This class allows to create and manipulate comprehensive artificial neural networks. More...

#include <opencv2/dnn/dnn.hpp>

Public Types
typedef DictValue	LayerId
	Container for strings and integers.

Public Member Functions
	Net ()
	Default constructor.

	~Net ()
	Destructor frees the net only if there aren't references to the net anymore.

int	addLayer (const String &name, const String &type, const int &dtype, LayerParams &params)
	Adds new layer to the net.

int	addLayer (const String &name, const String &type, LayerParams &params)

int	addLayerToPrev (const String &name, const String &type, const int &dtype, LayerParams &params)
	Adds new layer and connects its first input to the first output of previously added layer.

int	addLayerToPrev (const String &name, const String &type, LayerParams &params)

void	connect (int outLayerId, int outNum, int inpLayerId, int inpNum)
	Connects #`outNum` output of the first layer to #`inNum` input of the second layer.

void	connect (String outPin, String inpPin)
	Connects output of the first layer to input of the second layer.

String	dump ()
	Dump net to String.

void	dumpToFile (const String &path)
	Dump net structure, hyperparameters, backend, target and fusion to dot file.

bool	empty () const

void	enableFusion (bool fusion)
	Enables or disables layer fusion in the network.

void	enableWinograd (bool useWinograd)
	Enables or disables the Winograd compute branch. The Winograd compute branch can speed up 3x3 Convolution at a small loss of accuracy.

Mat	forward (const String &outputName=String())
	Runs forward pass to compute output of layer with name `outputName`.

void	forward (OutputArrayOfArrays outputBlobs, const std::vector< String > &outBlobNames)
	Runs forward pass to compute outputs of layers listed in `outBlobNames`.

void	forward (OutputArrayOfArrays outputBlobs, const String &outputName=String())
	Runs forward pass to compute output of layer with name `outputName`.

void	forward (std::vector< std::vector< Mat > > &outputBlobs, const std::vector< String > &outBlobNames)
	Runs forward pass to compute outputs of layers listed in `outBlobNames`.

AsyncArray	forwardAsync (const String &outputName=String())
	Runs forward pass to compute output of layer with name `outputName`.

int64	getFLOPS (const int layerId, const MatShape &netInputShape) const

int64	getFLOPS (const int layerId, const std::vector< MatShape > &netInputShapes) const

int64	getFLOPS (const MatShape &netInputShape) const

int64	getFLOPS (const std::vector< MatShape > &netInputShapes) const
	Computes FLOP for whole loaded model with specified input shapes.

Impl *	getImpl () const

Impl &	getImplRef () const

void	getInputDetails (std::vector< float > &scales, std::vector< int > &zeropoints) const
	Returns input scale and zeropoint for a quantized Net.

Ptr< Layer >	getLayer (const LayerId &layerId) const

Ptr< Layer >	getLayer (const String &layerName) const

Ptr< Layer >	getLayer (int layerId) const
	Returns pointer to layer with specified id or name which the network use.

int	getLayerId (const String &layer) const
	Converts string name of the layer to the integer identifier.

std::vector< Ptr< Layer > >	getLayerInputs (int layerId) const
	Returns pointers to input layers of specific layer.

std::vector< String >	getLayerNames () const

int	getLayersCount (const String &layerType) const
	Returns count of layers of specified type.

void	getLayerShapes (const MatShape &netInputShape, const int layerId, std::vector< MatShape > &inLayerShapes, std::vector< MatShape > &outLayerShapes) const
	Returns input and output shapes for layer with specified id in loaded model; preliminary inferencing isn't necessary.

void	getLayerShapes (const std::vector< MatShape > &netInputShapes, const int layerId, std::vector< MatShape > &inLayerShapes, std::vector< MatShape > &outLayerShapes) const

void	getLayersShapes (const MatShape &netInputShape, std::vector< int > &layersIds, std::vector< std::vector< MatShape > > &inLayersShapes, std::vector< std::vector< MatShape > > &outLayersShapes) const

void	getLayersShapes (const std::vector< MatShape > &netInputShapes, std::vector< int > &layersIds, std::vector< std::vector< MatShape > > &inLayersShapes, std::vector< std::vector< MatShape > > &outLayersShapes) const
	Returns input and output shapes for all layers in loaded model; preliminary inferencing isn't necessary.

void	getLayerTypes (std::vector< String > &layersTypes) const
	Returns list of types for layer used in model.

