer, or nullptr if it could not be created.

◆ hasImplicitBatchDimension()

virtual bool nvinfer1::INetworkDefinition::hasImplicitBatchDimension ( ) const

pure virtual

Query whether the network was created with an implicit batch dimension.

Returns: True if tensors have implicit batch dimension, false otherwise.

This is a network-wide property. Either all tensors in the network have an implicit batch dimension or none of them do.

hasImplicitBatchDimension() is true if and only if this INetworkDefinition was created with createNetwork() or createNetworkV2() without NetworkDefinitionCreationFlag::kEXPLICIT_BATCH flag.

See also: createNetworkV2

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◆ markOutputForShapes()

virtual bool nvinfer1::INetworkDefinition::markOutputForShapes ( ITensor & tensor )

pure virtual

Enable tensor's value to be computed by IExecutionContext::getShapeBinding.

Returns: True if successful, false if tensor is already marked as an output.

The tensor must be of type DataType::kINT32 and have no more than one dimension.

Warning: The tensor must have dimensions that can be determined to be constants at build time.; It is an error to mark a network input as a shape output.

See also: isShapeBinding(), getShapeBinding()

◆ unmarkOutputForShapes()

virtual bool nvinfer1::INetworkDefinition::unmarkOutputForShapes ( ITensor & tensor )

pure virtual

Undo markOutputForShapes.

Warning: inputs to addShape cannot contain wildcard dimension values.

Returns: True if successful, false if tensor is not marked as an output.

◆ addParametricReLU()

virtual IParametricReLULayer* nvinfer1::INetworkDefinition::addParametricReLU	(	ITensor &	input,
		ITensor &	slope
	)

pure virtualnoexcept

Add a parametric ReLU layer to the network.

Parameters

input	The input tensor to the layer.
slope	The slope tensor to the layer. This tensor should be unidirectionally broadcastable to the input tensor.

See also: IParametricReLULayer

Warning: Int32 tensors are not valid input tensors.

Returns: The new parametric ReLU layer, or nullptr if it could not be created.

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◆ addConvolutionNd()

virtual IConvolutionLayer* nvinfer1::INetworkDefinition::addConvolutionNd	(	ITensor &	input,
		int32_t	nbOutputMaps,
		Dims	kernelSize,
		Weights	kernelWeights,
		Weights	biasWeights
	)

pure virtual

Add a multi-dimension convolution layer to the network.

Parameters

input	The input tensor to the convolution.
nbOutputMaps	The number of output feature maps for the convolution.
kernelSize	The multi-dimensions of the convolution kernel.
kernelWeights	The kernel weights for the convolution.
biasWeights	The optional bias weights for the convolution.

See also: IConvolutionLayer

Warning: It is an error to specify a wildcard value for the 'C' dimension of the input tensor.; Int32 tensors are not valid input tensors.; Only 2D or 3D convolution is supported.

Returns: The new convolution layer, or nullptr if it could not be created.

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◆ addPoolingNd()

virtual IPoolingLayer* nvinfer1::INetworkDefinition::addPoolingNd	(	ITensor &	input,
		PoolingType	type,
		Dims	windowSize
	)

pure virtual

Add a multi-dimension pooling layer to the network.

Parameters

input	The input tensor to the layer.
type	The type of pooling to apply.
windowSize	The size of the pooling window.

See also: IPoolingLayer PoolingType

Warning: Int32 tensors are not valid input tensors.; Only 2D or 3D pooling is supported.

Returns: The new pooling layer, or nullptr if it could not be created.

◆ addDeconvolutionNd()

virtual IDeconvolutionLayer* nvinfer1::INetworkDefinition::addDeconvolutionNd	(	ITensor &	input,
		int32_t	nbOutputMaps,
		Dims	kernelSize,
		Weights	kernelWeights,
		Weights	biasWeights
	)

pure virtual

Add a multi-dimension deconvolution layer to the network.

Parameters

input	The input tensor to the layer.
nbOutputMaps	The number of output feature maps.
kernelSize	The multi-dimensions of the deconvolution kernel.
kernelWeights	The kernel weights for the deconvolution.
biasWeights	The optional bias weights for the deconvolution.

See also: IDeconvolutionLayer

Warning: It is an error to specify a wildcard value for the 'C' dimension of the input tensor.; Int32 tensors are not valid input tensors.; Only 2D or 3D deconvolution is supported.

Returns: The new deconvolution layer, or nullptr if it could not be created.

◆ addScaleNd()

virtual IScaleLayer* nvinfer1::INetworkDefinition::addScaleNd	(	ITensor &	input,
		ScaleMode	mode,
		Weights	shift,
		Weights	scale,
		Weights	power,
		int32_t	channelAxis
	)

pure virtual

Add a multi-dimension scale layer to the network.

