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

Public Member Functions
	Net ()

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

void	disableKVCache ()
	Disables KV-Cache for all AttentionOnnxI layers.

string	dump ()
	Dump net to String.

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

void	dumpToPbtxt (string path)
	Dump net structure, hyperparameters, backend, target and fusion to pbtxt file.

bool	empty ()

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

void	enableKVCache ()
	Enables KV-Cache for all AttentionOnnxI layers.

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.

void	finalizeNet ()
	Finalizes the network configuration and prepares it for inference.

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

void	forward (List< Mat > outputBlobs)
	Runs forward pass to compute output of layer with name `outputName`.

void	forward (List< Mat > outputBlobs, List< string > outBlobNames)
	Runs forward pass to compute outputs of layers listed in `outBlobNames`.

void	forward (List< Mat > outputBlobs, string outputName)
	Runs forward pass to compute output of layer with name `outputName`.

Mat	forward (string outputName)
	Runs forward pass to compute output of layer with name `outputName`.

void	forwardAndRetrieve (List< List< Mat > > outputBlobs, List< string > outBlobNames)
	Runs forward pass to compute outputs of layers listed in `outBlobNames`.

long	getFLOPS (List< MatOfInt > netInputShapes, MatOfInt netInputTypes)
	Computes FLOP for whole loaded model with specified input shapes.

Layer	getLayer (DictValue layerId)

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

Layer	getLayer (string layerName)

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

List< string >	getLayerNames ()

int	getLayersCount (string layerType)
	Returns count of layers of specified type.

void	getLayerShapes (List< MatOfInt > netInputShapes, MatOfInt netInputTypes, int layerId, List< MatOfInt > inLayerShapes, List< MatOfInt > outLayerShapes)

void	getLayerTypes (List< string > layersTypes)
	Returns list of types for layer used in model.

void	getMemoryConsumption (List< MatOfInt > netInputShapes, MatOfInt netInputTypes, long[] weights, long[] blobs)
	Computes bytes number which are required to store all weights and intermediate blobs for model.

IntPtr	getNativeObjAddr ()

Mat	getParam (int layer)
	Returns parameter blob of the layer.

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

Mat	getParam (string layerName)

Mat	getParam (string layerName, int numParam)

void	getPerfProfile (List< string > names, List< string > timems, List< string > counts)
	Returns profiling data captured during the last forward pass.

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

MatOfInt	getUnconnectedOutLayers ()
	Returns indexes of layers with unconnected outputs.

List< string >	getUnconnectedOutLayersNames ()
	Returns names of layers with unconnected outputs.

void	printPerfProfile ()
	Prints the profile captured during the last forward pass in a formatted table using CV_LOG_INFO.

int	registerOutput (string outputName, int layerId, int outputPort)
	Registers network output with name.

void	resetKVCache ()
	Resets KV-Cache for all AttentionOnnxI layers.

void	setInput (Mat blob)
	Sets the new input value for the network.

void	setInput (Mat blob, string name)
	Sets the new input value for the network.

void	setInput (Mat blob, string name, double scalefactor)
	Sets the new input value for the network.

void	setInput (Mat blob, string name, double scalefactor, in Vec4d mean)
	Sets the new input value for the network.

void	setInput (Mat blob, string name, double scalefactor, in(double v0, double v1, double v2, double v3) mean)
	Sets the new input value for the network.

void	setInput (Mat blob, string name, double scalefactor, Scalar mean)
	Sets the new input value for the network.

void	setInputShape (string inputName, MatOfInt shape)
	Specify shape of network input.

void	setInputsNames (List< string > inputBlobNames)
	Sets outputs names of the network input pseudo layer.

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

void	setParam (string layerName, int numParam, Mat blob)
	Sets the parameter blob of a layer identified by its name or output tensor name.

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.

Public Member Functions inherited from OpenCVForUnity.DisposableObject
void	Dispose ()
	Releases resources used by this object.

void	ThrowIfDisposed ()
	Throws ObjectDisposedException if this object has been disposed.

Static Public Member Functions
static Net	__fromPtr__ (IntPtr addr)

static Net	readFromModelOptimizer (MatOfByte bufferModelConfig, MatOfByte bufferWeights)
	Create a network from Intel's Model Optimizer in-memory buffers with intermediate representation (IR).

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

Static Public Member Functions inherited from OpenCVForUnity.DisposableObject
static IntPtr	ThrowIfNullIntPtr (IntPtr ptr)
	Returns the native pointer, or throws CoreModule.CvException if it is zero.

Protected Member Functions
override void	Dispose (bool disposing)

Protected Member Functions inherited from OpenCVForUnity.DisposableOpenCVObject
	DisposableOpenCVObject ()
	Initializes a new instance with a zero native pointer and dispose enabled.

