OpenCV
Open Source Computer Vision
|
LSTM recurrent layer. More...
#include <opencv2/dnn/all_layers.hpp>
Public Member Functions | |
int | inputNameToIndex (String inputName) CV_OVERRIDE |
Returns index of input blob into the input array. | |
int | outputNameToIndex (const String &outputName) CV_OVERRIDE |
Returns index of output blob in output array. | |
virtual void | setOutShape (const MatShape &outTailShape=MatShape())=0 |
Specifies shape of output blob which will be [[T ], N ] + outTailShape . | |
virtual void | setProduceCellOutput (bool produce=false)=0 |
If this flag is set to true then layer will produce | |
virtual void | setUseTimstampsDim (bool use=true)=0 |
Specifies either interpret first dimension of input blob as timestamp dimension either as sample. | |
virtual void | setWeights (const Mat &Wh, const Mat &Wx, const Mat &b)=0 |
Set trained weights for LSTM layer. | |
Public Member Functions inherited from cv::dnn::Layer | |
Layer () | |
Layer (const LayerParams ¶ms) | |
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< 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 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 > | initHalide (const std::vector< Ptr< BackendWrapper > > &inputs) |
Returns Halide 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) |
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 ¶ms) |
Initializes only name, type and blobs fields. | |
virtual bool | supportBackend (int backendId) |
Ask layer if it support specific backend for doing computations. | |
virtual Ptr< BackendNode > | tryAttach (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 ¶ms) |
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 |
Static Public Member Functions | |
static Ptr< LSTMLayer > | create (const LayerParams ¶ms) |
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 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(). | |
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 |
LSTM recurrent layer.
|
static |
Creates instance of LSTM layer
|
virtual |
Returns index of input blob into the input array.
inputName | label of input blob |
Each layer input and output can be labeled to easily identify them using "%<layer_name%>[.output_name]" notation. This method maps label of input blob to its index into input vector.
Reimplemented from cv::dnn::Layer.
|
virtual |
Returns index of output blob in output array.
Reimplemented from cv::dnn::Layer.
|
pure virtual |
Specifies shape of output blob which will be [[T
], N
] + outTailShape
.
If this parameter is empty or unset then outTailShape
= [Wh
.size(0)] will be used, where Wh
is parameter from setWeights().
|
pure virtual |
If this flag is set to true then layer will produce
use_timestamp_dim
in LayerParams. Shape of the second output is the same as first output.
|
pure virtual |
Specifies either interpret first dimension of input blob as timestamp dimension either as sample.
produce_cell_output
in LayerParams. If flag is set to true then shape of input blob will be interpreted as [T
, N
, [data dims]
] where T
specifies number of timestamps, N
is number of independent streams. In this case each forward() call will iterate through T
timestamps and update layer's state T
times.
If flag is set to false then shape of input blob will be interpreted as [N
, [data dims]
]. In this case each forward() call will make one iteration and produce one timestamp with shape [N
, [out dims]
].
|
pure virtual |
Set trained weights for LSTM layer.
LSTM behavior on each step is defined by current input, previous output, previous cell state and learned weights.
Let
where
Gates are computed as follows:
where
For simplicity and performance purposes we use
Wh | is matrix defining how previous output is transformed to internal gates (i.e. according to above mentioned notation is |
Wx | is matrix defining how current input is transformed to internal gates (i.e. according to above mentioned notation is |
b | is bias vector (i.e. according to above mentioned notation is |