cv::dnn::Net Class Reference

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

#include <opencv2/dnn/dnn.hpp>

Collaboration diagram for cv::dnn::Net:

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 (CV_WRAP_FILE_PATH const String &path)
	Dump net structure, hyperparameters, backend, target and fusion to dot file.
void	dumpToPbtxt (CV_WRAP_FILE_PATH const String &path)
	Dump net structure, hyperparameters, backend, target and fusion to pbtxt 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 (CV_ND OutputArrayOfArrays outputBlobs, const std::vector< String > &outBlobNames)
	Runs forward pass to compute outputs of layers listed in outBlobNames.
void	forward (CV_ND 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 (CV_ND 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, CV_ND const Mat &blob)
void	setParam (int layer, int numParam, CV_ND 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 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).
static Net	readFromModelOptimizer (CV_WRAP_FILE_PATH const String &xml, CV_WRAP_FILE_PATH const String &bin)
	Create a network from Intel's Model Optimizer 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

DictValue cv::dnn::Net::LayerId

Container for strings and integers.

Deprecated

Use getLayerId() with int result.

Constructor & Destructor Documentation

Net()

cv::dnn::Net::Net

(

)

Python:
	cv.dnn.Net(		) ->	<dnn_Net object>

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 )

Python:
	cv.dnn.Net.addLayer(	name, type, dtype, params	) ->	retval

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 )

Python:
	cv.dnn.Net.addLayer(	name, type, dtype, params	) ->	retval

addLayerToPrev() [1/2]

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

Python:
	cv.dnn.Net.addLayerToPrev(	name, type, dtype, params	) ->	retval

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 )

Python:
	cv.dnn.Net.addLayerToPrev(	name, type, dtype, params	) ->	retval

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 )

Python:
	cv.dnn.Net.connect(	outPin, inpPin	) ->	None

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 )

Python:
	cv.dnn.Net.connect(	outPin, inpPin	) ->	None

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

(

)

Python:
	cv.dnn.Net.dump(		) ->	retval

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

(

CV_WRAP_FILE_PATH const String &

path

)

Python:
	cv.dnn.Net.dumpToFile(	path	) ->	None

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

Parameters

path	path to output file with .dot extension

See also

dump()

dumpToPbtxt()

void cv::dnn::Net::dumpToPbtxt

(

CV_WRAP_FILE_PATH const String &

path

)

Python:
	cv.dnn.Net.dumpToPbtxt(	path	) ->	None

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

Parameters

path	path to output file with .pbtxt extension

Use Netron (https://netron.app) to open the target file to visualize the model. Call method after setInput(). To see correct backend, target and fusion run after forward().

empty()

bool cv::dnn::Net::empty

(

)

const

Python:
	cv.dnn.Net.empty(		) ->	retval

Returns true if there are no layers in the network.

enableFusion()

void cv::dnn::Net::enableFusion

(

bool

fusion

)

Python:
	cv.dnn.Net.enableFusion(	fusion	) ->	None

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

)

Python:
	cv.dnn.Net.enableWinograd(	useWinograd	) ->	None

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()

)

Python:
	cv.dnn.Net.forward(	[, outputName]	) ->	retval
	cv.dnn.Net.forward(	[, outputBlobs[, outputName]]	) ->	outputBlobs
	cv.dnn.Net.forward(	outBlobNames[, outputBlobs]	) ->	outputBlobs
	cv.dnn.Net.forwardAndRetrieve(	outBlobNames	) ->	outputBlobs

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	(	CV_ND OutputArrayOfArrays	outputBlobs,
		const std::vector< String > &	outBlobNames )

Python:
	cv.dnn.Net.forward(	[, outputName]	) ->	retval
	cv.dnn.Net.forward(	[, outputBlobs[, outputName]]	) ->	outputBlobs
	cv.dnn.Net.forward(	outBlobNames[, outputBlobs]	) ->	outputBlobs
	cv.dnn.Net.forwardAndRetrieve(	outBlobNames	) ->	outputBlobs

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	(	CV_ND OutputArrayOfArrays	outputBlobs,
		const String &	outputName = String() )

Python:
	cv.dnn.Net.forward(	[, outputName]	) ->	retval
	cv.dnn.Net.forward(	[, outputBlobs[, outputName]]	) ->	outputBlobs
	cv.dnn.Net.forward(	outBlobNames[, outputBlobs]	) ->	outputBlobs
	cv.dnn.Net.forwardAndRetrieve(	outBlobNames	) ->	outputBlobs

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 )

