MTImageFeatureToBatch

An abstract class to convert ImageFeature iterator to MiniBatches. This transformer will be run on each image feature. "processImageFeature" will be called to buffer the image features. When there are enough buffered image features to form a batch, "createBatch" will be called. You should override processImageFeature to buffer each image feature, and createBatch to convert the buffered data into a mini-batch

Linear Supertypes

Transformer[ImageFeature, MiniBatch[Float]], Serializable, Serializable, AnyRef, Any

Known Subclasses

ClassificationMTImageFeatureToBatch, RoiImageFeatureToBatch, RoiImageFeatureToBatchWithResize

Abstract Value Members

abstract def createBatch(batchSize: Int): MiniBatch[Float]

Attributes
protected
abstract def processImageFeature(img: ImageFeature, position: Int): Unit

Attributes
protected

Concrete Value Members

final def !=(arg0: Any): Boolean

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

Definition Classes
AnyRef → Any
def ->[C](other: Transformer[MiniBatch[Float], C]): Transformer[ImageFeature, C]

Definition Classes
Transformer
final def ==(arg0: Any): Boolean

Definition Classes
AnyRef → Any
def apply(prev: Iterator[ImageFeature]): Iterator[MiniBatch[Float]]

Definition Classes
MTImageFeatureToBatch → Transformer
def apply(dataset: RDD[ImageFeature])(implicit evidence: ClassTag[MiniBatch[Float]]): RDD[MiniBatch[Float]]

Apply this transformer to rdd
Apply this transformer to rdd

Definition Classes
Transformer
final def asInstanceOf[T0]: T0

Definition Classes
Any
val batchSize: Int

Attributes
protected
def clone(): AnyRef

Attributes
protected[java.lang]
Definition Classes
AnyRef
Annotations
@throws( ... )
def cloneTransformer(): Transformer[ImageFeature, MiniBatch[Float]]

Definition Classes
Transformer
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
final def isInstanceOf[T0]: Boolean

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

Definition Classes
AnyRef
final def notify(): Unit

Definition Classes
AnyRef
final def notifyAll(): Unit

Definition Classes
AnyRef
val parallelism: Int

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

Definition Classes
AnyRef
def toString(): String

Definition Classes
AnyRef → Any
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 Docs: object MTImageFeatureToBatch | package image

abstract class MTImageFeatureToBatch extends Transformer[ImageFeature, MiniBatch[Float]]

Abstract Value Members

abstract def createBatch(batchSize: Int): MiniBatch[Float]

abstract def processImageFeature(img: ImageFeature, position: Int): Unit

Concrete Value Members

final def !=(arg0: Any): Boolean

final def ##(): Int

def ->[C](other: Transformer[MiniBatch[Float], C]): Transformer[ImageFeature, C]

final def ==(arg0: Any): Boolean

def apply(prev: Iterator[ImageFeature]): Iterator[MiniBatch[Float]]

def apply(dataset: RDD[ImageFeature])(implicit evidence: ClassTag[MiniBatch[Float]]): RDD[MiniBatch[Float]]

final def asInstanceOf[T0]: T0

val batchSize: Int

def clone(): AnyRef

def cloneTransformer(): Transformer[ImageFeature, MiniBatch[Float]]

final def eq(arg0: AnyRef): Boolean

def equals(arg0: Any): Boolean

def finalize(): Unit

final def getClass(): Class[_]

def hashCode(): Int

final def isInstanceOf[T0]: Boolean

final def ne(arg0: AnyRef): Boolean

final def notify(): Unit

final def notifyAll(): Unit

val parallelism: Int

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

def toString(): String

final def wait(): Unit

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

final def wait(arg0: Long): Unit

Inherited from Transformer[ImageFeature, MiniBatch[Float]]

Inherited from Serializable

Inherited from Serializable

Inherited from AnyRef

Inherited from Any

Ungrouped