A resize layer in a network definition. More...

Inheritance diagram for nvinfer1::IResizeLayer:

Collaboration diagram for nvinfer1::IResizeLayer:

Public Member Functions
virtual void	setOutputDimensions (Dims dimensions)=0
	Set the output dimensions. More...

virtual Dims	getOutputDimensions () const =0
	Get the output dimensions. More...

virtual void	setScales (const float *scales, int32_t nbScales)=0
	Set the resize scales. More...

virtual int32_t	getScales (int32_t size, float *scales) const =0
	Copies resize scales to scales[0, ..., nbScales-1], where nbScales is the number of scales that were set. More...

virtual void	setResizeMode (ResizeMode resizeMode)=0
	Set resize mode for an input tensor. More...

virtual ResizeMode	getResizeMode () const =0
	Get resize mode for an input tensor. More...

virtual void	setAlignCorners (bool alignCorners)=0
	Set whether to align corners while resizing. More...

virtual bool	getAlignCorners () const =0
	True if align corners has been set. More...

void	setInput (int32_t index, ITensor &tensor)=0
	Append or replace an input of this layer with a specific tensor. More...

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 int32_t	getNbInputs () const =0
	Get the number of inputs of a layer. More...

virtual ITensor *	getInput (int32_t index) const =0
	Get the layer input corresponding to the given index. More...

virtual int32_t	getNbOutputs () const =0
	Get the number of outputs of a layer. More...

virtual ITensor *	getOutput (int32_t index) const =0
	Get the layer output corresponding to the given index. 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 (int32_t index, DataType dataType)=0
	Set the output type of this layer. More...

virtual DataType	getOutputType (int32_t index) const =0
	get the output type of this layer More...

virtual bool	outputTypeIsSet (int32_t index) const =0
	whether the output type has been set for this layer More...

virtual void	resetOutputType (int32_t index)=0
	reset the output type for this layer More...

Protected Member Functions
virtual	~IResizeLayer ()

Detailed Description

A resize layer in a network definition.

Resize layer can be used for resizing a ND tensor.

Resize layer currently supports the following configurations:

ResizeMode::kNEAREST - resizes innermost m dimensions of ND, where 0 < m <= min(8, N) and N > 0
ResizeMode::kLINEAR - resizes innermost m dimensions of ND, where 0 < m <= min(3, N) and N > 0

Default resize mode is ResizeMode::kNEAREST. Resize layer provides two ways to resize tensor dimensions.

Set output dimensions directly. It can be done for static as well as dynamic resize layer. Static resize layer requires output dimensions to be known at build-time. Dynamic resize layer requires output dimensions to be set as one of the input tensors.
Set scales for resize. Each output dimension is calculated as floor(input dimension * scale). Only static resize layer allows setting scales where the scales are known at build-time.

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

Constructor & Destructor Documentation

◆ ~IResizeLayer()

virtual nvinfer1::IResizeLayer::~IResizeLayer ( )

inlineprotectedvirtual

Member Function Documentation

◆ setOutputDimensions()

virtual void nvinfer1::IResizeLayer::setOutputDimensions ( Dims dimensions )

pure virtual

Set the output dimensions.

Parameters

dimensions The output dimensions. Number of output dimensions must be the same as the number of input dimensions.

If there is a second input, i.e. resize layer is dynamic, calling setOutputDimensions() is an error and does not update the dimensions.

Output dimensions can be specified directly, or via scale factors relative to input dimensions. Scales for resize can be provided using setScales().

See also: setScales; getOutputDimensions

◆ getOutputDimensions()

virtual Dims nvinfer1::IResizeLayer::getOutputDimensions ( ) const

pure virtual

Get the output dimensions.

Returns: The output dimensions.

◆ setScales()

virtual void nvinfer1::IResizeLayer::setScales	(	const float *	scales,
		int32_t	nbScales
	)

pure virtual

Set the resize scales.

Parameters

scales	An array of resize scales.
nbScales	Number of scales. Number of scales must be equal to the number of input dimensions.

If there is a second input, i.e. resize layer is dynamic, calling setScales() is an error and does not update the scales.