Python:
	cv.dnn.Net.forward(	[, outputName]	) ->	retval
	cv.dnn.Net.forward(	[, outputBlobs[, outputName]]	) ->	outputBlobs
	cv.dnn.Net.forward(	outBlobNames[, outputBlobs]	) ->	outputBlobs
	cv.dnn.Net.forwardAndRetrieve(	outBlobNames	) ->	outputBlobs

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()

)

Python:
	cv.dnn.Net.forwardAsync(	[, outputName]	) ->	retval

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

Python:
	cv.dnn.Net.getFLOPS(	netInputShapes	) ->	retval
	cv.dnn.Net.getFLOPS(	netInputShape	) ->	retval
	cv.dnn.Net.getFLOPS(	layerId, netInputShapes	) ->	retval
	cv.dnn.Net.getFLOPS(	layerId, netInputShape	) ->	retval

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

Python:
	cv.dnn.Net.getFLOPS(	netInputShapes	) ->	retval
	cv.dnn.Net.getFLOPS(	netInputShape	) ->	retval
	cv.dnn.Net.getFLOPS(	layerId, netInputShapes	) ->	retval
	cv.dnn.Net.getFLOPS(	layerId, netInputShape	) ->	retval

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

Python:
	cv.dnn.Net.getFLOPS(	netInputShapes	) ->	retval
	cv.dnn.Net.getFLOPS(	netInputShape	) ->	retval
	cv.dnn.Net.getFLOPS(	layerId, netInputShapes	) ->	retval
	cv.dnn.Net.getFLOPS(	layerId, netInputShape	) ->	retval

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

Python:
	cv.dnn.Net.getFLOPS(	netInputShapes	) ->	retval
	cv.dnn.Net.getFLOPS(	netInputShape	) ->	retval
	cv.dnn.Net.getFLOPS(	layerId, netInputShapes	) ->	retval
	cv.dnn.Net.getFLOPS(	layerId, netInputShape	) ->	retval

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

Python:
	cv.dnn.Net.getInputDetails(		) ->	scales, zeropoints

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

Python:
	cv.dnn.Net.getLayer(	layerId	) ->	retval
	cv.dnn.Net.getLayer(	layerName	) ->	retval

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

Python:
	cv.dnn.Net.getLayer(	layerId	) ->	retval
	cv.dnn.Net.getLayer(	layerName	) ->	retval

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)

Here is the call graph for this function:

getLayer() [3/3]

Ptr< Layer > cv::dnn::Net::getLayer

(

int

layerId

)

const

Python:
	cv.dnn.Net.getLayer(	layerId	) ->	retval
	cv.dnn.Net.getLayer(	layerName	) ->	retval

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

Python:
	cv.dnn.Net.getLayerId(	layer	) ->	retval

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

Python:
	cv.dnn.Net.getLayerNames(		) ->	retval

getLayersCount()

int cv::dnn::Net::getLayersCount

(

const String &

layerType

)

const

Python:
	cv.dnn.Net.getLayersCount(	layerType	) ->	retval

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

Python:
	cv.dnn.Net.getLayersShapes(	netInputShapes	) ->	layersIds, inLayersShapes, outLayersShapes
	cv.dnn.Net.getLayersShapes(	netInputShape	) ->	layersIds, inLayersShapes, outLayersShapes

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

Python:
	cv.dnn.Net.getLayersShapes(	netInputShapes	) ->	layersIds, inLayersShapes, outLayersShapes
	cv.dnn.Net.getLayersShapes(	netInputShape	) ->	layersIds, inLayersShapes, outLayersShapes

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

Python:
	cv.dnn.Net.getLayerTypes(		) ->	layersTypes

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

Python:
	cv.dnn.Net.getMemoryConsumption(	netInputShape	) ->	weights, blobs
	cv.dnn.Net.getMemoryConsumption(	layerId, netInputShapes	) ->	weights, blobs
	cv.dnn.Net.getMemoryConsumption(	layerId, netInputShape	) ->	weights, blobs

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

Python:
	cv.dnn.Net.getMemoryConsumption(	netInputShape	) ->	weights, blobs
	cv.dnn.Net.getMemoryConsumption(	layerId, netInputShapes	) ->	weights, blobs
	cv.dnn.Net.getMemoryConsumption(	layerId, netInputShape	) ->	weights, blobs

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

Python:
	cv.dnn.Net.getMemoryConsumption(	netInputShape	) ->	weights, blobs
	cv.dnn.Net.getMemoryConsumption(	layerId, netInputShapes	) ->	weights, blobs
	cv.dnn.Net.getMemoryConsumption(	layerId, netInputShape	) ->	weights, blobs

