cv::dnn::Net Class Reference

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, LayerParams &params)
	Adds new layer to the net.
int	addLayerToPrev (const String &name, const String &type, LayerParams &params)
	Adds new layer and connects its first input to the first output of previously added layer.
void	connect (String outPin, String inpPin)
	Connects output of the first layer to input of the second layer.
void	connect (int outLayerId, int outNum, int inpLayerId, int inpNum)
	Connects #outNum output of the first layer to #inNum input of the second layer.
bool	empty () const
void	enableFusion (bool fusion)
	Enables or disables layer fusion in the network.
Mat	forward (const String &outputName=String())
	Runs forward pass to compute output of layer with name outputName.
void	forward (OutputArrayOfArrays outputBlobs, 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 (std::vector< std::vector< Mat > > &outputBlobs, const std::vector< String > &outBlobNames)
	Runs forward pass to compute outputs of layers listed in outBlobNames.
int64	getFLOPS (const std::vector< MatShape > &netInputShapes) const
	Computes FLOP for whole loaded model with specified input shapes.
int64	getFLOPS (const MatShape &netInputShape) const
int64	getFLOPS (const int layerId, const std::vector< MatShape > &netInputShapes) const
int64	getFLOPS (const int layerId, const MatShape &netInputShape) const
Ptr< Layer >	getLayer (LayerId layerId)
	Returns pointer to layer with specified id or name which the network use.
int	getLayerId (const String &layer)
	Converts string name of the layer to the integer identifier.
std::vector< Ptr< Layer > >	getLayerInputs (LayerId layerId)
	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 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	getLayersShapes (const MatShape &netInputShape, std::vector< int > &layersIds, std::vector< std::vector< MatShape > > &inLayersShapes, std::vector< std::vector< MatShape > > &outLayersShapes) const
void	getLayerTypes (std::vector< String > &layersTypes) const
	Returns list of types for layer used in model.
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 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 int layerId, const MatShape &netInputShape, size_t &weights, size_t &blobs) const
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	getMemoryConsumption (const MatShape &netInputShape, std::vector< int > &layerIds, std::vector< size_t > &weights, std::vector< size_t > &blobs) const
Mat	getParam (LayerId layer, int numParam=0)
	Returns parameter blob of the layer.
int64	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.
std::vector< int >	getUnconnectedOutLayers () const
	Returns indexes of layers with unconnected outputs.
std::vector< String >	getUnconnectedOutLayersNames () const
	Returns names of layers with unconnected outputs.
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	setInputsNames (const std::vector< String > &inputBlobNames)
	Sets outputs names of the network input pseudo layer.
void	setParam (LayerId 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 String &xml, const String &bin)
	Create a network from Intel's Model Optimizer intermediate representation.

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, samples/dnn/openpose.cpp, and samples/dnn/text_detection.cpp.

Member Typedef Documentation

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

Container for strings and integers.

Constructor & Destructor Documentation

cv::dnn::Net::Net

(

)

Python:
	<dnn_Net object>	=	cv.dnn_Net(		)

Default constructor.

cv::dnn::Net::~Net

(

)

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

Member Function Documentation

int cv::dnn::Net::addLayer	(	const String &	name,
		const String &	type,
		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).
params	parameters which will be used to initialize the creating layer.

Returns

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

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

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

See Also

addLayer()

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

Python:
	None	=	cv.dnn_Net.connect(	outPin, 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 sting 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()

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

Python:
	None	=	cv.dnn_Net.connect(	outPin, inpPin	)

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

bool cv::dnn::Net::empty

(

)

const

Python:
	retval	=	cv.dnn_Net.empty(		)

Returns true if there are no layers in the network.

void cv::dnn::Net::enableFusion

(

bool

fusion

)

Python:
	None	=	cv.dnn_Net.enableFusion(	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.

Mat cv::dnn::Net::forward

(

const String &

outputName = String()

)

Python:
	retval	=	cv.dnn_Net.forward(	[, outputName]	)
	outputBlobs	=	cv.dnn_Net.forward(	[, outputBlobs[, outputName]]	)
	outputBlobs	=	cv.dnn_Net.forward(	outBlobNames[, outputBlobs]	)
	outputBlobs	=	cv.dnn_Net.forwardAndRetrieve(	outBlobNames	)

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.