void	getMemoryConsumption (const int layerId, const MatShape &netInputShape, size_t &weights, size_t &blobs) const

void	getMemoryConsumption (const int layerId, const std::vector< MatShape > &netInputShapes, size_t &weights, size_t &blobs) const

void	getMemoryConsumption (const MatShape &netInputShape, size_t &weights, size_t &blobs) const

void	getMemoryConsumption (const MatShape &netInputShape, std::vector< int > &layerIds, std::vector< size_t > &weights, std::vector< size_t > &blobs) const

void	getMemoryConsumption (const std::vector< MatShape > &netInputShapes, size_t &weights, size_t &blobs) const
	Computes bytes number which are required to store all weights and intermediate blobs for model.

void	getMemoryConsumption (const std::vector< MatShape > &netInputShapes, std::vector< int > &layerIds, std::vector< size_t > &weights, std::vector< size_t > &blobs) const
	Computes bytes number which are required to store all weights and intermediate blobs for each layer.

void	getOutputDetails (std::vector< float > &scales, std::vector< int > &zeropoints) const
	Returns output scale and zeropoint for a quantized Net.

Mat	getParam (const String &layerName, int numParam=0) const

Mat	getParam (int layer, int numParam=0) const
	Returns parameter blob of the layer.

int64	getPerfProfile (std::vector< double > &timings)
	Returns overall time for inference and timings (in ticks) for layers.

std::vector< int >	getUnconnectedOutLayers () const
	Returns indexes of layers with unconnected outputs.

std::vector< String >	getUnconnectedOutLayersNames () const
	Returns names of layers with unconnected outputs.

Net	quantize (InputArrayOfArrays calibData, int inputsDtype, int outputsDtype, bool perChannel=true)
	Returns a quantized Net from a floating-point Net.

int	registerOutput (const std::string &outputName, int layerId, int outputPort)
	Registers network output with name.

void	setHalideScheduler (const String &scheduler)
	Compile Halide layers.

void	setInput (InputArray blob, const String &name="", double scalefactor=1.0, const Scalar &mean=Scalar())
	Sets the new input value for the network.

void	setInputShape (const String &inputName, const MatShape &shape)
	Specify shape of network input.

void	setInputsNames (const std::vector< String > &inputBlobNames)
	Sets outputs names of the network input pseudo layer.

void	setParam (const String &layerName, int numParam, const Mat &blob)

void	setParam (int layer, int numParam, const Mat &blob)
	Sets the new value for the learned param of the layer.

void	setPreferableBackend (int backendId)
	Ask network to use specific computation backend where it supported.

void	setPreferableTarget (int targetId)
	Ask network to make computations on specific target device.

Static Public Member Functions
static Net	readFromModelOptimizer (const std::vector< uchar > &bufferModelConfig, const std::vector< uchar > &bufferWeights)
	Create a network from Intel's Model Optimizer in-memory buffers with intermediate representation (IR).

static Net	readFromModelOptimizer (const String &xml, const String &bin)
	Create a network from Intel's Model Optimizer intermediate representation (IR).

static Net	readFromModelOptimizer (const uchar bufferModelConfigPtr, size_t bufferModelConfigSize, const uchar bufferWeightsPtr, size_t bufferWeightsSize)
	Create a network from Intel's Model Optimizer in-memory buffers with intermediate representation (IR).

Protected Attributes
Ptr< Impl >	impl

Friends
class	accessor::DnnNetAccessor

Detailed Description

This class allows to create and manipulate comprehensive artificial neural networks.

Neural network is presented as directed acyclic graph (DAG), where vertices are Layer instances, and edges specify relationships between layers inputs and outputs.

Each network layer has unique integer id and unique string name inside its network. LayerId can store either layer name or layer id.

This class supports reference counting of its instances, i. e. copies point to the same instance.

Examples: samples/dnn/colorization.cpp, and samples/dnn/openpose.cpp.

Member Typedef Documentation

◆ LayerId

typedef DictValue cv::dnn::Net::LayerId

Container for strings and integers.

Deprecated:: Use getLayerId() with int result.

Constructor & Destructor Documentation

◆ Net()

cv::dnn::Net::Net ( )

Default constructor.

◆ ~Net()

cv::dnn::Net::~Net ( )

Destructor frees the net only if there aren't references to the net anymore.

Member Function Documentation

◆ addLayer() [1/2]

int cv::dnn::Net::addLayer	(	const String &	name,
		const String &	type,
		const int &	dtype,
		LayerParams &	params
	)

Adds new layer to the net.