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

Python:
	cv.dnn.Net.getMemoryConsumption(	netInputShape	) ->	weights, blobs
	cv.dnn.Net.getMemoryConsumption(	layerId, netInputShapes	) ->	weights, blobs
	cv.dnn.Net.getMemoryConsumption(	layerId, netInputShape	) ->	weights, blobs

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

Python:
	cv.dnn.Net.getMemoryConsumption(	netInputShape	) ->	weights, blobs
	cv.dnn.Net.getMemoryConsumption(	layerId, netInputShapes	) ->	weights, blobs
	cv.dnn.Net.getMemoryConsumption(	layerId, netInputShape	) ->	weights, blobs

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

Python:
	cv.dnn.Net.getMemoryConsumption(	netInputShape	) ->	weights, blobs
	cv.dnn.Net.getMemoryConsumption(	layerId, netInputShapes	) ->	weights, blobs
	cv.dnn.Net.getMemoryConsumption(	layerId, netInputShape	) ->	weights, blobs

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

Python:
	cv.dnn.Net.getOutputDetails(		) ->	scales, zeropoints

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

Python:
	cv.dnn.Net.getParam(	layer[, numParam]	) ->	retval
	cv.dnn.Net.getParam(	layerName[, numParam]	) ->	retval

Here is the call graph for this function:

getParam() [2/2]

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

Python:
	cv.dnn.Net.getParam(	layer[, numParam]	) ->	retval
	cv.dnn.Net.getParam(	layerName[, numParam]	) ->	retval

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

)

Python:
	cv.dnn.Net.getPerfProfile(		) ->	retval, 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

Python:
	cv.dnn.Net.getUnconnectedOutLayers(		) ->	retval

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

Python:
	cv.dnn.Net.getUnconnectedOutLayersNames(		) ->	retval

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 )

Python:
	cv.dnn.Net.quantize(	calibData, inputsDtype, outputsDtype[, perChannel]	) ->	retval

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

Python:
	cv.dnn.Net.readFromModelOptimizer(	xml, bin	) ->	retval
	cv.dnn.Net.readFromModelOptimizer(	bufferModelConfig, bufferWeights	) ->	retval
	cv.dnn.Net_readFromModelOptimizer(	xml, bin	) ->	retval
	cv.dnn.Net_readFromModelOptimizer(	bufferModelConfig, bufferWeights	) ->	retval

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 uchar * bufferModelConfigPtr, size_t bufferModelConfigSize, const uchar * bufferWeightsPtr, size_t bufferWeightsSize )

static

Python:
	cv.dnn.Net.readFromModelOptimizer(	xml, bin	) ->	retval
	cv.dnn.Net.readFromModelOptimizer(	bufferModelConfig, bufferWeights	) ->	retval
	cv.dnn.Net_readFromModelOptimizer(	xml, bin	) ->	retval
	cv.dnn.Net_readFromModelOptimizer(	bufferModelConfig, bufferWeights	) ->	retval

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.

readFromModelOptimizer() [3/3]

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

static

Python:
	cv.dnn.Net.readFromModelOptimizer(	xml, bin	) ->	retval
	cv.dnn.Net.readFromModelOptimizer(	bufferModelConfig, bufferWeights	) ->	retval
	cv.dnn.Net_readFromModelOptimizer(	xml, bin	) ->	retval
	cv.dnn.Net_readFromModelOptimizer(	bufferModelConfig, bufferWeights	) ->	retval

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.

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

)

Python:
	cv.dnn.Net.setHalideScheduler(	scheduler	) ->	None

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	(	CV_ND InputArray	blob,
		const String &	name = "",
		double	scalefactor = 1.0,
		const Scalar &	mean = Scalar() )

Python:
	cv.dnn.Net.setInput(	blob[, name[, scalefactor[, mean]]]	) ->	None

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 )

Python:
	cv.dnn.Net.setInputShape(	inputName, shape	) ->	None

Specify shape of network input.

setInputsNames()

void cv::dnn::Net::setInputsNames

(

const std::vector< String > &

inputBlobNames

)

Python:
	cv.dnn.Net.setInputsNames(	inputBlobNames	) ->	None

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, CV_ND const Mat & blob )

inline

Python:
	cv.dnn.Net.setParam(	layer, numParam, blob	) ->	None
	cv.dnn.Net.setParam(	layerName, numParam, blob	) ->	None

Here is the call graph for this function:

setParam() [2/2]

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

Python:
	cv.dnn.Net.setParam(	layer, numParam, blob	) ->	None
	cv.dnn.Net.setParam(	layerName, numParam, blob	) ->	None

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

)

Python:
	cv.dnn.Net.setPreferableBackend(	backendId	) ->	None

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

)

Python:
	cv.dnn.Net.setPreferableTarget(	targetId	) ->	None

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 Symbol 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