AbstractInferenceModel

Instance Constructors

new AbstractInferenceModel(autoScalingEnabled: Boolean, concurrentNum: Int)
new AbstractInferenceModel(concurrentNum: Int)
new AbstractInferenceModel()

Value Members

final def !=(arg0: Any): Boolean

Definition Classes
AnyRef → Any
final def ##(): Int

Definition Classes
AnyRef → Any
final def ==(arg0: Any): Boolean

Definition Classes
AnyRef → Any
final def asInstanceOf[T0]: T0

Definition Classes
Any
def clearWeightBias(model: Module[Float]): Unit

Definition Classes
InferenceSupportive
def clone(): AnyRef

Attributes
protected[java.lang]
Definition Classes
AnyRef
Annotations
@throws( ... )
def doLoad(modelPath: String, weightPath: String = null): Unit

loads a bigdl, analytics-zoo model
loads a bigdl, analytics-zoo model
modelPath
the file path of the model
weightPath
the file path of the weights

Definition Classes
InferenceModel
def doLoadCaffe(modelPath: String, weightPath: String): Unit

loads a caffe model
loads a caffe model
modelPath
the path of the prototxt file
weightPath
the path of the caffemodel file

Definition Classes
InferenceModel
def doLoadOpenVINO(modelPath: String, weightPath: String): Unit

loads a openvino IR
loads a openvino IR
modelPath
the path of openvino ir xml file
weightPath
the path of openvino ir bin file

Definition Classes
InferenceModel
def doLoadOpenVINOInt8(modelPath: String, weightPath: String, batchSize: Int): Unit

loads a openvino IR Int8
loads a openvino IR Int8
modelPath
the path of openvino ir xml file
weightPath
the path of openvino ir bin file

Definition Classes
InferenceModel
def doLoadTF(modelPath: String, imageClassificationModelType: String, checkpointPath: String, inputShape: Array[Int], ifReverseInputChannels: Boolean, meanValues: Array[Float], scale: Float): Unit

load TF model as OpenVINO IR
load TF model as OpenVINO IR
modelPath
the path of the tensorflow model
imageClassificationModelType
the type of the tensorflow model
checkpointPath
the path of the tensorflow checkpoint file
inputShape
input shape that should be fed to an input node(s) of the model
ifReverseInputChannels
the boolean value of if need reverse input channels. switch the input channels order from RGB to BGR (or vice versa).
meanValues
all input values coming from original network inputs will be divided by this value.
scale
the scale value, to be used for the input image per channel.

Definition Classes
InferenceModel
def doLoadTF(modelPath: String, objectDetectionModelType: String, pipelineConfigPath: String, extensionsConfigPath: String): Unit

loads a TF model as OpenVINO
loads a TF model as OpenVINO
modelPath
the path of the tensorflow model
objectDetectionModelType
the type of the tensorflow model, please refer to ModelType e.g. faster_rcnn_resnet101_coco, mask_rcnn_inception_v2_coco, rfcn_resnet101_coco, ssd_inception_v2_coco
pipelineConfigPath
the path of the pipeline configure file
extensionsConfigPath
the path of the extensions configure file

Definition Classes
InferenceModel
def doLoadTF(modelPath: String, pipelineConfigPath: String, extensionsConfigPath: String): Unit

loads a TF model as OpenVINO
loads a TF model as OpenVINO
modelPath
the path of the tensorflow model
pipelineConfigPath
the path of the pipeline configure file
extensionsConfigPath
the path of the extensions configure file

Definition Classes
InferenceModel
def doLoadTF(modelPath: String, objectDetectionModelType: String): Unit

loads a TF model as OpenVINO
loads a TF model as OpenVINO
modelPath
the path of the tensorflow model
objectDetectionModelType
the type of the tensorflow model, please refer to ModelType e.g. faster_rcnn_resnet101_coco, mask_rcnn_inception_v2_coco, rfcn_resnet101_coco, ssd_inception_v2_coco

Definition Classes
InferenceModel
def doLoadTF(modelPath: String, intraOpParallelismThreads: Int, interOpParallelismThreads: Int, usePerSessionThreads: Boolean): Unit

loads a TF model as TFNet
loads a TF model as TFNet
modelPath
the path of the tensorflow model
intraOpParallelismThreads
the num of intraOpParallelismThreads
interOpParallelismThreads
the num of interOpParallelismThreads
usePerSessionThreads
whether to perSessionThreads

