This interface class allows to build new Layers - are building blocks of networks. More...

#include <opencv2/dnn/dnn.hpp>

Inheritance diagram for cv::dnn::Layer:

Public Member Functions
	Layer ()

	Layer (const LayerParams &params)
	Initializes only name, type and blobs fields. More...

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. More...

virtual void	finalize (const std::vector< Mat *> &input, std::vector< Mat > &output)
	Computes and sets internal parameters according to inputs, outputs and blobs. More...

virtual void	finalize (InputArrayOfArrays inputs, OutputArrayOfArrays outputs)
	Computes and sets internal parameters according to inputs, outputs and blobs. More...

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. More...

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. More...

virtual void	forward (std::vector< Mat *> &input, std::vector< Mat > &output, std::vector< Mat > &internals)
	Given the `input` blobs, computes the output `blobs`. More...

virtual void	forward (InputArrayOfArrays inputs, OutputArrayOfArrays outputs, OutputArrayOfArrays internals)
	Given the `input` blobs, computes the output `blobs`. More...

void	forward_fallback (InputArrayOfArrays inputs, OutputArrayOfArrays outputs, OutputArrayOfArrays internals)
	Given the `input` blobs, computes the output `blobs`. More...

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. More...

virtual Ptr< BackendNode >	initCUDA (void *context, const std::vector< Ptr< BackendWrapper >> &inputs, const std::vector< Ptr< BackendWrapper >> &outputs)
	Returns a CUDA backend node. More...

virtual Ptr< BackendNode >	initHalide (const std::vector< Ptr< BackendWrapper > > &inputs)
	Returns Halide backend node. More...

virtual Ptr< BackendNode >	initInfEngine (const std::vector< Ptr< BackendWrapper > > &inputs)

virtual Ptr< BackendNode >	initNgraph (const std::vector< Ptr< BackendWrapper > > &inputs, const std::vector< Ptr< BackendNode > > &nodes)

virtual Ptr< BackendNode >	initVkCom (const std::vector< Ptr< BackendWrapper > > &inputs)

virtual int	inputNameToIndex (String inputName)
	Returns index of input blob into the input array. More...

virtual int	outputNameToIndex (const String &outputName)
	Returns index of output blob in output array. More...

void	run (const std::vector< Mat > &inputs, std::vector< Mat > &outputs, std::vector< Mat > &internals)
	Allocates layer and computes output. More...

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. More...

void	setParamsFrom (const LayerParams &params)
	Initializes only name, type and blobs fields. More...

virtual bool	supportBackend (int backendId)
	Ask layer if it support specific backend for doing computations. More...

virtual Ptr< BackendNode >	tryAttach (const Ptr< BackendNode > &node)
	Implement layers fusing. More...

virtual bool	tryFuse (Ptr< Layer > &top)
	Try to fuse current layer with a next one. More...

virtual void	unsetAttached ()
	"Deattaches" all the layers, attached to particular layer. More...

Public Member Functions inherited from cv::Algorithm
	Algorithm ()

virtual	~Algorithm ()

virtual void	clear ()
	Clears the algorithm state. More...

virtual bool	empty () const
	Returns true if the Algorithm is empty (e.g. in the very beginning or after unsuccessful read. More...

virtual String	getDefaultName () const

virtual void	read (const FileNode &fn)
	Reads algorithm parameters from a file storage. More...

virtual void	save (const String &filename) const

virtual void	write (FileStorage &fs) const
	Stores algorithm parameters in a file storage. More...

void	write (const Ptr< FileStorage > &fs, const String &name=String()) const
	simplified API for language bindings This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts. More...

Public Attributes
std::vector< Mat >	blobs
	List of learned parameters must be stored here to allow read them by using Net::getParam(). More...

String	name
	Name of the layer instance, can be used for logging or other internal purposes. More...

int	preferableTarget
	prefer target for layer forwarding More...