Parameters

input	The input tensor to the layer.
mode	The scaling mode.
shift	The shift value.
scale	The scale value.
power	The power value.
channelAxis	The channel axis.

If the weights are available, then the size of weights are dependent on the ScaleMode. For ::kUNIFORM, the number of weights equals 1. For ::kCHANNEL, the number of weights equals the channel dimension. For ::kELEMENTWISE, the number of weights equals the product of all input dimensions at channelAxis and beyond.

For example, if the inputs dimensions are [A,B,C,D,E,F], and channelAxis=2: For ::kUNIFORM, the number of weights is equal to 1. For ::kCHANNEL, the number of weights is C. For ::kELEMENTWISE, the number of weights is C*D*E*F.

See also: IScaleLayer

Warning: Int32 tensors are not valid input tensors.; Only 2D or 3D scale is supported.

Returns: The new Scale layer, or nullptr if it could not be created.

◆ addResize()

virtual IResizeLayer* nvinfer1::INetworkDefinition::addResize ( ITensor & input )

pure virtual

Add a resize layer to the network.

Parameters

input The input tensor to the layer.

See also: IResizeLayer

Warning: Int32 tensors are not valid input tensors.

Returns: The new resize layer, or nullptr if it could not be created.

◆ hasExplicitPrecision()

virtual bool nvinfer1::INetworkDefinition::hasExplicitPrecision ( ) const

pure virtual

True if network is an explicit precision network.

hasExplicitPrecision() is true if and only if this INetworkDefinition was created with createNetworkV2() with NetworkDefinitionCreationFlag::kEXPLICIT_PRECISION set.

See also: createNetworkV2

Returns: True if network has explicit precision, false otherwise.

◆ addLoop()

virtual ILoop* nvinfer1::INetworkDefinition::addLoop ( )

pure virtualnoexcept

Add a loop to the network.

An ILoop provides a way to specify a recurrent subgraph.

Returns: Pointer to ILoop that can be used to add loop boundary layers for the loop, or nullptr if network has an implicit batch dimension or this version of TensorRT does not support loops.

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◆ addSelect()

virtual ISelectLayer* nvinfer1::INetworkDefinition::addSelect	(	ITensor &	condition,
		ITensor &	thenInput,
		ITensor &	elseInput
	)

pure virtual

Add a select layer to the network.

Parameters

condition	The condition tensor to the layer.
thenInput	The "then" input tensor to the layer.
elseInput	The "else" input tensor to the layer.

See also: ISelectLayer

Returns: The new select layer, or nullptr if it could not be created.

◆ addFill()

virtual IFillLayer* nvinfer1::INetworkDefinition::addFill	(	Dims	dimensions,
		FillOperation	op
	)

pure virtualnoexcept

Add a fill layer to the network.

Parameters

dimensions	The output tensor dimensions.
op	The fill operation that the layer applies.

Warning: The dimensions's nbDims must be 1.

See also: IFillLayer

Returns: The new fill layer, or nullptr if it could not be created.

◆ addPaddingNd()

virtual IPaddingLayer* nvinfer1::INetworkDefinition::addPaddingNd	(	ITensor &	input,
		Dims	prePadding,
		Dims	postPadding
	)

pure virtual

Add a padding layer to the network.

Only 2D padding is currently supported.

Parameters

input	The input tensor to the layer.
prePadding	The padding to apply to the start of the tensor.
postPadding	The padding to apply to the end of the tensor.

See also: IPaddingLayer

Returns: The new padding layer, or nullptr if it could not be created.

Member Data Documentation

◆ nbOutputMaps

int32_t nvinfer1::INetworkDefinition::nbOutputMaps

◆ kernelSize

int32_t DimsHW nvinfer1::INetworkDefinition::kernelSize

◆ kernelWeights

int32_t DimsHW Weights nvinfer1::INetworkDefinition::kernelWeights

◆ biasWeights

int32_t DimsHW Weights Weights nvinfer1::INetworkDefinition::biasWeights = 0

◆ type

PoolingType nvinfer1::INetworkDefinition::type

◆ windowSize

PoolingType DimsHW nvinfer1::INetworkDefinition::windowSize = 0

◆ layerCount

int32_t nvinfer1::INetworkDefinition::layerCount

◆ hiddenSize [1/2]

int32_t std::size_t nvinfer1::INetworkDefinition::hiddenSize

◆ maxSeqLen [1/2]

int32_t std::size_t int32_t nvinfer1::INetworkDefinition::maxSeqLen

◆ op [1/2]

int32_t std::size_t int32_t RNNOperation nvinfer1::INetworkDefinition::op

◆ mode

int32_t std::size_t int32_t RNNOperation RNNInputMode nvinfer1::INetworkDefinition::mode

