MlUtils

Type Members

case class ByteImage(data: Array[Byte], imageName: String) extends Product with Serializable

ByteImage is case class, which represents an object of image in byte format.
ByteImage is case class, which represents an object of image in byte format.
data
image byte data
imageName
image name
case class DfPoint(features: DenseVector, imageName: String) extends Product with Serializable

It is used to store single data frame information
It is used to store single data frame information
features
extracted features after the transformers
imageName
image name
sealed trait ModelType extends AnyRef

This is a trait meaning the model type.
This is a trait meaning the model type. There are two sorts of model type, which are torch model TorchModel and BigDL model BigDlModel.
case class PredictParams(folder: String = "./", batchSize: Int = 32, classNum: Int = 1000, isHdfs: Boolean = false, modelType: ModelType = BigDlModel, modelPath: String = "", showNum: Int = 100) extends Product with Serializable

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
object BigDlModel extends ModelType with Product with Serializable
object TorchModel extends ModelType with Product with Serializable
final def asInstanceOf[T0]: T0

Definition Classes
Any
def clone(): AnyRef

Attributes
protected[java.lang]
Definition Classes
AnyRef
Annotations
@throws( ... )
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 hashCode(): Int

Definition Classes
AnyRef → Any
val imageSize: Int
def imagesLoad(paths: Array[LocalLabeledImagePath], scaleTo: Int): Array[ByteImage]
def imagesLoadSeq(url: String, sc: SparkContext, classNum: Int): RDD[ByteImage]
final def isInstanceOf[T0]: Boolean

Definition Classes
Any
def loadModel[T](param: PredictParams)(implicit arg0: ClassTag[T], ev: TensorNumeric[T]): Module[T]
final def ne(arg0: AnyRef): Boolean

Definition Classes
AnyRef
final def notify(): Unit

Definition Classes
AnyRef
final def notifyAll(): Unit

Definition Classes
AnyRef
val predictParser: OptionParser[PredictParams]
final def synchronized[T0](arg0: ⇒ T0): T0

Definition Classes
AnyRef
val testMean: (Double, Double, Double)
val testStd: (Double, Double, Double)
def toString(): String

Definition Classes
AnyRef → Any
def transformDF(data: DataFrame, f: Transformer[Row, DenseVector]): DataFrame
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( ... )

Related Doc: package imageclassification

object MlUtils

Type Members

case class ByteImage(data: Array[Byte], imageName: String) extends Product with Serializable

case class DfPoint(features: DenseVector, imageName: String) extends Product with Serializable

sealed trait ModelType extends AnyRef

case class PredictParams(folder: String = "./", batchSize: Int = 32, classNum: Int = 1000, isHdfs: Boolean = false, modelType: ModelType = BigDlModel, modelPath: String = "", showNum: Int = 100) extends Product with Serializable

Value Members

final def !=(arg0: Any): Boolean

final def ##(): Int

final def ==(arg0: Any): Boolean

object BigDlModel extends ModelType with Product with Serializable

object TorchModel extends ModelType with Product with Serializable

final def asInstanceOf[T0]: T0

def clone(): AnyRef

final def eq(arg0: AnyRef): Boolean

def equals(arg0: Any): Boolean

def finalize(): Unit

final def getClass(): Class[_]

def hashCode(): Int

val imageSize: Int

def imagesLoad(paths: Array[LocalLabeledImagePath], scaleTo: Int): Array[ByteImage]

def imagesLoadSeq(url: String, sc: SparkContext, classNum: Int): RDD[ByteImage]

final def isInstanceOf[T0]: Boolean

def loadModel[T](param: PredictParams)(implicit arg0: ClassTag[T], ev: TensorNumeric[T]): Module[T]

final def ne(arg0: AnyRef): Boolean

final def notify(): Unit

final def notifyAll(): Unit

val predictParser: OptionParser[PredictParams]

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

val testMean: (Double, Double, Double)

val testStd: (Double, Double, Double)

def toString(): String

def transformDF(data: DataFrame, f: Transformer[Row, DenseVector]): DataFrame

final def wait(): Unit

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

final def wait(arg0: Long): Unit

Inherited from AnyRef

Inherited from Any

Ungrouped