Parameters

name	unique name of the adding layer.
type	typename of the adding layer (type must be registered in LayerRegister).
dtype	datatype of output blobs.
params	parameters which will be used to initialize the creating layer.

Returns: unique identifier of created layer, or -1 if a failure will happen.

◆ addLayer() [2/2]

int cv::dnn::Net::addLayer	(	const String &	name,
		const String &	type,
		LayerParams &	params
	)

◆ addLayerToPrev() [1/2]

int cv::dnn::Net::addLayerToPrev	(	const String &	name,
		const String &	type,
		const int &	dtype,
		LayerParams &	params
	)

Adds new layer and connects its first input to the first output of previously added layer.

See also: addLayer()

◆ addLayerToPrev() [2/2]

int cv::dnn::Net::addLayerToPrev	(	const String &	name,
		const String &	type,
		LayerParams &	params
	)

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

◆ connect() [1/2]

void cv::dnn::Net::connect	(	int	outLayerId,
		int	outNum,
		int	inpLayerId,
		int	inpNum
	)

Connects #outNum output of the first layer to #inNum input of the second layer.

Parameters

outLayerId	identifier of the first layer
outNum	number of the first layer output
inpLayerId	identifier of the second layer
inpNum	number of the second layer input

◆ connect() [2/2]

void cv::dnn::Net::connect	(	String	outPin,
		String	inpPin
	)

Connects output of the first layer to input of the second layer.

Parameters

outPin	descriptor of the first layer output.
inpPin	descriptor of the second layer input.

Descriptors have the following template <layer_name>[.input_number]:

the first part of the template layer_name is string name of the added layer. If this part is empty then the network input pseudo layer will be used;
the second optional part of the template input_number is either number of the layer input, either label one. If this part is omitted then the first layer input will be used.

See also
setNetInputs(), Layer::inputNameToIndex(), Layer::outputNameToIndex()

◆ dump()

String cv::dnn::Net::dump ( )

Dump net to String.

Returns: String with structure, hyperparameters, backend, target and fusion Call method after setInput(). To see correct backend, target and fusion run after forward().

◆ dumpToFile()

void cv::dnn::Net::dumpToFile ( const String & path )

Dump net structure, hyperparameters, backend, target and fusion to dot file.

Parameters

path	path to output file with .dot extension

See also: dump()

◆ empty()

bool cv::dnn::Net::empty ( ) const

Returns true if there are no layers in the network.

◆ enableFusion()

void cv::dnn::Net::enableFusion ( bool fusion )

Enables or disables layer fusion in the network.

Parameters

fusion true to enable the fusion, false to disable. The fusion is enabled by default.

◆ enableWinograd()

void cv::dnn::Net::enableWinograd ( bool useWinograd )

Enables or disables the Winograd compute branch. The Winograd compute branch can speed up 3x3 Convolution at a small loss of accuracy.

Parameters

useWinograd true to enable the Winograd compute branch. The default is true.

◆ forward() [1/4]

Mat cv::dnn::Net::forward ( const String & outputName = String() )

Runs forward pass to compute output of layer with name outputName.

Parameters

outputName name for layer which output is needed to get

Returns: blob for first output of specified layer.

By default runs forward pass for the whole network.

Examples: samples/dnn/colorization.cpp, and samples/dnn/openpose.cpp.

◆ forward() [2/4]

void cv::dnn::Net::forward	(	OutputArrayOfArrays	outputBlobs,
		const std::vector< String > &	outBlobNames
	)

Runs forward pass to compute outputs of layers listed in outBlobNames.

Parameters

outputBlobs	contains blobs for first outputs of specified layers.
outBlobNames	names for layers which outputs are needed to get

◆ forward() [3/4]

void cv::dnn::Net::forward	(	OutputArrayOfArrays	outputBlobs,
		const String &	outputName = `String()`
	)

Runs forward pass to compute output of layer with name outputName.

Parameters

outputBlobs	contains all output blobs for specified layer.
outputName	name for layer which output is needed to get

If outputName is empty, runs forward pass for the whole network.

◆ forward() [4/4]

void cv::dnn::Net::forward	(	std::vector< std::vector< Mat > > &	outputBlobs,
		const std::vector< String > &	outBlobNames
	)

Runs forward pass to compute outputs of layers listed in outBlobNames.