Definition Classes
InferenceModel
def doLoadTF(modelPath: String): Unit

loads a TF model as TFNet
loads a TF model as TFNet
modelPath
the path of the tensorflow model file

Definition Classes
InferenceModel
def doLoadTFAsCalibratedOpenVINO(modelPath: String, modelType: String, checkpointPath: String, inputShape: Array[Int], ifReverseInputChannels: Boolean, meanValues: Array[Float], scale: Float, networkType: String, validationFilePath: String, subset: Int, opencvLibPath: String): Unit

load TF model as Calibrated OpenVINO IR
load TF model as Calibrated OpenVINO IR
modelPath
the path of the tensorflow model
modelType
the type of the tensorflow model
checkpointPath
the path of the tensorflow checkpoint file
inputShape
input shape that should be fed to an input node(s) of the model
ifReverseInputChannels
the boolean value of if need reverse input channels. switch the input channels order from RGB to BGR (or vice versa).
meanValues
all input values coming from original network inputs will be divided by this value.
scale
the scale value, to be used for the input image per channel.
networkType
Type of an inferred network, "C" to calibrate Classification, "OD" to calibrate Object Detection, "RawC" to collect only statistics for Classification, "RawOD" to collect only statistics for Object Detection
validationFilePath
Path to a file with validation images
subset
Number of pictures from the whole validation set to create the calibration dataset.
opencvLibPath
the lib path whwere libopencv_imgcodecs.so.4.0, libopencv_core.so.4.0 and libopencv_imgproc.so.4.0 can be found

Definition Classes
InferenceModel
def doPredict(inputActivity: Activity): Activity

predicts the inference result
predicts the inference result
inputActivity
the input activity
returns
the output activity

Definition Classes
InferenceModel
def doPredict(inputs: List[List[JTensor]]): List[List[JTensor]]

predicts the inference result
predicts the inference result
inputs
the input tensor with batch
returns
the output tensor with batch

Definition Classes
InferenceModel
def doPredictInt8(inputActivity: Activity): Activity

predicts int8 the inference result
predicts int8 the inference result
inputActivity
the input activity
returns
the output activity

Definition Classes
InferenceModel
def doPredictInt8(inputs: List[List[JTensor]]): List[List[JTensor]]

predicts int8 the inference result
predicts int8 the inference result
inputs
the input tensor with batch
returns
the output tensor with batch

Definition Classes
InferenceModel
def doReload(modelPath: String, weightPath: String): Unit

reloads the bigdl, analytics-zoo model
reloads the bigdl, analytics-zoo model
modelPath
the file path of the model
weightPath
the file path of the weights

Definition Classes
InferenceModel
final def eq(arg0: AnyRef): Boolean

Definition Classes
AnyRef
def equals(arg0: Any): Boolean

Definition Classes
AnyRef → Any
def finalize(): Unit

Attributes
protected[java.lang]
Definition Classes
AnyRef
Annotations
@throws( classOf[java.lang.Throwable] )
final def getClass(): Class[_]

Definition Classes
AnyRef → Any
def getOriginalModel: AbstractModel

Definition Classes
InferenceModel
def hashCode(): Int

Definition Classes
AnyRef → Any
final def isInstanceOf[T0]: Boolean

Definition Classes
Any
def load(modelPath: String, weightPath: String): Unit
def load(modelPath: String): Unit
def loadCaffe(modelPath: String, weightPath: String): Unit
def loadCaffe(modelPath: String): Unit
def loadOpenVINO(modelFilePath: String, weightFilePath: String): Unit
def loadOpenVINOInt8(modelFilePath: String, weightFilePath: String, batchSize: Int): Unit
def loadTF(modelPath: String, imageClassificationModelType: String, checkpointPath: String, inputShape: Array[Int], ifReverseInputChannels: Boolean, meanValues: Array[Float], scale: Float): Unit
def loadTF(modelPath: String, objectDetectionModelType: String, pipelineConfigFilePath: String, extensionsConfigFilePath: String): Unit
def loadTF(modelPath: String, pipelineConfigFilePath: String, extensionsConfigFilePath: String): Unit
def loadTF(modelPath: String, objectDetectionModelType: String): Unit
def loadTF(modelPath: String, intraOpParallelismThreads: Int, interOpParallelismThreads: Int, usePerSessionThreads: Boolean): Unit
def loadTF(modelPath: String): Unit
def loadTFAsCalibratedOpenVINO(modelPath: String, modelType: String, checkpointPath: String, inputShape: Array[Int], ifReverseInputChannels: Boolean, meanValues: Array[Float], scale: Float, networkType: String, validationFilePath: String, subset: Int, opencvLibPath: String): Unit
def makeMetaModel(original: AbstractModule[Activity, Activity, Float]): AbstractModule[Activity, Activity, Float]

