nvinfer1::IFullyConnectedLayer Class Reference

A fully connected layer in a network definition. This layer expects an input tensor of three or more non-batch dimensions. The input is automatically reshaped into an MxV tensor X, where V is a product of the last three dimensions and M is a product of the remaining dimensions (where the product over 0 dimensions is defined as 1). For example: More...

#include <NvInfer.h>

Inheritance diagram for nvinfer1::IFullyConnectedLayer:

Public Member Functions
void	setNbOutputChannels (int32_t nbOutputs) noexcept
	Set the number of output channels K from the fully connected layer. More...
int32_t	getNbOutputChannels () const noexcept
	Get the number of output channels K from the fully connected layer. More...
void	setKernelWeights (Weights weights) noexcept
	Set the kernel weights, given as a KxC matrix in row-major order. More...
Weights	getKernelWeights () const noexcept
	Get the kernel weights. More...
void	setBiasWeights (Weights weights) noexcept
	Set the bias weights. More...
Weights	getBiasWeights () const noexcept
	Get the bias weights. 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 (const char *name) noexcept
	Set the name of a layer. More...
const char *	getName () const noexcept
	Return the name of a layer. More...
int32_t	getNbInputs () const noexcept
	Get the number of inputs of a layer.
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.
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 computational precision of this layer. 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. 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...

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

Additional Inherited Members
Protected Member Functions inherited from nvinfer1::INoCopy
	INoCopy (const INoCopy &other)=delete
INoCopy &	operator= (const INoCopy &other)=delete
	INoCopy (INoCopy &&other)=delete
INoCopy &	operator= (INoCopy &&other)=delete

Detailed Description

A fully connected layer in a network definition. This layer expects an input tensor of three or more non-batch dimensions. The input is automatically reshaped into an MxV tensor X, where V is a product of the last three dimensions and M is a product of the remaining dimensions (where the product over 0 dimensions is defined as 1). For example:

• If the input tensor has shape {C, H, W}, then the tensor is reshaped into {1, C*H*W}.
• If the input tensor has shape {P, C, H, W}, then the tensor is reshaped into {P, C*H*W}.

The layer then performs the following operation:

Y := matmul(X, W^T) + bias

Where X is the MxV tensor defined above, W is the KxV weight tensor of the layer, and bias is a row vector size K that is broadcasted to MxK. K is the number of output channels, and configurable via setNbOutputChannels(). If bias is not specified, it is implicitly 0.

The MxK result Y is then reshaped such that the last three dimensions are {K, 1, 1} and the remaining dimensions match the dimensions of the input tensor. For example:

• If the input tensor has shape {C, H, W}, then the output tensor will have shape {K, 1, 1}.
• If the input tensor has shape {P, C, H, W}, then the output tensor will have shape {P, K, 1, 1}.

Warning

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

Member Function Documentation

getBiasWeights()

Weights nvinfer1::IFullyConnectedLayer::getBiasWeights ( ) const

inlinenoexcept

Get the bias weights.

See also

setBiasWeightsWeights()

getKernelWeights()

Weights nvinfer1::IFullyConnectedLayer::getKernelWeights ( ) const

inlinenoexcept

Get the kernel weights.

See also

setKernelWeights()

getNbOutputChannels()

int32_t nvinfer1::IFullyConnectedLayer::getNbOutputChannels ( ) const

inlinenoexcept

Get the number of output channels K from the fully connected layer.

See also

setNbOutputChannels()

setBiasWeights()

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

inlinenoexcept

Set the bias weights.

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

See also

getBiasWeightsWeights()

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

Only index 0 (data input) is valid, unless explicit-quantization mode is enabled. In explicit-quantization mode, input with index 1 is the kernel-weights tensor, if present. The kernel-weights tensor must be a build-time constant (computable at build-time via constant-folding) and an output of a dequantize layer. If input index 1 is used then the kernel-weights parameter must be set to empty Weights.

See also

getKernelWeights(), setKernelWeights()

The indices are as follows:

• 0: The input activation tensor.
• 1: The kernel weights tensor (a constant tensor).

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

setKernelWeights()

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

inlinenoexcept

Set the kernel weights, given as a KxC matrix in row-major order.

See also

getKernelWeights()

setNbOutputChannels()

void nvinfer1::IFullyConnectedLayer::setNbOutputChannels ( int32_t  nbOutputs )

inlinenoexcept

Set the number of output channels K from the fully connected layer.

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

See also

getNbOutputChannels()

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

• NvInfer.h