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)

• 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.

• 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

• 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.

• 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

• 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().

• empty

  public boolean empty()

  Returns true if there are no layers in the network.

  Returns:

  automatically generated

• 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.

• getLayersCount

  public int getLayersCount​(java.lang.String layerType)

  Returns count of layers of specified type.

  Parameters:

  layerType - type.

  Returns:

  count of layers

• getFLOPS

  public long getFLOPS​(MatOfInt netInputShape)

• getFLOPS

  public long getFLOPS​(int layerId,
                       MatOfInt netInputShape)

• getFLOPS

  public long getFLOPS​(int layerId,
                       java.util.List<MatOfInt> netInputShapes)

• 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.

• 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.

• getLayerNames

  public java.util.List<java.lang.String> getLayerNames()

• getUnconnectedOutLayersNames

  public java.util.List<java.lang.String> getUnconnectedOutLayersNames()

  Returns names of layers with unconnected outputs.

  Returns:

  automatically generated

• getUnconnectedOutLayers

  public MatOfInt getUnconnectedOutLayers()

  Returns indexes of layers with unconnected outputs.

  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>&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()

• 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()

• 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.

• 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

• 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.

• getMemoryConsumption

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

• getMemoryConsumption

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

• getMemoryConsumption

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

• 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.

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

• setInputShape

  public void setInputShape​(java.lang.String inputName,
                            MatOfInt shape)

  Specify shape of network input.

  Parameters:

  inputName - automatically generated

  shape - automatically generated

• 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

• 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.

• 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 | | | | + |

• finalize

  protected void finalize()
                   throws java.lang.Throwable

  Overrides:

  finalize in class java.lang.Object

  Throws:

  java.lang.Throwable