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

#include <NvInfer.h>

Inheritance diagram for nvinfer1::IConvolutionLayer:

Public Member Functions
void	setNbOutputMaps (int64_t nbOutputMaps) noexcept
	Set the number of output maps for the convolution. More...

int64_t	getNbOutputMaps () const noexcept
	Get the number of output maps for the convolution. More...

void	setNbGroups (int64_t nbGroups) noexcept
	Set the number of groups for a convolution. More...

int64_t	getNbGroups () const noexcept
	Get the number of groups of the convolution. More...

void	setKernelWeights (Weights weights) noexcept
	Set the kernel weights for the convolution. More...

Weights	getKernelWeights () const noexcept
	Get the kernel weights of the convolution. More...

void	setBiasWeights (Weights weights) noexcept
	Set the bias weights for the convolution. More...

Weights	getBiasWeights () const noexcept
	Get the bias weights for the convolution. More...

void	setPrePadding (Dims const &padding) noexcept
	Set the multi-dimension pre-padding of the convolution. More...

Dims	getPrePadding () const noexcept
	Get the pre-padding. More...

void	setPostPadding (Dims const &padding) noexcept
	Set the multi-dimension post-padding of the convolution. More...

Dims	getPostPadding () const noexcept
	Get the post-padding. More...

void	setPaddingMode (PaddingMode paddingMode) noexcept
	Set the padding mode. More...

PaddingMode	getPaddingMode () const noexcept
	Get the padding mode. More...

void	setKernelSizeNd (Dims const &kernelSize) noexcept
	Set the multi-dimension kernel size of the convolution. More...

Dims	getKernelSizeNd () const noexcept
	Get the multi-dimension kernel size of the convolution. More...

void	setStrideNd (Dims const &stride) noexcept
	Set the multi-dimension stride of the convolution. More...

Dims	getStrideNd () const noexcept
	Get the multi-dimension stride of the convolution. More...

void	setPaddingNd (Dims const &padding) noexcept
	Set the multi-dimension padding of the convolution. More...

Dims	getPaddingNd () const noexcept
	Get the multi-dimension padding of the convolution. More...

void	setDilationNd (Dims const &dilation) noexcept
	Set the multi-dimension dilation of the convolution. More...

Dims	getDilationNd () const noexcept
	Get the multi-dimension dilation of the convolution. More...

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

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

void	setName (char const *name) noexcept
	Set the name of a layer. More...

char const *	getName () const noexcept
	Return the name of a layer. More...

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

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

int32_t	getNbOutputs () const noexcept
	Get the number of outputs of a layer. More...

ITensor *	getOutput (int32_t index) const noexcept
	Get the layer output corresponding to the given index. More...

void	setInput (int32_t index, ITensor &tensor) noexcept
	Replace an input of this layer with a specific tensor. More...

void	setPrecision (DataType dataType) noexcept
	Set the preferred or required computational precision of this layer in a weakly-typed network. More...

DataType	getPrecision () const noexcept
	get the computational precision of this layer More...

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

void	resetPrecision () noexcept
	reset the computational precision for this layer More...

void	setOutputType (int32_t index, DataType dataType) noexcept
	Set the output type of this layer in a weakly-typed network. More...

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

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

void	resetOutputType (int32_t index) noexcept
	reset the output type for this layer More...

void	setMetadata (char const *metadata) noexcept
	Set the metadata for this layer. More...

char const *	getMetadata () const noexcept
	Get the metadata of the layer. More...

Protected Member Functions
virtual	~IConvolutionLayer () noexcept=default

Protected Member Functions inherited from nvinfer1::ILayer
virtual	~ILayer () noexcept=default

Protected Member Functions inherited from nvinfer1::INoCopy
	INoCopy ()=default

virtual	~INoCopy ()=default

	INoCopy (INoCopy const &other)=delete

INoCopy &	operator= (INoCopy const &other)=delete

	INoCopy (INoCopy &&other)=delete

INoCopy &	operator= (INoCopy &&other)=delete

Protected Attributes
apiv::VConvolutionLayer *	mImpl

Protected Attributes inherited from nvinfer1::ILayer
apiv::VLayer *	mLayer

Detailed Description

A convolution layer in a network definition.