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

Python:
	retval	=	cv.dnn_Net.forward(	[, outputName]	)
	outputBlobs	=	cv.dnn_Net.forward(	[, outputBlobs[, outputName]]	)
	outputBlobs	=	cv.dnn_Net.forward(	outBlobNames[, outputBlobs]	)
	outputBlobs	=	cv.dnn_Net.forwardAndRetrieve(	outBlobNames	)

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.

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

Python:
	retval	=	cv.dnn_Net.forward(	[, outputName]	)
	outputBlobs	=	cv.dnn_Net.forward(	[, outputBlobs[, outputName]]	)
	outputBlobs	=	cv.dnn_Net.forward(	outBlobNames[, outputBlobs]	)
	outputBlobs	=	cv.dnn_Net.forwardAndRetrieve(	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

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

Python:
	retval	=	cv.dnn_Net.forward(	[, outputName]	)
	outputBlobs	=	cv.dnn_Net.forward(	[, outputBlobs[, outputName]]	)
	outputBlobs	=	cv.dnn_Net.forward(	outBlobNames[, outputBlobs]	)
	outputBlobs	=	cv.dnn_Net.forwardAndRetrieve(	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

int64 cv::dnn::Net::getFLOPS

(

const std::vector< MatShape > &

netInputShapes

)

const

Python:
	retval	=	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	)

Computes FLOP for whole loaded model with specified input shapes.

Parameters

netInputShapes

vector of shapes for all net inputs.

Returns

computed FLOP.

int64 cv::dnn::Net::getFLOPS

(

const MatShape &

netInputShape

)

const

Python:
	retval	=	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	)

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

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

Python:
	retval	=	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	)

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

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

Python:
	retval	=	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	)

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

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

(

LayerId

layerId

)

Python:
	retval	=	cv.dnn_Net.getLayer(	layerId	)

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

Examples:

samples/dnn/colorization.cpp.

int cv::dnn::Net::getLayerId

(

const String &

layer

)

Python:
	retval	=	cv.dnn_Net.getLayerId(	layer	)

Converts string name of the layer to the integer identifier.

Returns

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

std::vector<Ptr<Layer> > cv::dnn::Net::getLayerInputs

(

LayerId

layerId

)

Returns pointers to input layers of specific layer.

std::vector<String> cv::dnn::Net::getLayerNames

(

)

const

Python:
	retval	=	cv.dnn_Net.getLayerNames(		)

int cv::dnn::Net::getLayersCount

(

const String &

layerType

)

const

Python:
	retval	=	cv.dnn_Net.getLayersCount(	layerType	)

Returns count of layers of specified type.

Parameters

layerType

type.

Returns

count of layers

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

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.

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:
	layersIds, inLayersShapes, outLayersShapes	=	cv.dnn_Net.getLayersShapes(	netInputShapes	)
	layersIds, inLayersShapes, outLayersShapes	=	cv.dnn_Net.getLayersShapes(	netInputShape	)

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

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:
	layersIds, inLayersShapes, outLayersShapes	=	cv.dnn_Net.getLayersShapes(	netInputShapes	)
	layersIds, inLayersShapes, outLayersShapes	=	cv.dnn_Net.getLayersShapes(	netInputShape	)

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

void cv::dnn::Net::getLayerTypes

(

std::vector< String > &

layersTypes

)

const

Python:
	layersTypes	=	cv.dnn_Net.getLayerTypes(		)

Returns list of types for layer used in model.

Parameters

layersTypes

output parameter for returning types.

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

Python:
	weights, blobs	=	cv.dnn_Net.getMemoryConsumption(	netInputShape	)
	weights, blobs	=	cv.dnn_Net.getMemoryConsumption(	layerId, netInputShapes	)
	weights, blobs	=	cv.dnn_Net.getMemoryConsumption(	layerId, netInputShape	)

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.

