java.lang.Object
- org.opencv.dnn.Net

```
public class Net
extends java.lang.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 Description

protected long nativeObj

Constructor Summary

Constructors
Modifier Constructor Description

Net()

protected Net(long addr)

Method Summary

All Methods Static Methods Instance Methods Concrete Methods
Modifier and Type	Method	Description
`static Net`	`__fromPtr__(long addr)`
`void`	`connect(java.lang.String outPin, java.lang.String inpPin)`	Connects output of the first layer to input of the second layer.
`java.lang.String`	`dump()`	Dump net to String
`void`	`dumpToFile(java.lang.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`.
`Mat`	`forward(java.lang.String outputName)`	Runs forward pass to compute output of layer with name `outputName`.
`void`	`forward(java.util.List<Mat> outputBlobs)`	Runs forward pass to compute output of layer with name `outputName`.
`void`	`forward(java.util.List<Mat> outputBlobs, java.lang.String outputName)`	Runs forward pass to compute output of layer with name `outputName`.
`void`	`forward(java.util.List<Mat> outputBlobs, java.util.List<java.lang.String> outBlobNames)`	Runs forward pass to compute outputs of layers listed in `outBlobNames`.
`long`	`getFLOPS(int layerId, java.util.List<MatOfInt> netInputShapes)`
`long`	`getFLOPS(int layerId, MatOfInt netInputShape)`
`long`	`getFLOPS(java.util.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(java.lang.String layer)`	Converts string name of the layer to the integer identifier.
`java.util.List<java.lang.String>`	`getLayerNames()`
`int`	`getLayersCount(java.lang.String layerType)`	Returns count of layers of specified type.
`void`	`getLayerTypes(java.util.List<java.lang.String> layersTypes)`	Returns list of types for layer used in model.
`void`	`getMemoryConsumption(int layerId, java.util.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.
`java.util.List<java.lang.String>`	`getUnconnectedOutLayersNames()`	Returns names of layers with unconnected outputs.
`static Net`	`readFromModelOptimizer(java.lang.String xml, java.lang.String bin)`	Create a network from Intel's Model Optimizer intermediate representation (IR).
`static Net`	`readFromModelOptimizer(MatOfByte bufferModelConfig, MatOfByte bufferWeights)`	Create a network from Intel's Model Optimizer in-memory buffers with intermediate representation (IR).
`void`	`setHalideScheduler(java.lang.String scheduler)`	Compile Halide layers.
`void`	`setInput(Mat blob)`	Sets the new input value for the network
`void`	`setInput(Mat blob, java.lang.String name)`	Sets the new input value for the network
`void`	`setInput(Mat blob, java.lang.String name, double scalefactor)`	Sets the new input value for the network
`void`	`setInput(Mat blob, java.lang.String name, double scalefactor, Scalar mean)`	Sets the new input value for the network
`void`	`setInputShape(java.lang.String inputName, MatOfInt shape)`	Specify shape of network input.
`void`	`setInputsNames(java.util.List<java.lang.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(java.lang.String xml,
                                         java.lang.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 java.lang.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(java.lang.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(java.lang.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 java.util.List<java.lang.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(java.lang.String outPin,
                    java.lang.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><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()
  - setInputsNames
```
public void setInputsNames(java.util.List<java.lang.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(java.lang.String inputName,
                          MatOfInt shape)
```
    Specify shape of network input.
    
    Parameters:
    
    inputName - automatically generated
    
    shape - automatically generated
  - forward
```
public Mat forward(java.lang.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(java.util.List<Mat> outputBlobs,
                    java.lang.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(java.util.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(java.util.List<Mat> outputBlobs,
                    java.util.List<java.lang.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(java.lang.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,
                     java.lang.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,
                     java.lang.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,
                     java.lang.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 java.util.List<java.lang.String> getUnconnectedOutLayersNames()
```
    Returns names of layers with unconnected outputs.
    
    Returns:
    
    automatically generated
  - getFLOPS
```
public long getFLOPS(java.util.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,
                     java.util.List<MatOfInt> netInputShapes)
```
  - getFLOPS
```
public long getFLOPS(int layerId,
                     MatOfInt netInputShape)
```
  - getLayerTypes
```
public void getLayerTypes(java.util.List<java.lang.String> layersTypes)
```
    Returns list of types for layer used in model.
    
    Parameters:
    
    layersTypes - output parameter for returning types.
  - getLayersCount
```
public int getLayersCount(java.lang.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,
                                 java.util.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. 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.
  - finalize
```
protected void finalize()
                 throws java.lang.Throwable
```
    Overrides:
    
    finalize in class java.lang.Object
    
    Throws:
    
    java.lang.Throwable

Constructors
Modifier	Constructor	Description
	`Net()`
`protected`	`Net(long addr)`

Class Net

Field Summary

Constructor Summary

Method Summary

Methods inherited from class java.lang.Object

Field Detail

nativeObj

Constructor Detail

Net

Net

Method Detail

getNativeObjAddr

__fromPtr__

readFromModelOptimizer

readFromModelOptimizer

empty

dump

dumpToFile

getLayerId

getLayerNames

getLayer

connect

setInputsNames

setInputShape

forward

forward

forward

forward

forward

setHalideScheduler

setPreferableBackend

setPreferableTarget

setInput

setInput

setInput

setInput

setParam

getParam

getParam

getUnconnectedOutLayers

getUnconnectedOutLayersNames

getFLOPS

getFLOPS

getFLOPS

getFLOPS

getLayerTypes

getLayersCount

getMemoryConsumption

getMemoryConsumption

getMemoryConsumption

enableFusion

getPerfProfile

finalize

fromPtr