This layer performs a correlation operation between 3 or 4 dimensional filter with a 4 or 5 dimensional tensor to produce another 4 or 5 dimensional tensor.

An optional bias argument is supported, which adds a per-channel constant to each value in the output.

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

Constructor & Destructor Documentation

◆ ~IConvolutionLayer()

virtual nvinfer1::IConvolutionLayer::~IConvolutionLayer ( )

protectedvirtualdefaultnoexcept

Member Function Documentation

◆ getBiasWeights()

Weights nvinfer1::IConvolutionLayer::getBiasWeights ( ) const

inlinenoexcept

Get the bias weights for the convolution.

See also: setBiasWeights()

◆ getDilationNd()

Dims nvinfer1::IConvolutionLayer::getDilationNd ( ) const

inlinenoexcept

Get the multi-dimension dilation of the convolution.

See also: setDilationNd()

◆ getKernelSizeNd()

Dims nvinfer1::IConvolutionLayer::getKernelSizeNd ( ) const

inlinenoexcept

Get the multi-dimension kernel size of the convolution.

See also: setKernelSizeNd()

◆ getKernelWeights()

Weights nvinfer1::IConvolutionLayer::getKernelWeights ( ) const

inlinenoexcept

Get the kernel weights of the convolution.

See also: setKernelWeights()

◆ getNbGroups()

int64_t nvinfer1::IConvolutionLayer::getNbGroups ( ) const

inlinenoexcept

Get the number of groups of the convolution.

See also: setNbGroups()

◆ getNbOutputMaps()

int64_t nvinfer1::IConvolutionLayer::getNbOutputMaps ( ) const

inlinenoexcept

Get the number of output maps for the convolution.

See also: setNbOutputMaps()

◆ getPaddingMode()

PaddingMode nvinfer1::IConvolutionLayer::getPaddingMode ( ) const

inlinenoexcept

Get the padding mode.

Default: kEXPLICIT_ROUND_DOWN

See also: setPaddingMode()

◆ getPaddingNd()

Dims nvinfer1::IConvolutionLayer::getPaddingNd ( ) const

inlinenoexcept

Get the multi-dimension padding of the convolution.

If the padding is asymmetric, the pre-padding is returned.

See also: setPaddingNd()

◆ getPostPadding()

Dims nvinfer1::IConvolutionLayer::getPostPadding ( ) const

inlinenoexcept

Get the post-padding.

See also: setPostPadding()

◆ getPrePadding()

Dims nvinfer1::IConvolutionLayer::getPrePadding ( ) const

inlinenoexcept

Get the pre-padding.

See also: setPrePadding()

◆ getStrideNd()

Dims nvinfer1::IConvolutionLayer::getStrideNd ( ) const

inlinenoexcept

Get the multi-dimension stride of the convolution.

See also: setStrideNd()

◆ setBiasWeights()

void nvinfer1::IConvolutionLayer::setBiasWeights ( Weights weights )

inlinenoexcept

Set the bias weights for the convolution.

Bias is optional. To omit bias, set the count value of the weights structure to zero.

The bias is applied per-channel, so the number of weights (if non-zero) must be equal to the number of output feature maps.

See also: getBiasWeights()

◆ setDilationNd()

void nvinfer1::IConvolutionLayer::setDilationNd ( Dims const & dilation )

inlinenoexcept

Set the multi-dimension dilation of the convolution.

Default: (1, 1, ..., 1)

If executing this layer on DLA, only support 2D padding, both height and width must be in the range [1,32].

See also: getDilationNd()

◆ setInput()

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

inlinenoexcept

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

The indices are as follows:

Input 0 is the input activation tensor. Input 1 is the kernel tensor. If used, the kernel weights parameter must be set to empty weights. Input 2 is the bias tensor. If used, the bias parameter must be set to empty weights.

See also: getKernelWeights(), setKernelWeights(), getBiasWeights(), setBiasWeights()

◆ setKernelSizeNd()

void nvinfer1::IConvolutionLayer::setKernelSizeNd ( Dims const & kernelSize )

inlinenoexcept

Set the multi-dimension kernel size of the convolution.

If executing this layer on DLA, only support 2D kernel size, both height and width of kernel size must be in the range [1,32].

See also: getKernelSizeNd()

◆ setKernelWeights()

void nvinfer1::IConvolutionLayer::setKernelWeights ( Weights weights )

inlinenoexcept

Set the kernel weights for the convolution.