Parameters

outputBlobs	contains all output blobs for each layer specified in `outBlobNames`.
outBlobNames	names for layers which outputs are needed to get

◆ forwardAsync()

AsyncArray cv::dnn::Net::forwardAsync ( const String & outputName = String() )

Runs forward pass to compute output of layer with name outputName.

Parameters

outputName name for layer which output is needed to get

By default runs forward pass for the whole network.

This is an asynchronous version of forward(const String&). dnn::DNN_BACKEND_INFERENCE_ENGINE backend is required.

◆ getFLOPS() [1/4]

int64 cv::dnn::Net::getFLOPS	(	const int	layerId,
		const MatShape &	netInputShape
	)		const

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

◆ getFLOPS() [2/4]

int64 cv::dnn::Net::getFLOPS	(	const int	layerId,
		const std::vector< MatShape > &	netInputShapes
	)		const

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

◆ getFLOPS() [3/4]

int64 cv::dnn::Net::getFLOPS ( const MatShape & netInputShape ) const

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

◆ getFLOPS() [4/4]

int64 cv::dnn::Net::getFLOPS ( const std::vector< MatShape > & netInputShapes ) const

Computes FLOP for whole loaded model with specified input shapes.

Parameters

netInputShapes vector of shapes for all net inputs.

Returns: computed FLOP.

◆ getImpl()

Impl * cv::dnn::Net::getImpl ( ) const

inline

◆ getImplRef()

Impl & cv::dnn::Net::getImplRef ( ) const

inline

◆ getInputDetails()

void cv::dnn::Net::getInputDetails	(	std::vector< float > &	scales,
		std::vector< int > &	zeropoints
	)		const

Returns input scale and zeropoint for a quantized Net.

Parameters

scales	output parameter for returning input scales.
zeropoints	output parameter for returning input zeropoints.

◆ getLayer() [1/3]

Ptr< Layer > cv::dnn::Net::getLayer ( const LayerId & layerId ) const

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

Deprecated:: to be removed

◆ getLayer() [2/3]

Ptr< Layer > cv::dnn::Net::getLayer ( const String & layerName ) const

inline

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

Deprecated:: Use int getLayerId(const String &layer)

◆ getLayer() [3/3]

Ptr< Layer > cv::dnn::Net::getLayer ( int layerId ) const

Returns pointer to layer with specified id or name which the network use.

Examples: samples/dnn/colorization.cpp.

◆ getLayerId()

int cv::dnn::Net::getLayerId ( const String & layer ) const

Converts string name of the layer to the integer identifier.

Returns: id of the layer, or -1 if the layer wasn't found.

◆ getLayerInputs()

std::vector< Ptr< Layer > > cv::dnn::Net::getLayerInputs ( int layerId ) const

Returns pointers to input layers of specific layer.

◆ getLayerNames()

std::vector< String > cv::dnn::Net::getLayerNames ( ) const

◆ getLayersCount()

int cv::dnn::Net::getLayersCount ( const String & layerType ) const

Returns count of layers of specified type.

Parameters

layerType type.

Returns: count of layers

◆ getLayerShapes() [1/2]

void cv::dnn::Net::getLayerShapes	(	const MatShape &	netInputShape,
		const int	layerId,
		std::vector< MatShape > &	inLayerShapes,
		std::vector< MatShape > &	outLayerShapes
	)		const

Returns input and output shapes for layer with specified id in loaded model; preliminary inferencing isn't necessary.

Parameters

netInputShape	shape input blob in net input layer.
layerId	id for layer.
inLayerShapes	output parameter for input layers shapes; order is the same as in layersIds
outLayerShapes	output parameter for output layers shapes; order is the same as in layersIds

◆ getLayerShapes() [2/2]

void cv::dnn::Net::getLayerShapes	(	const std::vector< MatShape > &	netInputShapes,
		const int	layerId,
		std::vector< MatShape > &	inLayerShapes,
		std::vector< MatShape > &	outLayerShapes
	)		const

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

◆ getLayersShapes() [1/2]

void cv::dnn::Net::getLayersShapes	(	const MatShape &	netInputShape,
		std::vector< int > &	layersIds,
		std::vector< std::vector< MatShape > > &	inLayersShapes,
		std::vector< std::vector< MatShape > > &	outLayersShapes
	)		const

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

◆ getLayersShapes() [2/2]

void cv::dnn::Net::getLayersShapes	(	const std::vector< MatShape > &	netInputShapes,
		std::vector< int > &	layersIds,
		std::vector< std::vector< MatShape > > &	inLayersShapes,
		std::vector< std::vector< MatShape > > &	outLayersShapes
	)		const

Returns input and output shapes for all layers in loaded model; preliminary inferencing isn't necessary.

