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

#include <dnn.hpp>

公開型
typedef DictValue	LayerId
	Container for strings and integers.

公開メンバ関数
CV_WRAP	Net ()
	Default constructor.

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

CV_WRAP bool	empty () const

CV_WRAP String	dump ()
	Dump net to String [詳解]

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

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

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. [詳解]

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

CV_WRAP std::vector< String >	getLayerNames () const

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

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

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

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

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

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

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

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

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

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

CV_WRAP_AS(forwardAndRetrieve) void forward(CV_OUT std CV_WRAP void	setHalideScheduler (const String &scheduler)
	Runs forward pass to compute outputs of layers listed in `outBlobNames`. [詳解]

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

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

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

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

CV_WRAP Mat	getParam (LayerId layer, int numParam=0)
	Returns parameter blob of the layer. [詳解]

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

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

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

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

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

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

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

CV_WRAP int64	getFLOPS (const MatShape &netInputShape) const

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

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

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

CV_WRAP int	getLayersCount (const String &layerType) const
	Returns count of layers of specified type. [詳解]

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

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

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

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

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

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

CV_WRAP void	enableFusion (bool fusion)
	Enables or disables layer fusion in the network. [詳解]

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

静的公開メンバ関数
static CV_WRAP Net	readFromModelOptimizer (const String &xml, const String &bin)
	Create a network from Intel's Model Optimizer intermediate representation (IR). [詳解]

static CV_WRAP 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). [詳解]

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). [詳解]

詳解

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.

関数詳解

◆ addLayer()

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

Adds new layer to the net.

引数

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.

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

◆ addLayerToPrev()

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.

参照: addLayer()

◆ 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.

引数

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]

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

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

引数

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.

参照
setNetInputs(), Layer::inputNameToIndex(), Layer::outputNameToIndex()

◆ dump()

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

Dump net to String

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

◆ dumpToFile()

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

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

引数

path	path to output file with .dot extension

参照: dump()

◆ empty()

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

Returns true if there are no layers in the network.

◆ enableFusion()

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

Enables or disables layer fusion in the network.

引数

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

◆ forward() [1/3]

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

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

引数

outputName name for layer which output is needed to get

戻り値: blob for first output of specified layer.

By default runs forward pass for the whole network.

◆ forward() [2/3]

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

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

引数

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

◆ forward() [3/3]

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

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

引数

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.

◆ forwardAsync()

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

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

引数

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]

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

これはオーバーロードされたメンバ関数です。利便性のために用意されています。元の関数との違いは引き数のみです。

◆ getFLOPS() [2/4]

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

これはオーバーロードされたメンバ関数です。利便性のために用意されています。元の関数との違いは引き数のみです。

◆ getFLOPS() [3/4]

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

これはオーバーロードされたメンバ関数です。利便性のために用意されています。元の関数との違いは引き数のみです。

◆ getFLOPS() [4/4]

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

Computes FLOP for whole loaded model with specified input shapes.

引数

netInputShapes vector of shapes for all net inputs.

戻り値: computed FLOP.

◆ getLayerId()

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

Converts string name of the layer to the integer identifier.

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

◆ getLayersCount()

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

Returns count of layers of specified type.

引数

layerType type.

戻り値: count of layers

◆ getLayerShapes() [1/2]

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

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

引数

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,
		CV_OUT std::vector< MatShape > &	inLayerShapes,
		CV_OUT std::vector< MatShape > &	outLayerShapes
	)		const

これはオーバーロードされたメンバ関数です。利便性のために用意されています。元の関数との違いは引き数のみです。

◆ getLayersShapes() [1/2]

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

これはオーバーロードされたメンバ関数です。利便性のために用意されています。元の関数との違いは引き数のみです。

◆ getLayersShapes() [2/2]

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

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

引数

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()

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

Returns list of types for layer used in model.

引数

layersTypes output parameter for returning types.

◆ getMemoryConsumption() [1/6]

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

これはオーバーロードされたメンバ関数です。利便性のために用意されています。元の関数との違いは引き数のみです。

◆ getMemoryConsumption() [2/6]

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

これはオーバーロードされたメンバ関数です。利便性のために用意されています。元の関数との違いは引き数のみです。

◆ getMemoryConsumption() [3/6]

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

これはオーバーロードされたメンバ関数です。利便性のために用意されています。元の関数との違いは引き数のみです。

◆ getMemoryConsumption() [4/6]

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

これはオーバーロードされたメンバ関数です。利便性のために用意されています。元の関数との違いは引き数のみです。

◆ getMemoryConsumption() [5/6]

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

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

引数

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,
		CV_OUT std::vector< int > &	layerIds,
		CV_OUT std::vector< size_t > &	weights,
		CV_OUT std::vector< size_t > &	blobs
	)		const

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

引数

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.

◆ getParam()

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

Returns parameter blob of the layer.

引数

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

参照: Layer::blobs

◆ getPerfProfile()

CV_WRAP int64 cv::dnn::Net::getPerfProfile ( CV_OUT 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.

引数

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

戻り値: overall ticks for model inference.

◆ readFromModelOptimizer() [1/3]

static CV_WRAP 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).

引数

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

戻り値: Net object.

◆ readFromModelOptimizer() [2/3]

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

static

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

引数

[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).

引数

[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.

戻り値: Net object.

◆ setHalideScheduler()

CV_WRAP_AS(forwardAndRetrieve) void forward(CV_OUT std CV_WRAP void cv::dnn::Net::setHalideScheduler ( const String & scheduler )

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

引数

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

Compile Halide layers.

引数

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

参照: 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()

CV_WRAP 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

引数

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.

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

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

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

◆ setInputsNames()

CV_WRAP 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()

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

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

引数

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

参照: Layer::blobs

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

◆ setPreferableBackend()

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

Ask network to use specific computation backend where it supported.

引数

[in] backendId backend identifier.

参照: 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.

◆ setPreferableTarget()

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

Ask network to make computations on specific target device.

引数

[in] targetId target identifier.

参照: 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		+

このクラス詳解は次のファイルから抽出されました:

dnn/dnn.hpp

公開型

公開メンバ関数

静的公開メンバ関数

詳解

関数詳解

◆ addLayer()

◆ addLayerToPrev()

◆ connect() [1/2]

◆ connect() [2/2]

◆ dump()

◆ dumpToFile()

◆ empty()

◆ enableFusion()

◆ forward() [1/3]

◆ forward() [2/3]

◆ forward() [3/3]

◆ forwardAsync()

◆ getFLOPS() [1/4]

◆ getFLOPS() [2/4]

◆ getFLOPS() [3/4]

◆ getFLOPS() [4/4]

◆ getLayerId()

◆ 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]

◆ getParam()

◆ getPerfProfile()

◆ readFromModelOptimizer() [1/3]

◆ readFromModelOptimizer() [2/3]

◆ readFromModelOptimizer() [3/3]

◆ setHalideScheduler()

◆ setInput()

◆ setInputsNames()

◆ setParam()

◆ setPreferableBackend()

◆ setPreferableTarget()