Output dimensions are calculated as follows: outputDims[i] = floor(inputDims[i] * scales[i])

Output dimensions can be specified directly, or via scale factors relative to input dimensions. Output dimensions can be provided directly using setOutputDimensions().

See also: setOutputDimensions; getScales

◆ getScales()

virtual int32_t nvinfer1::IResizeLayer::getScales	(	int32_t	size,
		float *	scales
	)		const

pure virtual

Copies resize scales to scales[0, ..., nbScales-1], where nbScales is the number of scales that were set.

Parameters

size	The number of scales to get. If size != nbScales, no scales will be copied.
scales	Pointer to where to copy the scales. Scales will be copied only if size == nbScales and scales != nullptr.

In case the size is not known consider using size = 0 and scales = nullptr. This method will return the number of resize scales.

Returns: The number of resize scales i.e. nbScales if scales were set. Return -1 in case no scales were set or resize layer is used in dynamic mode.

◆ setResizeMode()

virtual void nvinfer1::IResizeLayer::setResizeMode ( ResizeMode resizeMode )

pure virtual

Set resize mode for an input tensor.

Supported resize modes are Nearest Neighbor and Linear.

See also: ResizeMode

◆ getResizeMode()

virtual ResizeMode nvinfer1::IResizeLayer::getResizeMode ( ) const

pure virtual

Get resize mode for an input tensor.

Returns: The resize mode.

◆ setAlignCorners()

virtual void nvinfer1::IResizeLayer::setAlignCorners ( bool alignCorners )

pure virtual

Set whether to align corners while resizing.

If true, the centers of the 4 corner pixels of both input and output tensors are aligned i.e. preserves the values of corner pixels.

Default: false.

◆ getAlignCorners()

virtual bool nvinfer1::IResizeLayer::getAlignCorners ( ) const

pure virtual

True if align corners has been set.

Returns: True if align corners has been set, false otherwise.

◆ setInput()

void nvinfer1::IResizeLayer::setInput	(	int32_t	index,
		ITensor &	tensor
	)

pure virtual

Append or replace an input of this layer with a specific tensor.

Parameters

index	the index of the input to modify.
tensor	the new input tensor

Sets the input tensor for the given index. The index must be 0 for a static resize layer. A static resize layer is converted to a dynamic resize layer by calling setInput with an index 1. A dynamic resize layer cannot be converted back to a static resize layer.

For a dynamic resize layer, the values 0 and 1 are valid. The indices in the dynamic case are as follows:

0: Data or Shape tensor to be resized.
1: The output dimensions, as a 1D Int32 shape tensor.

If this function is called with a value 1, then the function getNbInputs() changes from returning 1 to 2.

Implements nvinfer1::ILayer.

◆ getType()

virtual LayerType nvinfer1::ILayer::getType ( ) const

pure virtualinherited

Return the type of a layer.

See also: LayerType

◆ setName()

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

pure virtualinherited

Set the name of a layer.

This method copies the name string.

See also: getName()

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◆ getName()

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

pure virtualinherited

Return the name of a layer.

See also: setName()

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◆ getNbInputs()

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

pure virtualinherited

Get the number of inputs of a layer.

◆ getInput()

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

pure virtualinherited

Get the layer input corresponding to the given index.

Parameters

index The index of the input tensor.

Returns: The input tensor, or nullptr if the index is out of range or the tensor is optional (ISliceLayer, IRNNLayer and IRNNv2Layer).

◆ getNbOutputs()

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

pure virtualinherited

Get the number of outputs of a layer.

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◆ getOutput()

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

pure virtualinherited

Get the layer output corresponding to the given index.

Returns: The indexed output tensor, or nullptr if the index is out of range or the tensor is optional (IRNNLayer and IRNNv2Layer).

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◆ setPrecision()

virtual void nvinfer1::ILayer::setPrecision ( DataType dataType )

pure virtualinherited

Set the computational precision of this layer.

Setting the precision allows TensorRT to choose implementation which run at this computational precision. Layer input type would also get inferred from layer computational precision. TensorRT could still choose a non-conforming fastest implementation ignoring set layer precision. Use BuilderFlag::kSTRICT_TYPES to force choose implementations with requested precision. In case no implementation is found with requested precision, TensorRT would choose available fastest implementation. If precision is not set, TensorRT will select the layer computational precision and layer input type based on performance considerations and the flags specified to the builder.