Parameters

netInputShapes	shapes for all input blobs in net input layer.
layersIds	output parameter for layer IDs.
inLayersShapes	output parameter for input layers shapes; order is the same as in layersIds
outLayersShapes	output parameter for output layers shapes; order is the same as in layersIds

◆ getLayerTypes()

void cv::dnn::Net::getLayerTypes ( std::vector< String > & layersTypes ) const

Returns list of types for layer used in model.

Parameters

layersTypes output parameter for returning types.

◆ getMemoryConsumption() [1/6]

void cv::dnn::Net::getMemoryConsumption	(	const int	layerId,
		const MatShape &	netInputShape,
		size_t &	weights,
		size_t &	blobs
	)		const

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

◆ getMemoryConsumption() [2/6]

void cv::dnn::Net::getMemoryConsumption	(	const int	layerId,
		const std::vector< MatShape > &	netInputShapes,
		size_t &	weights,
		size_t &	blobs
	)		const

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

◆ getMemoryConsumption() [3/6]

void cv::dnn::Net::getMemoryConsumption	(	const MatShape &	netInputShape,
		size_t &	weights,
		size_t &	blobs
	)		const

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

◆ getMemoryConsumption() [4/6]

void cv::dnn::Net::getMemoryConsumption	(	const MatShape &	netInputShape,
		std::vector< int > &	layerIds,
		std::vector< size_t > &	weights,
		std::vector< size_t > &	blobs
	)		const

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

◆ getMemoryConsumption() [5/6]

void cv::dnn::Net::getMemoryConsumption	(	const std::vector< MatShape > &	netInputShapes,
		size_t &	weights,
		size_t &	blobs
	)		const

Computes bytes number which are required to store all weights and intermediate blobs for model.

Parameters

netInputShapes	vector of shapes for all net inputs.
weights	output parameter to store resulting bytes for weights.
blobs	output parameter to store resulting bytes for intermediate blobs.

◆ getMemoryConsumption() [6/6]

void cv::dnn::Net::getMemoryConsumption	(	const std::vector< MatShape > &	netInputShapes,
		std::vector< int > &	layerIds,
		std::vector< size_t > &	weights,
		std::vector< size_t > &	blobs
	)		const

Computes bytes number which are required to store all weights and intermediate blobs for each layer.

Parameters

netInputShapes	vector of shapes for all net inputs.
layerIds	output vector to save layer IDs.
weights	output parameter to store resulting bytes for weights.
blobs	output parameter to store resulting bytes for intermediate blobs.

◆ getOutputDetails()

void cv::dnn::Net::getOutputDetails	(	std::vector< float > &	scales,
		std::vector< int > &	zeropoints
	)		const

Returns output scale and zeropoint for a quantized Net.

Parameters

scales	output parameter for returning output scales.
zeropoints	output parameter for returning output zeropoints.

◆ getParam() [1/2]

Mat cv::dnn::Net::getParam	(	const String &	layerName,
		int	numParam = `0`
	)		const

inline

◆ getParam() [2/2]

Mat cv::dnn::Net::getParam	(	int	layer,
		int	numParam = `0`
	)		const

Returns parameter blob of the layer.

Parameters

layer	name or id of the layer.
numParam	index of the layer parameter in the Layer::blobs array.

See also: Layer::blobs

◆ getPerfProfile()

int64 cv::dnn::Net::getPerfProfile ( std::vector< double > & timings )

Returns overall time for inference and timings (in ticks) for layers.

Indexes in returned vector correspond to layers ids. Some layers can be fused with others, in this case zero ticks count will be return for that skipped layers. Supported by DNN_BACKEND_OPENCV on DNN_TARGET_CPU only.

Parameters

[out] timings vector for tick timings for all layers.

Returns: overall ticks for model inference.

◆ getUnconnectedOutLayers()

std::vector< int > cv::dnn::Net::getUnconnectedOutLayers ( ) const

Returns indexes of layers with unconnected outputs.

FIXIT: Rework API to registerOutput() approach, deprecate this call

◆ getUnconnectedOutLayersNames()

std::vector< String > cv::dnn::Net::getUnconnectedOutLayersNames ( ) const

Returns names of layers with unconnected outputs.