Definition Classes
InferenceSupportive
def makeUpModel(clonedModel: Module[Float], weightBias: Array[Tensor[Float]]): AbstractModule[Activity, Activity, Float]

Definition Classes
InferenceSupportive
final def ne(arg0: AnyRef): Boolean

Definition Classes
AnyRef
final def notify(): Unit

Definition Classes
AnyRef
final def notifyAll(): Unit

Definition Classes
AnyRef
def predict(inputs: Array[List[JTensor]]): List[List[JTensor]]
def predict(inputs: List[List[JTensor]]): List[List[JTensor]]
def predict(input: List[Float], shape: <repeated...>[Int]): List[Float]
def predictInt8(inputs: Array[List[JTensor]]): List[List[JTensor]]
def predictInt8(inputs: List[List[JTensor]]): List[List[JTensor]]
def release(): Unit

release original model and all the cloned ones in the queue
release original model and all the cloned ones in the queue

Definition Classes
AbstractInferenceModel → InferenceModel
def releaseWeightBias(model: Module[Float]): Unit

Definition Classes
InferenceSupportive
def reload(modelPath: String, weightPath: String): Unit
def reload(modelPath: String): Unit
final def synchronized[T0](arg0: ⇒ T0): T0

Definition Classes
AnyRef
def timing[T](name: String)(f: ⇒ T): T

Definition Classes
InferenceSupportive
def toString(): String

Definition Classes
AbstractInferenceModel → InferenceModel → AnyRef → Any
def transferBatchTableToJListOfJListOfJTensor(batchTable: Table, batchSize: Int): List[List[JTensor]]

Definition Classes
InferenceSupportive
def transferBatchTensorToJListOfJListOfJTensor(batchTensor: Tensor[Float], batchSize: Int): List[List[JTensor]]

Definition Classes
InferenceSupportive
def transferListOfActivityToActivityOfBatch(inputs: List[List[JTensor]], batchSize: Int): Activity

Definition Classes
InferenceSupportive
def transferTensorToJTensor(input: Tensor[Float]): JTensor

Definition Classes
InferenceSupportive
def transferTensorsToTensorOfBatch(tensors: Array[JTensor]): Tensor[Float]

Definition Classes
InferenceSupportive
final def wait(): Unit

Definition Classes
AnyRef
Annotations
@throws( ... )
final def wait(arg0: Long, arg1: Int): Unit

Definition Classes
AnyRef
Annotations
@throws( ... )
final def wait(arg0: Long): Unit

Definition Classes
AnyRef
Annotations
@throws( ... )

Deprecated Value Members

def doPredict(input: List[Float], shape: List[Integer]): List[Float]

Definition Classes
InferenceModel
Annotations
@deprecated
Deprecated

Related Doc: package inference

abstract class AbstractInferenceModel extends InferenceModel with Serializable

Instance Constructors

new AbstractInferenceModel(autoScalingEnabled: Boolean, concurrentNum: Int)

new AbstractInferenceModel(concurrentNum: Int)

new AbstractInferenceModel()

Value Members

final def !=(arg0: Any): Boolean

final def ##(): Int

final def ==(arg0: Any): Boolean

final def asInstanceOf[T0]: T0

def clearWeightBias(model: Module[Float]): Unit

def clone(): AnyRef

def doLoad(modelPath: String, weightPath: String = null): Unit

def doLoadCaffe(modelPath: String, weightPath: String): Unit

def doLoadOpenVINO(modelPath: String, weightPath: String): Unit

def doLoadOpenVINOInt8(modelPath: String, weightPath: String, batchSize: Int): Unit

def doLoadTF(modelPath: String, imageClassificationModelType: String, checkpointPath: String, inputShape: Array[Int], ifReverseInputChannels: Boolean, meanValues: Array[Float], scale: Float): Unit

def doLoadTF(modelPath: String, objectDetectionModelType: String, pipelineConfigPath: String, extensionsConfigPath: String): Unit

def doLoadTF(modelPath: String, pipelineConfigPath: String, extensionsConfigPath: String): Unit

