org.opencv.dnn

Class Net

java.lang.Object

org.opencv.dnn.Net

public class Net
extends Object

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.

Field Summary

Fields

Modifier and Type	Field and Description
protected long	nativeObj

Constructor Summary

Constructors

Modifier	Constructor and Description
	Net()
protected	Net(long addr)

Method Summary

Modifier and Type	Method and Description
static Net	__fromPtr__(long addr)
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(String path) Dump net structure, hyperparameters, backend, target and fusion to dot file
boolean	empty() Returns true if there are no layers in the network.
void	enableFusion(boolean fusion) Enables or disables layer fusion in the network.
protected void	finalize()
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.
long	getFLOPS(int layerId, List<MatOfInt> netInputShapes)
long	getFLOPS(int layerId, MatOfInt netInputShape)
long	getFLOPS(List<MatOfInt> netInputShapes) Computes FLOP for whole loaded model with specified input shapes.
long	getFLOPS(MatOfInt netInputShape)
Layer	getLayer(DictValue layerId) Returns pointer to layer with specified id or name which the network use.
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	getLayerTypes(List<String> layersTypes) Returns list of types for layer used in model.
void	getMemoryConsumption(int layerId, List<MatOfInt> netInputShapes, long[] weights, long[] blobs)
void	getMemoryConsumption(int layerId, MatOfInt netInputShape, long[] weights, long[] blobs)
void	getMemoryConsumption(MatOfInt netInputShape, long[] weights, long[] blobs)
long	getNativeObjAddr()
Mat	getParam(DictValue layer) Returns parameter blob of the layer.
Mat	getParam(DictValue layer, int numParam) Returns parameter blob of the layer.
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.
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).
void	setHalideScheduler(String scheduler) Compile Halide 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, 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(DictValue layer, int numParam, 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.

Methods inherited from class java.lang.Object

clone, equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Field Detail

nativeObj

protected final long nativeObj

Constructor Detail

Net

protected Net(long addr)

Net

public Net()

Method Detail

getNativeObjAddr

public long getNativeObjAddr()

__fromPtr__

public static Net __fromPtr__(long addr)

readFromModelOptimizer

public static Net readFromModelOptimizer(String xml,
                                         String bin)

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.

Returns:

automatically generated

readFromModelOptimizer

public static Net readFromModelOptimizer(MatOfByte bufferModelConfig,
                                         MatOfByte bufferWeights)

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.

empty

public boolean empty()

Returns true if there are no layers in the network.

Returns:

automatically generated

dump

public String 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

public void dumpToFile(String path)

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

Parameters:

path - path to output file with .dot extension SEE: dump()

getLayerId

public int getLayerId(String layer)

Converts string name of the layer to the integer identifier.

Parameters:

layer - automatically generated

Returns:

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

getLayerNames

public List<String> getLayerNames()

getLayer

public Layer getLayer(DictValue layerId)

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

Parameters:

layerId - automatically generated

Returns:

automatically generated

connect

public void 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 <DFN>&lt;layer_name&gt;[.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()

setInputsNames

public void 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.

Parameters:

inputBlobNames - automatically generated

setInputShape

public void setInputShape(String inputName,
                          MatOfInt shape)

Specify shape of network input.

Parameters:

inputName - automatically generated

shape - automatically generated

forward

public Mat 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.

forward

public Mat forward()

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

Returns:

blob for first output of specified layer. By default runs forward pass for the whole network.

forward

public void 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

public void forward(List<Mat> outputBlobs)

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

Parameters:

outputBlobs - contains all output blobs for specified layer. If outputName is empty, runs forward pass for the whole network.

forward

public void 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

setHalideScheduler

public void setHalideScheduler(String scheduler)

Compile Halide layers.

Parameters:

scheduler - Path to YAML file with scheduling directives. SEE: 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.

setPreferableBackend

public void setPreferableBackend(int backendId)

Ask network to use specific computation backend where it supported.

Parameters:

backendId - backend identifier. SEE: 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

public void setPreferableTarget(int targetId)

Ask network to make computations on specific target device.

Parameters:

targetId - target identifier. SEE: 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 | | + | | |

setInput

public void 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. SEE: 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

public void 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. SEE: 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

public void 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. SEE: 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

public void setInput(Mat blob)

Sets the new input value for the network

Parameters:

blob - A new blob. Should have CV_32F or CV_8U depth. SEE: connect(String, String) to know format of the descriptor. If scale or mean values are specified, a final input blob is computed as: \(input(n,c,h,w) = scalefactor \times (blob(n,c,h,w) - mean_c)\)

setParam

public void setParam(DictValue 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. SEE: Layer::blobs Note: If shape of the new blob differs from the previous shape, then the following forward pass may fail.

getParam

public Mat getParam(DictValue 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. SEE: Layer::blobs

Returns:

automatically generated

getParam

public Mat getParam(DictValue layer)

Returns parameter blob of the layer.

Parameters:

layer - name or id of the layer. SEE: Layer::blobs

Returns:

automatically generated

getUnconnectedOutLayers

public MatOfInt getUnconnectedOutLayers()

Returns indexes of layers with unconnected outputs.

Returns:

automatically generated

getUnconnectedOutLayersNames

public List<String> getUnconnectedOutLayersNames()

Returns names of layers with unconnected outputs.

Returns:

automatically generated

getFLOPS

public long getFLOPS(List<MatOfInt> netInputShapes)

Computes FLOP for whole loaded model with specified input shapes.

Parameters:

netInputShapes - vector of shapes for all net inputs.

Returns:

computed FLOP.

getFLOPS

public long getFLOPS(MatOfInt netInputShape)

getFLOPS

public long getFLOPS(int layerId,
                     List<MatOfInt> netInputShapes)

getFLOPS

public long getFLOPS(int layerId,
                     MatOfInt netInputShape)

getLayerTypes

public void getLayerTypes(List<String> layersTypes)

Returns list of types for layer used in model.

Parameters:

layersTypes - output parameter for returning types.

getLayersCount

public int getLayersCount(String layerType)

Returns count of layers of specified type.

Parameters:

layerType - type.

Returns:

count of layers

getMemoryConsumption

public void getMemoryConsumption(MatOfInt netInputShape,
                                 long[] weights,
                                 long[] blobs)

getMemoryConsumption

public void getMemoryConsumption(int layerId,
                                 List<MatOfInt> netInputShapes,
                                 long[] weights,
                                 long[] blobs)

getMemoryConsumption

public void getMemoryConsumption(int layerId,
                                 MatOfInt netInputShape,
                                 long[] weights,
                                 long[] blobs)

enableFusion

public void enableFusion(boolean 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.

getPerfProfile

public long 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.

Parameters:

timings - vector for tick timings for all layers.

Returns:

overall ticks for model inference.

finalize

protected void finalize()
                 throws Throwable

Overrides:

finalize in class Object

Throws:

Throwable