FIXIT: Rework API to registerOutput() approach, deprecate this call

◆ quantize()

Net cv::dnn::Net::quantize	(	InputArrayOfArrays	calibData,
		int	inputsDtype,
		int	outputsDtype,
		bool	perChannel = `true`
	)

Returns a quantized Net from a floating-point Net.

Parameters

calibData	Calibration data to compute the quantization parameters.
inputsDtype	Datatype of quantized net's inputs. Can be CV_32F or CV_8S.
outputsDtype	Datatype of quantized net's outputs. Can be CV_32F or CV_8S.
perChannel	Quantization granularity of quantized Net. The default is true, that means quantize model in per-channel way (channel-wise). Set it false to quantize model in per-tensor way (or tensor-wise).

◆ readFromModelOptimizer() [1/3]

static Net cv::dnn::Net::readFromModelOptimizer	(	const std::vector< uchar > &	bufferModelConfig,
		const std::vector< uchar > &	bufferWeights
	)

static

Create a network from Intel's Model Optimizer in-memory buffers with intermediate representation (IR).

Parameters

[in]	bufferModelConfig	buffer with model's configuration.
[in]	bufferWeights	buffer with model's trained weights.

Returns: Net object.

◆ readFromModelOptimizer() [2/3]

static Net cv::dnn::Net::readFromModelOptimizer	(	const String &	xml,
		const String &	bin
	)

static

Create a network from Intel's Model Optimizer intermediate representation (IR).

Parameters

[in]	xml	XML configuration file with network's topology.
[in]	bin	Binary file with trained weights. Networks imported from Intel's Model Optimizer are launched in Intel's Inference Engine backend.

◆ readFromModelOptimizer() [3/3]

static Net cv::dnn::Net::readFromModelOptimizer	(	const uchar *	bufferModelConfigPtr,
		size_t	bufferModelConfigSize,
		const uchar *	bufferWeightsPtr,
		size_t	bufferWeightsSize
	)

static

Create a network from Intel's Model Optimizer in-memory buffers with intermediate representation (IR).

Parameters

[in]	bufferModelConfigPtr	buffer pointer of model's configuration.
[in]	bufferModelConfigSize	buffer size of model's configuration.
[in]	bufferWeightsPtr	buffer pointer of model's trained weights.
[in]	bufferWeightsSize	buffer size of model's trained weights.

Returns: Net object.

◆ registerOutput()

int cv::dnn::Net::registerOutput	(	const std::string &	outputName,
		int	layerId,
		int	outputPort
	)

Registers network output with name.

Function may create additional 'Identity' layer.

Parameters

outputName	identifier of the output
layerId	identifier of the second layer
outputPort	number of the second layer input

Returns: index of bound layer (the same as layerId or newly created)

◆ setHalideScheduler()

void cv::dnn::Net::setHalideScheduler ( const String & scheduler )

Compile Halide layers.

Parameters

[in] scheduler Path to YAML file with scheduling directives.

See also: setPreferableBackend

Schedule layers that support Halide backend. Then compile them for specific target. For layers that not represented in scheduling file or if no manual scheduling used at all, automatic scheduling will be applied.

◆ setInput()

void cv::dnn::Net::setInput	(	InputArray	blob,
		const String &	name = `""`,
		double	scalefactor = `1.0`,
		const Scalar &	mean = `Scalar()`
	)

Sets the new input value for the network.

Parameters

blob	A new blob. Should have CV_32F or CV_8U depth.
name	A name of input layer.
scalefactor	An optional normalization scale.
mean	An optional mean subtraction values.

See also: connect(String, String) to know format of the descriptor.

If scale or mean values are specified, a final input blob is computed as:

\[input(n,c,h,w) = scalefactor \times (blob(n,c,h,w) - mean_c)\]

Examples: samples/dnn/colorization.cpp, and samples/dnn/openpose.cpp.

◆ setInputShape()

void cv::dnn::Net::setInputShape	(	const String &	inputName,
		const MatShape &	shape
	)

Specify shape of network input.

◆ setInputsNames()

void cv::dnn::Net::setInputsNames ( const std::vector< String > & inputBlobNames )

Sets outputs names of the network input pseudo layer.

Each net always has special own the network input pseudo layer with id=0. This layer stores the user blobs only and don't make any computations. In fact, this layer provides the only way to pass user data into the network. As any other layer, this layer can label its outputs and this function provides an easy way to do this.