◆ dir

int32_t std::size_t int32_t RNNOperation RNNInputMode RNNDirection nvinfer1::INetworkDefinition::dir

◆ weights

int32_t std::size_t int32_t RNNOperation RNNInputMode RNNDirection Weights nvinfer1::INetworkDefinition::weights

◆ bias

int32_t std::size_t int32_t RNNOperation RNNInputMode RNNDirection Weights Weights nvinfer1::INetworkDefinition::bias = 0

◆ nbInputs

int32_t nvinfer1::INetworkDefinition::nbInputs

◆ plugin [1/2]

int32_t IPlugin& nvinfer1::INetworkDefinition::plugin = 0

◆ prePadding

DimsHW nvinfer1::INetworkDefinition::prePadding

◆ postPadding

DimsHW DimsHW nvinfer1::INetworkDefinition::postPadding = 0

◆ transpose0

bool nvinfer1::INetworkDefinition::transpose0

◆ input1

bool ITensor& nvinfer1::INetworkDefinition::input1

◆ transpose1

bool ITensor bool nvinfer1::INetworkDefinition::transpose1 = 0

◆ hiddenSize [2/2]

int32_t int32_t nvinfer1::INetworkDefinition::hiddenSize

◆ maxSeqLen [2/2]

int32_t int32_t int32_t nvinfer1::INetworkDefinition::maxSeqLen

◆ op [2/2]

int32_t int32_t int32_t RNNOperation nvinfer1::INetworkDefinition::op = 0

◆ plugin [2/2]

int32_t IPluginExt& nvinfer1::INetworkDefinition::plugin = 0

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

NvInfer.h

Public Member Functions

Public Attributes

Protected Member Functions

Detailed Description

Constructor & Destructor Documentation

◆ ~INetworkDefinition()

Member Function Documentation

◆ addInput()

◆ markOutput()

◆ __attribute__() [1/15]

◆ addFullyConnected()

◆ addActivation()

◆ __attribute__() [2/15]

◆ addLRN()

◆ addScale()

◆ addSoftMax()

◆ addConcatenation()

◆ __attribute__() [3/15]

◆ addElementWise()

◆ __attribute__() [4/15]

◆ __attribute__() [5/15]

◆ addUnary()

◆ __attribute__() [6/15]

◆ addShuffle()

◆ __attribute__() [7/15]

◆ __attribute__() [8/15]

◆ __attribute__() [9/15]

◆ __attribute__() [10/15]

◆ __attribute__() [11/15]

◆ __attribute__() [12/15]

◆ getNbLayers()

◆ getLayer()

◆ getNbInputs()

◆ getInput()

◆ getNbOutputs()

◆ getOutput()

◆ destroy()

◆ addReduce()

◆ addTopK()

◆ addGather()

◆ addRaggedSoftMax()

◆ addMatrixMultiply()

◆ __attribute__() [13/15]

◆ addConstant()

◆ __attribute__() [14/15]

◆ __attribute__() [15/15]

◆ addIdentity()

◆ removeTensor()

◆ unmarkOutput()

◆ addPluginV2()

◆ addSlice()

◆ setName()

◆ getName()

◆ addShape()

◆ hasImplicitBatchDimension()

◆ markOutputForShapes()

◆ unmarkOutputForShapes()

◆ addParametricReLU()

◆ addConvolutionNd()

◆ addPoolingNd()

◆ addDeconvolutionNd()

◆ addScaleNd()

◆ addResize()

◆ hasExplicitPrecision()

◆ addLoop()

◆ addSelect()

◆ addFill()

◆ addPaddingNd()

Member Data Documentation

◆ nbOutputMaps

◆ kernelSize

◆ kernelWeights

◆ biasWeights

◆ type

◆ windowSize

◆ layerCount

◆ hiddenSize [1/2]

◆ maxSeqLen [1/2]

◆ op [1/2]

◆ mode

◆ attribute() [1/15]

◆ attribute() [2/15]

◆ attribute() [3/15]

◆ attribute() [4/15]

◆ attribute() [5/15]

◆ attribute() [6/15]

◆ attribute() [7/15]

◆ attribute() [8/15]

◆ attribute() [9/15]

◆ attribute() [10/15]

◆ attribute() [11/15]

◆ attribute() [12/15]

◆ attribute() [13/15]

◆ attribute() [14/15]

◆ attribute() [15/15]