Parameters

precision the computational precision.

See also: getPrecision() precisionIsSet() resetPrecision()

◆ getPrecision()

virtual DataType nvinfer1::ILayer::getPrecision ( ) const

pure virtualinherited

get the computational precision of this layer

Returns: the computational precision

See also: setPrecision() precisionIsSet() resetPrecision()

◆ precisionIsSet()

virtual bool nvinfer1::ILayer::precisionIsSet ( ) const

pure virtualinherited

whether the computational precision has been set for this layer

Returns: whether the computational precision has been explicitly set

See also: setPrecision() getPrecision() resetPrecision()

◆ resetPrecision()

virtual void nvinfer1::ILayer::resetPrecision ( )

pure virtualinherited

reset the computational precision for this layer

See also: setPrecision() getPrecision() precisionIsSet()

◆ setOutputType()

virtual void nvinfer1::ILayer::setOutputType	(	int32_t	index,
		DataType	dataType
	)

pure virtualinherited

Set the output type of this layer.

Setting the output type constrains TensorRT to choose implementations which generate output data with the given type. If it is not set, TensorRT will select output type based on layer computational precision. TensorRT could still choose non-conforming output type based on fastest implementation. Use BuilderFlag::kSTRICT_TYPES to force choose requested output type. In case layer precision is not specified, output type would depend on chosen implementation based on performance considerations and the flags specified to the builder.

This method cannot be used to set the data type of the second output tensor of the TopK layer. The data type of the second output tensor of the topK layer is always Int32. Also the output type of all layers that are shape operations must be DataType::kINT32, and all attempts to set the output type to some other data type will be ignored except for issuing an error message.

Note that the layer output type is generally not identical to the data type of the output tensor, as TensorRT may insert implicit reformatting operations to convert the former to the latter. Calling layer->setOutputType(i, type) has no effect on the data type of the i-th output tensor of layer, and users need to call layer->getOutput(i)->setType(type) to change the tensor data type. This is particularly relevant if the tensor is marked as a network output, since only setType() [but not setOutputType()] will affect the data representation in the corresponding output binding.

Parameters

index	the index of the output to set
dataType	the type of the output

See also: getOutputType() outputTypeIsSet() resetOutputType()

◆ getOutputType()

virtual DataType nvinfer1::ILayer::getOutputType ( int32_t index ) const

pure virtualinherited

get the output type of this layer

Parameters

index the index of the output

Returns: the output precision. If no precision has been set, DataType::kFLOAT will be returned, unless the output type is inherently DataType::kINT32.

See also: getOutputType() outputTypeIsSet() resetOutputType()

◆ outputTypeIsSet()

virtual bool nvinfer1::ILayer::outputTypeIsSet ( int32_t index ) const

pure virtualinherited

whether the output type has been set for this layer

Parameters

index the index of the output

Returns: whether the output type has been explicitly set

See also: setOutputType() getOutputType() resetOutputType()

◆ resetOutputType()

virtual void nvinfer1::ILayer::resetOutputType ( int32_t index )

pure virtualinherited

reset the output type for this layer

Parameters

index the index of the output

See also: setOutputType() getOutputType() outputTypeIsSet()

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

NvInfer.h

Public Member Functions

Protected Member Functions

Detailed Description

Constructor & Destructor Documentation

◆ ~IResizeLayer()

Member Function Documentation

◆ setOutputDimensions()

◆ getOutputDimensions()

◆ setScales()

◆ getScales()

◆ setResizeMode()

◆ getResizeMode()

◆ setAlignCorners()

◆ getAlignCorners()

◆ setInput()

◆ getType()

◆ setName()

◆ getName()

◆ getNbInputs()

◆ getInput()

◆ getNbOutputs()

◆ getOutput()

◆ setPrecision()

◆ getPrecision()

◆ precisionIsSet()

◆ resetPrecision()

◆ setOutputType()

◆ getOutputType()

◆ outputTypeIsSet()

◆ resetOutputType()