◆ setParam() [1/2]

void cv::dnn::Net::setParam	(	const String &	layerName,
		int	numParam,
		const Mat &	blob
	)

inline

◆ setParam() [2/2]

void cv::dnn::Net::setParam	(	int	layer,
		int	numParam,
		const Mat &	blob
	)

Sets the new value for the learned param of the layer.

Parameters

layer	name or id of the layer.
numParam	index of the layer parameter in the Layer::blobs array.
blob	the new value.

See also: Layer::blobs

Note: If shape of the new blob differs from the previous shape, then the following forward pass may fail.

◆ setPreferableBackend()

void cv::dnn::Net::setPreferableBackend ( int backendId )

Ask network to use specific computation backend where it supported.

Parameters

[in] backendId backend identifier.

See also: Backend

◆ setPreferableTarget()

void cv::dnn::Net::setPreferableTarget ( int targetId )

Ask network to make computations on specific target device.

Parameters

[in] targetId target identifier.

See also: Target

List of supported combinations backend / target:

	DNN_BACKEND_OPENCV	DNN_BACKEND_INFERENCE_ENGINE	DNN_BACKEND_HALIDE	DNN_BACKEND_CUDA
DNN_TARGET_CPU	+	+	+
DNN_TARGET_OPENCL	+	+	+
DNN_TARGET_OPENCL_FP16	+	+
DNN_TARGET_MYRIAD		+
DNN_TARGET_FPGA		+
DNN_TARGET_CUDA				+
DNN_TARGET_CUDA_FP16				+
DNN_TARGET_HDDL		+

Examples: samples/dnn/colorization.cpp.

Friends And Related Function Documentation

◆ accessor::DnnNetAccessor

friend class accessor::DnnNetAccessor

friend

Member Data Documentation

◆ impl

Ptr<Impl> cv::dnn::Net::impl

protected

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

opencv2/dnn/dnn.hpp

Public Types

Public Member Functions

Static Public Member Functions

Protected Attributes

Friends

Detailed Description

Member Typedef Documentation

◆ LayerId

Constructor & Destructor Documentation

◆ Net()

◆ ~Net()

Member Function Documentation

◆ addLayer() [1/2]

◆ addLayer() [2/2]

◆ addLayerToPrev() [1/2]

◆ addLayerToPrev() [2/2]

◆ connect() [1/2]

◆ connect() [2/2]

◆ dump()

◆ dumpToFile()

◆ empty()

◆ enableFusion()

◆ enableWinograd()

◆ forward() [1/4]

◆ forward() [2/4]

◆ forward() [3/4]

◆ forward() [4/4]

◆ forwardAsync()

◆ getFLOPS() [1/4]

◆ getFLOPS() [2/4]

◆ getFLOPS() [3/4]

◆ getFLOPS() [4/4]

◆ getImpl()

◆ getImplRef()

◆ getInputDetails()

◆ getLayer() [1/3]

◆ getLayer() [2/3]

◆ getLayer() [3/3]

◆ getLayerId()

◆ getLayerInputs()

◆ getLayerNames()

◆ getLayersCount()

◆ getLayerShapes() [1/2]

◆ getLayerShapes() [2/2]

◆ getLayersShapes() [1/2]

◆ getLayersShapes() [2/2]

◆ getLayerTypes()

◆ getMemoryConsumption() [1/6]

◆ getMemoryConsumption() [2/6]

◆ getMemoryConsumption() [3/6]

◆ getMemoryConsumption() [4/6]

◆ getMemoryConsumption() [5/6]

◆ getMemoryConsumption() [6/6]

◆ getOutputDetails()

◆ getParam() [1/2]

◆ getParam() [2/2]

◆ getPerfProfile()

◆ getUnconnectedOutLayers()

◆ getUnconnectedOutLayersNames()

◆ quantize()

◆ readFromModelOptimizer() [1/3]

◆ readFromModelOptimizer() [2/3]

◆ readFromModelOptimizer() [3/3]

◆ registerOutput()

◆ setHalideScheduler()

◆ setInput()

◆ setInputShape()

◆ setInputsNames()

◆ setParam() [1/2]

◆ setParam() [2/2]

◆ setPreferableBackend()

◆ setPreferableTarget()

Friends And Related Function Documentation

◆ accessor::DnnNetAccessor

Member Data Documentation

◆ impl