The weights are specified as a contiguous array in GKCRS order, where G is the number of groups, K the number of output feature maps, C the number of input channels, and R and S are the height and width of the filter.

See also: getKernelWeights()

◆ setNbGroups()

void nvinfer1::IConvolutionLayer::setNbGroups ( int64_t nbGroups )

inlinenoexcept

Set the number of groups for a convolution.

The input tensor channels are divided into nbGroups groups, and a convolution is executed for each group, using a filter per group. The results of the group convolutions are concatenated to form the output.

Note: When using groups in int8 mode, the size of the groups (i.e. the channel count divided by the group count) must be a multiple of 4 for both input and output.

Default: 1

If executing this layer on DLA, the max number of groups is 8192.

See also: getNbGroups()

◆ setNbOutputMaps()

void nvinfer1::IConvolutionLayer::setNbOutputMaps ( int64_t nbOutputMaps )

inlinenoexcept

Set the number of output maps for the convolution.

If executing this layer on DLA, the number of output maps must be in the range [1,8192].

See also: getNbOutputMaps()

◆ setPaddingMode()

void nvinfer1::IConvolutionLayer::setPaddingMode ( PaddingMode paddingMode )

inlinenoexcept

Set the padding mode.

Padding mode takes precedence if both setPaddingMode and setPre/PostPadding are used.

Default: kEXPLICIT_ROUND_DOWN

See also: getPaddingMode()

◆ setPaddingNd()

void nvinfer1::IConvolutionLayer::setPaddingNd ( Dims const & padding )

inlinenoexcept

Set the multi-dimension padding of the convolution.

The input will be zero-padded by this number of elements in each dimension. Padding is symmetric.

Default: (0, 0, ..., 0)

If executing this layer on DLA, only support 2D padding, both height and width of padding must be in the range [0,31], and the padding must be less than the kernel size.

See also: getPaddingNd() setPadding() getPadding()

◆ setPostPadding()

void nvinfer1::IConvolutionLayer::setPostPadding ( Dims const & padding )

inlinenoexcept

Set the multi-dimension post-padding of the convolution.

The end of the input will be zero-padded by this number of elements in each dimension.

Default: (0, 0, ..., 0)

If executing this layer on DLA, only support 2D padding, both height and width of padding must be in the range [0,31], and the padding must be less than the kernel size.

See also: getPostPadding()

◆ setPrePadding()

void nvinfer1::IConvolutionLayer::setPrePadding ( Dims const & padding )

inlinenoexcept

Set the multi-dimension pre-padding of the convolution.

The start of the input will be zero-padded by this number of elements in each dimension.

Default: (0, 0, ..., 0)

If executing this layer on DLA, only support 2D padding, both height and width of padding must be in the range [0,31], and the padding must be less than the kernel size.

See also: getPrePadding()

◆ setStrideNd()

void nvinfer1::IConvolutionLayer::setStrideNd ( Dims const & stride )

inlinenoexcept

Set the multi-dimension stride of the convolution.

Default: (1, 1, ..., 1)

If executing this layer on DLA, only support 2D stride, both height and width of stride must be in the range [1,8].

See also: getStrideNd()

Member Data Documentation

◆ mImpl

apiv::VConvolutionLayer* nvinfer1::IConvolutionLayer::mImpl

protected

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

NvInfer.h

Public Member Functions

Protected Member Functions

Protected Attributes

Detailed Description

Constructor & Destructor Documentation

◆ ~IConvolutionLayer()

Member Function Documentation

◆ getBiasWeights()

◆ getDilationNd()

◆ getKernelSizeNd()

◆ getKernelWeights()

◆ getNbGroups()

◆ getNbOutputMaps()

◆ getPaddingMode()

◆ getPaddingNd()

◆ getPostPadding()

◆ getPrePadding()

◆ getStrideNd()

◆ setBiasWeights()

◆ setDilationNd()

◆ setInput()

◆ setKernelSizeNd()

◆ setKernelWeights()

◆ setNbGroups()

◆ setNbOutputMaps()

◆ setPaddingMode()

◆ setPaddingNd()

◆ setPostPadding()

◆ setPrePadding()

◆ setStrideNd()

Member Data Documentation

◆ mImpl