OpenCV  4.10.0-dev
Open Source Computer Vision
Loading...
Searching...
No Matches

GRU recurrent one-layer. More...

#include <opencv2/dnn/all_layers.hpp>

Collaboration diagram for cv::dnn::GRULayer:

Static Public Member Functions

static Ptr< GRULayercreate (const LayerParams &params)
 
- Static Public Member Functions inherited from cv::Algorithm
template<typename _Tp >
static Ptr< _Tpload (const String &filename, const String &objname=String())
 Loads algorithm from the file.
 
template<typename _Tp >
static Ptr< _TploadFromString (const String &strModel, const String &objname=String())
 Loads algorithm from a String.
 
template<typename _Tp >
static Ptr< _Tpread (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 void applyHalideScheduler (Ptr< BackendNode > &node, const std::vector< Mat * > &inputs, const std::vector< Mat > &outputs, int targetId) const
 Automatic Halide scheduling based on layer hyper-parameters.
 
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< Matfinalize (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 Ptr< BackendNodeinitCann (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< BackendNodeinitCUDA (void *context, const std::vector< Ptr< BackendWrapper > > &inputs, const std::vector< Ptr< BackendWrapper > > &outputs)
 Returns a CUDA backend node.
 
virtual Ptr< BackendNodeinitHalide (const std::vector< Ptr< BackendWrapper > > &inputs)
 Returns Halide backend node.
 
virtual Ptr< BackendNodeinitNgraph (const std::vector< Ptr< BackendWrapper > > &inputs, const std::vector< Ptr< BackendNode > > &nodes)
 
virtual Ptr< BackendNodeinitTimVX (void *timVxInfo, const std::vector< Ptr< BackendWrapper > > &inputsWrapper, const std::vector< Ptr< BackendWrapper > > &outputsWrapper, bool isLast)
 Returns a TimVX backend node.
 
virtual Ptr< BackendNodeinitVkCom (const std::vector< Ptr< BackendWrapper > > &inputs, std::vector< Ptr< BackendWrapper > > &outputs)
 
virtual Ptr< BackendNodeinitWebnn (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 bool supportBackend (int backendId)
 Ask layer if it support specific backend for doing computations.
 
virtual Ptr< BackendNodetryAttach (const Ptr< BackendNode > &node)
 Implement layers fusing.
 
virtual bool tryFuse (Ptr< Layer > &top)
 Try to fuse current layer with a next one.
 
virtual bool tryQuantize (const std::vector< std::vector< float > > &scales, const std::vector< std::vector< int > > &zeropoints, LayerParams &params)
 Tries to quantize the given layer and compute the quantization parameters required for fixed point implementation.
 
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
 
void write (const Ptr< FileStorage > &fs, const String &name=String()) 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< Matblobs
 List of learned parameters must be stored here to allow read them by using Net::getParam().
 
String name
 Name of the layer instance, can be used for logging or other internal purposes.
 
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


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