String	type
	Type name which was used for creating layer by layer factory. More...

Additional Inherited Members
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. More...

template<typename _Tp >
static Ptr< _Tp >	loadFromString (const String &strModel, const String &objname=String())
	Loads algorithm from a String. More...

template<typename _Tp >
static Ptr< _Tp >	read (const FileNode &fn)
	Reads algorithm from the file node. More...

Protected Member Functions inherited from cv::Algorithm
void	writeFormat (FileStorage &fs) const

Detailed Description

This interface class allows to build new Layers - are building blocks of networks.

Each class, derived from Layer, must implement allocate() methods to declare own outputs and forward() to compute outputs. Also before using the new layer into networks you must register your layer by using one of LayerFactory macros.

Constructor & Destructor Documentation

◆ Layer() [1/2]

cv::dnn::Layer::Layer ( )

◆ Layer() [2/2]

cv::dnn::Layer::Layer ( const LayerParams & params )

explicit

Initializes only name, type and blobs fields.

◆ ~Layer()

virtual cv::dnn::Layer::~Layer ( )

virtual

Member Function Documentation

◆ applyHalideScheduler()

virtual void cv::dnn::Layer::applyHalideScheduler	(	Ptr< BackendNode > &	node,
		const std::vector< Mat *> &	inputs,
		const std::vector< Mat > &	outputs,
		int	targetId
	)		const

virtual

Automatic Halide scheduling based on layer hyper-parameters.

Parameters

[in]	node	Backend node with Halide functions.
[in]	inputs	Blobs that will be used in forward invocations.
[in]	outputs	Blobs that will be used in forward invocations.
[in]	targetId	Target identifier

See also: BackendNode, Target

Layer don't use own Halide::Func members because we can have applied layers fusing. In this way the fused function should be scheduled.

◆ finalize() [1/4]

virtual void cv::dnn::Layer::finalize	(	const std::vector< Mat *> &	input,
		std::vector< Mat > &	output
	)

virtual

Python:
	outputs	=	cv.dnn_Layer.finalize(	inputs[, outputs]	)

Computes and sets internal parameters according to inputs, outputs and blobs.

Deprecated:: Use Layer::finalize(InputArrayOfArrays, OutputArrayOfArrays) instead

Parameters

[in]	input	vector of already allocated input blobs
[out]	output	vector of already allocated output blobs

If this method is called after network has allocated all memory for input and output blobs and before inferencing.

◆ finalize() [2/4]

virtual void cv::dnn::Layer::finalize	(	InputArrayOfArrays	inputs,
		OutputArrayOfArrays	outputs
	)

virtual

Python:
	outputs	=	cv.dnn_Layer.finalize(	inputs[, outputs]	)

Computes and sets internal parameters according to inputs, outputs and blobs.

Parameters

[in]	inputs	vector of already allocated input blobs
[out]	outputs	vector of already allocated output blobs

If this method is called after network has allocated all memory for input and output blobs and before inferencing.

◆ finalize() [3/4]

void cv::dnn::Layer::finalize	(	const std::vector< Mat > &	inputs,
		std::vector< Mat > &	outputs
	)

Python:
	outputs	=	cv.dnn_Layer.finalize(	inputs[, outputs]	)

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

Deprecated:: Use Layer::finalize(InputArrayOfArrays, OutputArrayOfArrays) instead

◆ finalize() [4/4]

std::vector<Mat> cv::dnn::Layer::finalize ( const std::vector< Mat > & inputs )

Python:
	outputs	=	cv.dnn_Layer.finalize(	inputs[, outputs]	)

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

Deprecated:: Use Layer::finalize(InputArrayOfArrays, OutputArrayOfArrays) instead

◆ forward() [1/2]

virtual void cv::dnn::Layer::forward	(	std::vector< Mat *> &	input,
		std::vector< Mat > &	output,
		std::vector< Mat > &	internals
	)

virtual

Given the input blobs, computes the output blobs.

