public class Net extends Object
Modifier and Type | Field and Description |
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protected long |
nativeObj |
Modifier | Constructor and Description |
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|
Net() |
protected |
Net(long addr) |
Modifier and Type | Method and Description |
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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.
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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.
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static Net |
readFromModelOptimizer(MatOfByte bufferModelConfig,
MatOfByte bufferWeights)
Create a network from Intel's Model Optimizer in-memory buffers with intermediate representation (IR).
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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.
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void |
setPreferableTarget(int targetId)
Ask network to make computations on specific target device.
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public long getNativeObjAddr()
public static Net __fromPtr__(long addr)
public Mat forward(String outputName)
outputName
.outputName
- name for layer which output is needed to getpublic Mat forward()
outputName
.public Mat getParam(DictValue layer, int numParam)
layer
- name or id of the layer.numParam
- index of the layer parameter in the Layer::blobs array.
SEE: Layer::blobspublic Mat getParam(DictValue layer)
layer
- name or id of the layer.
SEE: Layer::blobspublic static Net readFromModelOptimizer(String xml, String bin)
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.public static Net readFromModelOptimizer(MatOfByte bufferModelConfig, MatOfByte bufferWeights)
bufferModelConfig
- buffer with model's configuration.bufferWeights
- buffer with model's trained weights.public Layer getLayer(DictValue layerId)
layerId
- automatically generatedpublic String dump()
public boolean empty()
public int getLayerId(String layer)
layer
- automatically generatedpublic int getLayersCount(String layerType)
layerType
- type.public long getFLOPS(MatOfInt netInputShape)
public long getFLOPS(int layerId, MatOfInt netInputShape)
public long getFLOPS(List<MatOfInt> netInputShapes)
netInputShapes
- vector of shapes for all net inputs.public long getPerfProfile(MatOfDouble timings)
timings
- vector for tick timings for all layers.public List<String> getUnconnectedOutLayersNames()
public MatOfInt getUnconnectedOutLayers()
public void connect(String outPin, String inpPin)
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()public void dumpToFile(String path)
path
- path to output file with .dot extension
SEE: dump()public void enableFusion(boolean fusion)
fusion
- true to enable the fusion, false to disable. The fusion is enabled by default.public void forward(List<Mat> outputBlobs, String outputName)
outputName
.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.public void forward(List<Mat> outputBlobs)
outputName
.outputBlobs
- contains all output blobs for specified layer.
If outputName
is empty, runs forward pass for the whole network.public void forward(List<Mat> outputBlobs, List<String> outBlobNames)
outBlobNames
.outputBlobs
- contains blobs for first outputs of specified layers.outBlobNames
- names for layers which outputs are needed to getpublic void getLayerTypes(List<String> layersTypes)
layersTypes
- output parameter for returning types.public void getMemoryConsumption(MatOfInt netInputShape, long[] weights, long[] blobs)
public void getMemoryConsumption(int layerId, MatOfInt netInputShape, long[] weights, long[] blobs)
public void getMemoryConsumption(int layerId, List<MatOfInt> netInputShapes, long[] weights, long[] blobs)
public void setHalideScheduler(String scheduler)
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.public void setInput(Mat blob, String name, double scalefactor, Scalar mean)
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:
\(input(n,c,h,w) = scalefactor \times (blob(n,c,h,w) - mean_c)\)public void setInput(Mat blob, String name, double scalefactor)
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:
\(input(n,c,h,w) = scalefactor \times (blob(n,c,h,w) - mean_c)\)public void setInput(Mat blob, String name)
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:
\(input(n,c,h,w) = scalefactor \times (blob(n,c,h,w) - mean_c)\)public void setInput(Mat blob)
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:
\(input(n,c,h,w) = scalefactor \times (blob(n,c,h,w) - mean_c)\)public void setInputShape(String inputName, MatOfInt shape)
inputName
- automatically generatedshape
- automatically generatedpublic void setInputsNames(List<String> inputBlobNames)
inputBlobNames
- automatically generatedpublic void setParam(DictValue layer, int numParam, Mat blob)
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.public void setPreferableBackend(int backendId)
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.public void setPreferableTarget(int targetId)
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 | | | | + |Copyright © 2020. All rights reserved.