	DisposableOpenCVObject (bool isEnabledDispose)
	Initializes a new instance with a zero native pointer.

	DisposableOpenCVObject (IntPtr ptr)
	Initializes a new instance with the specified native pointer and dispose enabled.

	DisposableOpenCVObject (IntPtr ptr, bool isEnabledDispose)
	Initializes a new instance with the specified native pointer.

Protected Member Functions inherited from OpenCVForUnity.DisposableObject
	DisposableObject ()
	Initializes a new instance with dispose enabled.

	DisposableObject (bool isEnabledDispose)
	Initializes a new instance.

Additional Inherited Members
Package Attributes inherited from OpenCVForUnity.DisposableOpenCVObject
Properties inherited from OpenCVForUnity.DisposableObject
bool	IsDisposed `[get, protected set]`

bool	IsEnabledDispose `[get, set]`

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.

Constructor & Destructor Documentation

◆ Net()

OpenCVForUnity.DnnModule.Net.Net ( )

Member Function Documentation

◆ fromPtr()

static Net OpenCVForUnity.DnnModule.Net.__fromPtr__ ( IntPtr addr )

static

◆ connect()

void OpenCVForUnity.DnnModule.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]</DFN>:
            - the first part of the template <DFN>layer_name</DFN> 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 <DFN>input_number</DFN>
              is either number of the layer input, either label one.
              If this part is omitted then the first layer input will be used.
 
             @see setNetInputs(), Layer::inputNameToIndex(), Layer::outputNameToIndex()
</remarks> 

◆ disableKVCache()

void OpenCVForUnity.DnnModule.Net.disableKVCache ( )

Disables KV-Cache for all AttentionOnnxI layers.

◆ Dispose()

override void OpenCVForUnity.DnnModule.Net.Dispose ( bool disposing )

protectedvirtual

Reimplemented from OpenCVForUnity.DisposableOpenCVObject.

◆ dump()

string OpenCVForUnity.DnnModule.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 OpenCVForUnity.DnnModule.Net.dumpToFile ( string path )

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

Parameters

path	path to output file with .dot extension

dump()

◆ dumpToPbtxt()

void OpenCVForUnity.DnnModule.Net.dumpToPbtxt ( string path )

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 OpenCVForUnity.DnnModule.Net.empty ( )

Returns true if there are no layers in the network.

◆ enableFusion()

void OpenCVForUnity.DnnModule.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.

◆ enableKVCache()

void OpenCVForUnity.DnnModule.Net.enableKVCache ( )

Enables KV-Cache for all AttentionOnnxI layers.

◆ enableWinograd()

void OpenCVForUnity.DnnModule.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.

◆ finalizeNet()

void OpenCVForUnity.DnnModule.Net.finalizeNet ( )

Finalizes the network configuration and prepares it for inference.

This method must be called after setting backend/target via setPreferableBackend() and setPreferableTarget(), and before the first forward() call. It creates the underlying execution session (e.g. ONNX Runtime session) on the configured backend/target. If not called explicitly, the first forward() will call it automatically.

Calling finalizeNet() early lets you pay the one-time setup cost at a predictable point and catch configuration errors before inference.

◆ forward() [1/5]

Mat OpenCVForUnity.DnnModule.Net.forward ( )

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.

◆ forward() [2/5]

void OpenCVForUnity.DnnModule.Net.forward ( List< Mat > 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() [3/5]

void OpenCVForUnity.DnnModule.Net.forward	(	List< Mat >	outputBlobs,
		List< 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() [4/5]

void OpenCVForUnity.DnnModule.Net.forward	(	List< Mat >	outputBlobs,
		string	outputName )

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() [5/5]

Mat OpenCVForUnity.DnnModule.Net.forward ( string outputName )

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.

◆ forwardAndRetrieve()

void OpenCVForUnity.DnnModule.Net.forwardAndRetrieve	(	List< List< Mat > >	outputBlobs,
		List< 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

◆ getFLOPS()

long OpenCVForUnity.DnnModule.Net.getFLOPS	(	List< MatOfInt >	netInputShapes,
		MatOfInt	netInputTypes )

Computes FLOP for whole loaded model with specified input shapes.

Parameters

netInputShapes	vector of shapes for all net inputs.
netInputTypes	vector of types for all net inputs.

Returns: computed FLOP.

◆ getLayer() [1/3]

Layer OpenCVForUnity.DnnModule.Net.getLayer ( DictValue layerId )

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]

Layer OpenCVForUnity.DnnModule.Net.getLayer ( int layerId )

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

◆ getLayer() [3/3]

Layer OpenCVForUnity.DnnModule.Net.getLayer ( string layerName )

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)

◆ getLayerId()

int OpenCVForUnity.DnnModule.Net.getLayerId ( string layer )

Converts string name of the layer to the integer identifier.