Deprecated:: Use Layer::forward(InputArrayOfArrays, OutputArrayOfArrays, OutputArrayOfArrays) instead

Parameters

[in]	input	the input blobs.
[out]	output	allocated output blobs, which will store results of the computation.
[out]	internals	allocated internal blobs

◆ forward() [2/2]

virtual void cv::dnn::Layer::forward	(	InputArrayOfArrays	inputs,
		OutputArrayOfArrays	outputs,
		OutputArrayOfArrays	internals
	)

virtual

Given the input blobs, computes the output blobs.

Parameters

[in]	inputs	the input blobs.
[out]	outputs	allocated output blobs, which will store results of the computation.
[out]	internals	allocated internal blobs

◆ forward_fallback()

void cv::dnn::Layer::forward_fallback	(	InputArrayOfArrays	inputs,
		OutputArrayOfArrays	outputs,
		OutputArrayOfArrays	internals
	)

Given the input blobs, computes the output blobs.

Parameters

[in]	inputs	the input blobs.
[out]	outputs	allocated output blobs, which will store results of the computation.
[out]	internals	allocated internal blobs

◆ getFLOPS()

virtual int64 cv::dnn::Layer::getFLOPS	(	const std::vector< MatShape > &	inputs,
		const std::vector< MatShape > &	outputs
	)		const

inlinevirtual

◆ getMemoryShapes()

virtual bool cv::dnn::Layer::getMemoryShapes	(	const std::vector< MatShape > &	inputs,
		const int	requiredOutputs,
		std::vector< MatShape > &	outputs,
		std::vector< MatShape > &	internals
	)		const

virtual

◆ getScaleShift()

virtual void cv::dnn::Layer::getScaleShift	(	Mat &	scale,
		Mat &	shift
	)		const

virtual

Returns parameters of layers with channel-wise multiplication and addition.

Parameters

[out]	scale	Channel-wise multipliers. Total number of values should be equal to number of channels.
[out]	shift	Channel-wise offsets. Total number of values should be equal to number of channels.

Some layers can fuse their transformations with further layers. In example, convolution + batch normalization. This way base layer use weights from layer after it. Fused layer is skipped. By default, scale and shift are empty that means layer has no element-wise multiplications or additions.

◆ initCUDA()

virtual Ptr<BackendNode> cv::dnn::Layer::initCUDA	(	void *	context,
		const std::vector< Ptr< BackendWrapper >> &	inputs,
		const std::vector< Ptr< BackendWrapper >> &	outputs
	)

virtual

Returns a CUDA backend node.

Parameters

context	void pointer to CSLContext object
inputs	layer inputs
outputs	layer outputs

◆ initHalide()

virtual Ptr<BackendNode> cv::dnn::Layer::initHalide ( const std::vector< Ptr< BackendWrapper > > & inputs )

virtual

Returns Halide backend node.

Parameters

[in] inputs Input Halide buffers.

See also: BackendNode, BackendWrapper

Input buffers should be exactly the same that will be used in forward invocations. Despite we can use Halide::ImageParam based on input shape only, it helps prevent some memory management issues (if something wrong, Halide tests will be failed).

◆ initInfEngine()

virtual Ptr<BackendNode> cv::dnn::Layer::initInfEngine ( const std::vector< Ptr< BackendWrapper > > & inputs )

virtual

◆ initNgraph()

virtual Ptr<BackendNode> cv::dnn::Layer::initNgraph	(	const std::vector< Ptr< BackendWrapper > > &	inputs,
		const std::vector< Ptr< BackendNode > > &	nodes
	)

virtual

◆ initVkCom()

virtual Ptr<BackendNode> cv::dnn::Layer::initVkCom ( const std::vector< Ptr< BackendWrapper > > & inputs )

virtual

◆ inputNameToIndex()

virtual int cv::dnn::Layer::inputNameToIndex ( String inputName )

virtual

Returns index of input blob into the input array.

