cv::dnn::GRULayer Class Reference

GRU recurrent one-layer. More...

#include <opencv2/dnn/all_layers.hpp>

Collaboration diagram for cv::dnn::GRULayer:

Static Public Member Functions
static Ptr< GRULayer >	create (const LayerParams &params)
Static Public Member Functions inherited from cv::Algorithm
template<typename _Tp >
static Ptr< _Tp >	load (const String &filename, const String &objname=String())
	Loads algorithm from the file.
template<typename _Tp >
static Ptr< _Tp >	loadFromString (const String &strModel, const String &objname=String())
	Loads algorithm from a String.
template<typename _Tp >
static Ptr< _Tp >	read (const FileNode &fn)
	Reads algorithm from the file node.

Additional Inherited Members
Public Member Functions inherited from cv::dnn::Layer
	Layer ()
	Layer (const LayerParams &params)
	Initializes only name, type and blobs fields.
virtual	~Layer ()
virtual bool	alwaysSupportInplace () const
virtual std::ostream &	dump (std::ostream &strm, int indent, bool comma) const
virtual std::ostream &	dumpAttrs (std::ostream &strm, int indent) const
virtual bool	dynamicOutputShapes () const
virtual void	finalize (const std::vector< Mat * > &input, std::vector< Mat > &output)
	Computes and sets internal parameters according to inputs, outputs and blobs.
std::vector< Mat >	finalize (const std::vector< Mat > &inputs)
	This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.
void	finalize (const std::vector< Mat > &inputs, std::vector< Mat > &outputs)
	This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.
virtual void	finalize (InputArrayOfArrays inputs, OutputArrayOfArrays outputs)
	Computes and sets internal parameters according to inputs, outputs and blobs.
virtual void	forward (InputArrayOfArrays inputs, OutputArrayOfArrays outputs, OutputArrayOfArrays internals)
	Given the input blobs, computes the output blobs.
virtual void	forward (std::vector< Mat * > &input, std::vector< Mat > &output, std::vector< Mat > &internals)
	Given the input blobs, computes the output blobs.
void	forward_fallback (InputArrayOfArrays inputs, OutputArrayOfArrays outputs, OutputArrayOfArrays internals)
	Given the input blobs, computes the output blobs.
virtual int64	getFLOPS (const std::vector< MatShape > &inputs, const std::vector< MatShape > &outputs) const
virtual bool	getMemoryShapes (const std::vector< MatShape > &inputs, const int requiredOutputs, std::vector< MatShape > &outputs, std::vector< MatShape > &internals) const
virtual void	getScaleShift (Mat &scale, Mat &shift) const
	Returns parameters of layers with channel-wise multiplication and addition.
virtual void	getScaleZeropoint (float &scale, int &zeropoint) const
	Returns scale and zeropoint of layers.
virtual void	getTypes (const std::vector< MatType > &inputs, const int requiredOutputs, const int requiredInternals, std::vector< MatType > &outputs, std::vector< MatType > &internals) const
virtual Ptr< BackendNode >	initCann (const std::vector< Ptr< BackendWrapper > > &inputs, const std::vector< Ptr< BackendWrapper > > &outputs, const std::vector< Ptr< BackendNode > > &nodes)
	Returns a CANN backend node.
virtual Ptr< BackendNode >	initCUDA (void *context, const std::vector< Ptr< BackendWrapper > > &inputs, const std::vector< Ptr< BackendWrapper > > &outputs)
	Returns a CUDA backend node.
virtual Ptr< BackendNode >	initNgraph (const std::vector< Ptr< BackendWrapper > > &inputs, const std::vector< Ptr< BackendNode > > &nodes)
virtual Ptr< BackendNode >	initTimVX (void *timVxInfo, const std::vector< Ptr< BackendWrapper > > &inputsWrapper, const std::vector< Ptr< BackendWrapper > > &outputsWrapper, bool isLast)
	Returns a TimVX backend node.
virtual Ptr< BackendNode >	initVkCom (const std::vector< Ptr< BackendWrapper > > &inputs, std::vector< Ptr< BackendWrapper > > &outputs)
virtual Ptr< BackendNode >	initWebnn (const std::vector< Ptr< BackendWrapper > > &inputs, const std::vector< Ptr< BackendNode > > &nodes)
virtual int	inputNameToIndex (String inputName)
	Returns index of input blob into the input array.
virtual int	outputNameToIndex (const String &outputName)
	Returns index of output blob in output array.
void	run (const std::vector< Mat > &inputs, std::vector< Mat > &outputs, std::vector< Mat > &internals)
	Allocates layer and computes output.
virtual bool	setActivation (const Ptr< ActivationLayer > &layer)
	Tries to attach to the layer the subsequent activation layer, i.e. do the layer fusion in a partial case.
void	setParamsFrom (const LayerParams &params)
	Initializes only name, type and blobs fields.
virtual std::vector< Ptr< Graph > > *	subgraphs () const
virtual bool	supportBackend (int backendId)
	Ask layer if it support specific backend for doing computations.
virtual bool	tryFuse (Ptr< Layer > &top)
	Try to fuse current layer with a next one.
virtual void	unsetAttached ()
	"Detaches" all the layers, attached to particular layer.
virtual bool	updateMemoryShapes (const std::vector< MatShape > &inputs)
Public Member Functions inherited from cv::Algorithm
	Algorithm ()
virtual	~Algorithm ()
virtual void	clear ()
	Clears the algorithm state.
virtual bool	empty () const
	Returns true if the Algorithm is empty (e.g. in the very beginning or after unsuccessful read.
virtual String	getDefaultName () const
virtual void	read (const FileNode &fn)
	Reads algorithm parameters from a file storage.
virtual void	save (const String &filename) const
virtual void	write (FileStorage &fs) const
	Stores algorithm parameters in a file storage.
void	write (FileStorage &fs, const String &name) const
Public Attributes inherited from cv::dnn::Layer
std::vector< Mat >	blobs
	List of learned parameters must be stored here to allow read them by using Net::getParam().
std::vector< Arg >	inputs
String	name
	Name of the layer instance, can be used for logging or other internal purposes.
void *	netimpl
std::vector< Arg >	outputs
int	preferableTarget
	prefer target for layer forwarding
String	type
	Type name which was used for creating layer by layer factory.
Protected Member Functions inherited from cv::Algorithm
void	writeFormat (FileStorage &fs) const