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

◆ getLayerNames()

List< string > OpenCVForUnity.DnnModule.Net.getLayerNames ( )

◆ getLayersCount()

int OpenCVForUnity.DnnModule.Net.getLayersCount ( string layerType )

Returns count of layers of specified type.

Parameters

layerType type.

Returns: count of layers

◆ getLayerShapes()

void OpenCVForUnity.DnnModule.Net.getLayerShapes	(	List< MatOfInt >	netInputShapes,
		MatOfInt	netInputTypes,
		int	layerId,
		List< MatOfInt >	inLayerShapes,
		List< MatOfInt >	outLayerShapes )

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

       The only overload of getLayerShapes that should be kept in 5.x

◆ getLayerTypes()

void OpenCVForUnity.DnnModule.Net.getLayerTypes ( List< string > layersTypes )

Returns list of types for layer used in model.

Parameters

layersTypes output parameter for returning types.

◆ getMemoryConsumption()

void OpenCVForUnity.DnnModule.Net.getMemoryConsumption	(	List< MatOfInt >	netInputShapes,
		MatOfInt	netInputTypes,
		long[]	weights,
		long[]	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.
netInputTypes	vector of types for all net inputs.
weights	output parameter to store resulting bytes for weights.
blobs	output parameter to store resulting bytes for intermediate blobs.

◆ getNativeObjAddr()

IntPtr OpenCVForUnity.DnnModule.Net.getNativeObjAddr ( )

◆ getParam() [1/4]

Mat OpenCVForUnity.DnnModule.Net.getParam ( int layer )

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.

Layer::blobs

◆ getParam() [2/4]

Mat OpenCVForUnity.DnnModule.Net.getParam	(	int	layer,
		int	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.

Layer::blobs

◆ getParam() [3/4]

Mat OpenCVForUnity.DnnModule.Net.getParam ( string layerName )

◆ getParam() [4/4]

Mat OpenCVForUnity.DnnModule.Net.getParam	(	string	layerName,
		int	numParam )

◆ getPerfProfile() [1/2]

void OpenCVForUnity.DnnModule.Net.getPerfProfile	(	List< string >	names,
		List< string >	timems,
		List< string >	counts )

Returns profiling data captured during the last forward pass.

Entries are sorted by time in descending order. Empty vectors are returned if profiling is disabled (DNN_PROFILE_NONE).

◆ getPerfProfile() [2/2]

long OpenCVForUnity.DnnModule.Net.getPerfProfile ( MatOfDouble 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

timings vector for tick timings for all layers.

Returns: overall ticks for model inference.

◆ getUnconnectedOutLayers()

MatOfInt OpenCVForUnity.DnnModule.Net.getUnconnectedOutLayers ( )

Returns indexes of layers with unconnected outputs.

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

◆ getUnconnectedOutLayersNames()

List< string > OpenCVForUnity.DnnModule.Net.getUnconnectedOutLayersNames ( )

Returns names of layers with unconnected outputs.

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

◆ printPerfProfile()

void OpenCVForUnity.DnnModule.Net.printPerfProfile ( )

Prints the profile captured during the last forward pass in a formatted table using CV_LOG_INFO.

In DNN_PROFILE_DETAILED mode, prints per-layer label, time, and percentage. In DNN_PROFILE_SUMMARY mode, prints per-type count, time, and percentage. Does nothing if profiling is disabled (DNN_PROFILE_NONE) or all timings are zero.

◆ readFromModelOptimizer() [1/2]

static Net OpenCVForUnity.DnnModule.Net.readFromModelOptimizer	(	MatOfByte	bufferModelConfig,
		MatOfByte	bufferWeights )

static

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

Parameters

bufferModelConfig	buffer with model's configuration.
bufferWeights	buffer with model's trained weights.

Returns: Net object.

◆ readFromModelOptimizer() [2/2]

static Net OpenCVForUnity.DnnModule.Net.readFromModelOptimizer	(	string	xml,
		string	bin )

static

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

Parameters

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

◆ registerOutput()

int OpenCVForUnity.DnnModule.Net.registerOutput	(	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)

◆ resetKVCache()

void OpenCVForUnity.DnnModule.Net.resetKVCache ( )

Resets KV-Cache for all AttentionOnnxI layers.

◆ setInput() [1/6]

void OpenCVForUnity.DnnModule.Net.setInput ( Mat blob )

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.