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

Python:
	weights, blobs	=	cv.dnn_Net.getMemoryConsumption(	netInputShape	)
	weights, blobs	=	cv.dnn_Net.getMemoryConsumption(	layerId, netInputShapes	)
	weights, blobs	=	cv.dnn_Net.getMemoryConsumption(	layerId, netInputShape	)

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

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

Python:
	weights, blobs	=	cv.dnn_Net.getMemoryConsumption(	netInputShape	)
	weights, blobs	=	cv.dnn_Net.getMemoryConsumption(	layerId, netInputShapes	)
	weights, blobs	=	cv.dnn_Net.getMemoryConsumption(	layerId, netInputShape	)

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

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

Python:
	weights, blobs	=	cv.dnn_Net.getMemoryConsumption(	netInputShape	)
	weights, blobs	=	cv.dnn_Net.getMemoryConsumption(	layerId, netInputShapes	)
	weights, blobs	=	cv.dnn_Net.getMemoryConsumption(	layerId, netInputShape	)

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

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:
	weights, blobs	=	cv.dnn_Net.getMemoryConsumption(	netInputShape	)
	weights, blobs	=	cv.dnn_Net.getMemoryConsumption(	layerId, netInputShapes	)
	weights, blobs	=	cv.dnn_Net.getMemoryConsumption(	layerId, netInputShape	)

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.

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

Python:
	weights, blobs	=	cv.dnn_Net.getMemoryConsumption(	netInputShape	)
	weights, blobs	=	cv.dnn_Net.getMemoryConsumption(	layerId, netInputShapes	)
	weights, blobs	=	cv.dnn_Net.getMemoryConsumption(	layerId, netInputShape	)

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

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

Python:
	retval	=	cv.dnn_Net.getParam(	layer[, numParam]	)

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

int64 cv::dnn::Net::getPerfProfile

(

std::vector< double > &

timings

)

Python:
	retval, timings	=	cv.dnn_Net.getPerfProfile(		)

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.

Parameters

timings

vector for tick timings for all layers.

Returns

overall ticks for model inference.

std::vector<int> cv::dnn::Net::getUnconnectedOutLayers

(

)

const

Python:
	retval	=	cv.dnn_Net.getUnconnectedOutLayers(		)

Returns indexes of layers with unconnected outputs.

std::vector<String> cv::dnn::Net::getUnconnectedOutLayersNames

(

)

const

Python:
	retval	=	cv.dnn_Net.getUnconnectedOutLayersNames(		)

Returns names of layers with unconnected outputs.

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

static

Python:
	retval	=	cv.dnn.Net_readFromModelOptimizer(	xml, bin	)

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

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.

void cv::dnn::Net::setHalideScheduler

(

const String &

scheduler

)

Python:
	None	=	cv.dnn_Net.setHalideScheduler(	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.

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

Python:
	None	=	cv.dnn_Net.setInput(	blob[, name[, scalefactor[, mean]]]	)

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.

void cv::dnn::Net::setInputsNames

(

const std::vector< String > &

inputBlobNames

)

Python:
	None	=	cv.dnn_Net.setInputsNames(	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.

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

Python:
	None	=	cv.dnn_Net.setParam(	layer, numParam, 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.

void cv::dnn::Net::setPreferableBackend

(

int

backendId

)

Python:
	None	=	cv.dnn_Net.setPreferableBackend(	backendId	)

Ask network to use specific computation backend where it supported.

Parameters

[in]

backendId

backend identifier.

See Also

Backend

If OpenCV is compiled with Intel's Inference Engine library, DNN_BACKEND_DEFAULT means DNN_BACKEND_INFERENCE_ENGINE. Otherwise it equals to DNN_BACKEND_OPENCV.

void cv::dnn::Net::setPreferableTarget

(

int

targetId

)

Python:
	None	=	cv.dnn_Net.setPreferableTarget(	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_TARGET_CPU	+	+	+
DNN_TARGET_OPENCL	+	+	+
DNN_TARGET_OPENCL_FP16	+	+
DNN_TARGET_MYRIAD		+
DNN_TARGET_FPGA		+

Examples:

samples/dnn/colorization.cpp.

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

• dnn/dnn.hpp