Parameters

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 in cv::dnn::LSTMLayer.

◆ outputNameToIndex()

virtual int cv::dnn::Layer::outputNameToIndex ( const String & outputName )

virtual

Python:
	retval	=	cv.dnn_Layer.outputNameToIndex(	outputName	)

Returns index of output blob in output array.

See also: inputNameToIndex()

Reimplemented in cv::dnn::LSTMLayer.

◆ run()

void cv::dnn::Layer::run	(	const std::vector< Mat > &	inputs,
		std::vector< Mat > &	outputs,
		std::vector< Mat > &	internals
	)

Python:
	outputs, internals	=	cv.dnn_Layer.run(	inputs, internals[, outputs]	)

Allocates layer and computes output.

Deprecated:: This method will be removed in the future release.

◆ setActivation()

virtual bool cv::dnn::Layer::setActivation ( const Ptr< ActivationLayer > & layer )

virtual

Tries to attach to the layer the subsequent activation layer, i.e. do the layer fusion in a partial case.

Parameters

[in] layer The subsequent activation layer.

Returns true if the activation layer has been attached successfully.

◆ setParamsFrom()

void cv::dnn::Layer::setParamsFrom ( const LayerParams & params )

Initializes only name, type and blobs fields.

◆ supportBackend()

virtual bool cv::dnn::Layer::supportBackend ( int backendId )

virtual

Ask layer if it support specific backend for doing computations.

Parameters

[in] backendId computation backend identifier.

See also: Backend

◆ tryAttach()

virtual Ptr<BackendNode> cv::dnn::Layer::tryAttach ( const Ptr< BackendNode > & node )

virtual

Implement layers fusing.

Parameters

[in] node Backend node of bottom layer.

See also: BackendNode

Actual for graph-based backends. If layer attached successfully, returns non-empty cv::Ptr to node of the same backend. Fuse only over the last function.

◆ tryFuse()

virtual bool cv::dnn::Layer::tryFuse ( Ptr< Layer > & top )

virtual

Try to fuse current layer with a next one.

Parameters

[in] top Next layer to be fused.

Returns: True if fusion was performed.

◆ unsetAttached()

virtual void cv::dnn::Layer::unsetAttached ( )

virtual

"Deattaches" all the layers, attached to particular layer.

Member Data Documentation

◆ blobs

std::vector<Mat> cv::dnn::Layer::blobs

List of learned parameters must be stored here to allow read them by using Net::getParam().

◆ name

String cv::dnn::Layer::name

Name of the layer instance, can be used for logging or other internal purposes.

◆ preferableTarget

int cv::dnn::Layer::preferableTarget

prefer target for layer forwarding

◆ type

String cv::dnn::Layer::type

Type name which was used for creating layer by layer factory.

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

opencv2/dnn/dnn.hpp

Public Member Functions

Public Attributes

Additional Inherited Members

Detailed Description

Constructor & Destructor Documentation

◆ Layer() [1/2]

◆ Layer() [2/2]

◆ ~Layer()

Member Function Documentation

◆ applyHalideScheduler()

◆ finalize() [1/4]

◆ finalize() [2/4]

◆ finalize() [3/4]

◆ finalize() [4/4]

◆ forward() [1/2]

◆ forward() [2/2]

◆ forward_fallback()

◆ getFLOPS()

◆ getMemoryShapes()

◆ getScaleShift()

◆ initCUDA()

◆ initHalide()

◆ initInfEngine()

◆ initNgraph()

◆ initVkCom()

◆ inputNameToIndex()

◆ outputNameToIndex()

◆ run()

◆ setActivation()

◆ setParamsFrom()

◆ supportBackend()

◆ tryAttach()

◆ tryFuse()

◆ unsetAttached()

Member Data Documentation

◆ blobs

◆ name

◆ preferableTarget

◆ type