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

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

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

◆ setInput() [2/6]

void OpenCVForUnity.DnnModule.Net.setInput	(	Mat	blob,
		string	name )

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.

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

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

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

◆ setInput() [3/6]

void OpenCVForUnity.DnnModule.Net.setInput	(	Mat	blob,
		string	name,
		double	scalefactor )

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.

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

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

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

◆ setInput() [4/6]

void OpenCVForUnity.DnnModule.Net.setInput	(	Mat	blob,
		string	name,
		double	scalefactor,
		in Vec4d	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.

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

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

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

◆ setInput() [5/6]

void OpenCVForUnity.DnnModule.Net.setInput	(	Mat	blob,
		string	name,
		double	scalefactor,
		in(double v0, double v1, double v2, double v3)	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.

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

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

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

◆ setInput() [6/6]

void OpenCVForUnity.DnnModule.Net.setInput	(	Mat	blob,
		string	name,
		double	scalefactor,
		Scalar	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.

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

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

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

◆ setInputShape()

void OpenCVForUnity.DnnModule.Net.setInputShape	(	string	inputName,
		MatOfInt	shape )

Specify shape of network input.

◆ setInputsNames()

void OpenCVForUnity.DnnModule.Net.setInputsNames ( List< 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 OpenCVForUnity.DnnModule.Net.setParam	(	int	layer,
		int	numParam,
		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.

Layer::blobs

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

◆ setParam() [2/2]

void OpenCVForUnity.DnnModule.Net.setParam	(	string	layerName,
		int	numParam,
		Mat	blob )

Sets the parameter blob of a layer identified by its name or output tensor name.

Parameters

layerName	layer name (classic engine) or raw ONNX output tensor name (ENGINE_NEW).
numParam	index of the constant weight input to update (0 = kernel, 1 = bias, etc.).
blob	the new parameter value.

◆ setPreferableBackend()

void OpenCVForUnity.DnnModule.Net.setPreferableBackend ( int backendId )

Ask network to use specific computation backend where it supported.

Parameters

backendId backend identifier.

Backend

◆ setPreferableTarget()

void OpenCVForUnity.DnnModule.Net.setPreferableTarget ( int targetId )

Ask network to make computations on specific target device.

Parameters

targetId target identifier.

Target

List of supported combinations backend / target:

	DNN_BACKEND_OPENCV	DNN_BACKEND_INFERENCE_ENGINE	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		+

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

OpenCVForUnity/Assets/OpenCVForUnity/Scripts/Core/org/opencv/dnn/Net.cs
OpenCVForUnity/Assets/OpenCVForUnity/Scripts/Extensions/org/opencv/dnn/Net_Struct.cs
OpenCVForUnity/Assets/OpenCVForUnity/Scripts/Extensions/org/opencv/dnn/Net_ValueTuple.cs

Public Member Functions

Static Public Member Functions

Protected Member Functions

Additional Inherited Members

Detailed Description

Constructor & Destructor Documentation

◆ Net()

Member Function Documentation

◆ __fromPtr__()

◆ connect()

◆ disableKVCache()

◆ Dispose()

◆ dump()

◆ dumpToFile()

◆ dumpToPbtxt()

◆ empty()

◆ enableFusion()

◆ enableKVCache()

◆ enableWinograd()

◆ finalizeNet()

◆ forward() [1/5]

◆ forward() [2/5]

◆ forward() [3/5]

◆ forward() [4/5]

◆ forward() [5/5]

◆ forwardAndRetrieve()

◆ getFLOPS()

◆ getLayer() [1/3]

◆ getLayer() [2/3]

◆ getLayer() [3/3]

◆ getLayerId()

◆ getLayerNames()

◆ getLayersCount()

◆ getLayerShapes()

◆ getLayerTypes()

◆ getMemoryConsumption()

◆ getNativeObjAddr()

◆ getParam() [1/4]

◆ getParam() [2/4]

◆ getParam() [3/4]

◆ getParam() [4/4]

◆ getPerfProfile() [1/2]

◆ getPerfProfile() [2/2]

◆ getUnconnectedOutLayers()

◆ getUnconnectedOutLayersNames()

◆ printPerfProfile()

◆ readFromModelOptimizer() [1/2]

◆ readFromModelOptimizer() [2/2]

◆ registerOutput()

◆ resetKVCache()

◆ setInput() [1/6]

◆ setInput() [2/6]

◆ setInput() [3/6]

◆ setInput() [4/6]

◆ setInput() [5/6]

◆ setInput() [6/6]

◆ setInputShape()

◆ setInputsNames()

◆ setParam() [1/2]

◆ setParam() [2/2]

◆ setPreferableBackend()

◆ setPreferableTarget()

◆ fromPtr()