def doLoadTF(modelPath: String, objectDetectionModelType: String): Unit

def doLoadTF(modelPath: String, intraOpParallelismThreads: Int, interOpParallelismThreads: Int, usePerSessionThreads: Boolean): Unit

def doLoadTF(modelPath: String): Unit

def doLoadTFAsCalibratedOpenVINO(modelPath: String, modelType: String, checkpointPath: String, inputShape: Array[Int], ifReverseInputChannels: Boolean, meanValues: Array[Float], scale: Float, networkType: String, validationFilePath: String, subset: Int, opencvLibPath: String): Unit

def doPredict(inputActivity: Activity): Activity

def doPredict(inputs: List[List[JTensor]]): List[List[JTensor]]

def doPredictInt8(inputActivity: Activity): Activity

def doPredictInt8(inputs: List[List[JTensor]]): List[List[JTensor]]

def doReload(modelPath: String, weightPath: String): Unit

final def eq(arg0: AnyRef): Boolean

def equals(arg0: Any): Boolean

def finalize(): Unit

final def getClass(): Class[_]

def getOriginalModel: AbstractModel

def hashCode(): Int

final def isInstanceOf[T0]: Boolean

def load(modelPath: String, weightPath: String): Unit

def load(modelPath: String): Unit

def loadCaffe(modelPath: String, weightPath: String): Unit

def loadCaffe(modelPath: String): Unit

def loadOpenVINO(modelFilePath: String, weightFilePath: String): Unit

def loadOpenVINOInt8(modelFilePath: String, weightFilePath: String, batchSize: Int): Unit

def loadTF(modelPath: String, imageClassificationModelType: String, checkpointPath: String, inputShape: Array[Int], ifReverseInputChannels: Boolean, meanValues: Array[Float], scale: Float): Unit

def loadTF(modelPath: String, objectDetectionModelType: String, pipelineConfigFilePath: String, extensionsConfigFilePath: String): Unit

def loadTF(modelPath: String, pipelineConfigFilePath: String, extensionsConfigFilePath: String): Unit

def loadTF(modelPath: String, objectDetectionModelType: String): Unit

def loadTF(modelPath: String, intraOpParallelismThreads: Int, interOpParallelismThreads: Int, usePerSessionThreads: Boolean): Unit

def loadTF(modelPath: String): Unit

def loadTFAsCalibratedOpenVINO(modelPath: String, modelType: String, checkpointPath: String, inputShape: Array[Int], ifReverseInputChannels: Boolean, meanValues: Array[Float], scale: Float, networkType: String, validationFilePath: String, subset: Int, opencvLibPath: String): Unit

def makeMetaModel(original: AbstractModule[Activity, Activity, Float]): AbstractModule[Activity, Activity, Float]

def makeUpModel(clonedModel: Module[Float], weightBias: Array[Tensor[Float]]): AbstractModule[Activity, Activity, Float]

final def ne(arg0: AnyRef): Boolean

final def notify(): Unit

final def notifyAll(): Unit

def predict(inputs: Array[List[JTensor]]): List[List[JTensor]]

def predict(inputs: List[List[JTensor]]): List[List[JTensor]]

def predict(input: List[Float], shape: <repeated...>[Int]): List[Float]

def predictInt8(inputs: Array[List[JTensor]]): List[List[JTensor]]

def predictInt8(inputs: List[List[JTensor]]): List[List[JTensor]]

def release(): Unit

def releaseWeightBias(model: Module[Float]): Unit

def reload(modelPath: String, weightPath: String): Unit

def reload(modelPath: String): Unit

final def synchronized[T0](arg0: ⇒ T0): T0

def timing[T](name: String)(f: ⇒ T): T

def toString(): String

def transferBatchTableToJListOfJListOfJTensor(batchTable: Table, batchSize: Int): List[List[JTensor]]

def transferBatchTensorToJListOfJListOfJTensor(batchTensor: Tensor[Float], batchSize: Int): List[List[JTensor]]

def transferListOfActivityToActivityOfBatch(inputs: List[List[JTensor]], batchSize: Int): Activity

def transferTensorToJTensor(input: Tensor[Float]): JTensor

def transferTensorsToTensorOfBatch(tensors: Array[JTensor]): Tensor[Float]

final def wait(): Unit

final def wait(arg0: Long, arg1: Int): Unit

final def wait(arg0: Long): Unit

Deprecated Value Members

def doPredict(input: List[Float], shape: List[Integer]): List[Float]

Inherited from InferenceModel

Inherited from Serializable

Inherited from Serializable