Detailed Description

GRU recurrent one-layer.

Accepts input sequence and computes the final hidden state for each element in the batch.

• input[0] containing the features of the input sequence. input[0] should have shape [T, N, data_dims] where T is sequence length, N is batch size, data_dims is input size
• output would have shape [T, N, D * hidden_size] where D = 2 if layer is bidirectional otherwise D = 1

Depends on the following attributes:

• hidden_size - Number of neurons in the hidden layer
• direction - RNN could be bidirectional or forward

The final hidden state \( h_t \) computes by the following formulas:

\begin{eqnarray*} r_t = \sigma(W_{ir} x_t + b_{ir} + W_{hr} h_{(t-1)} + b_{hr}) \\ z_t = \sigma(W_{iz} x_t + b_{iz} + W_{hz} h_{(t-1)} + b_{hz}) \\ n_t = \tanh(W_{in} x_t + b_{in} + r_t \odot (W_{hn} h_{(t-1)}+ b_{hn})) \\ h_t = (1 - z_t) \odot n_t + z_t \odot h_{(t-1)} \\ \end{eqnarray*}

Where \(x_t\) is current input, \(h_{(t-1)}\) is previous or initial hidden state.

\(W_{x?}\), \(W_{h?}\) and \(b_{?}\) are learned weights represented as matrices: \(W_{x?} \in R^{N_h \times N_x}\), \(W_{h?} \in R^{N_h \times N_h}\), \(b_? \in R^{N_h}\).

\(\odot\) is per-element multiply operation.

Member Function Documentation

create()

static Ptr< GRULayer > cv::dnn::GRULayer::create ( const LayerParams & params )

static

Creates instance of GRU layer

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

• opencv2/dnn/all_layers.hpp