com.intel.analytics.bigdl.transform.vision.image

MTImageFeatureToBatch

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

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
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Inherited
  1. MTImageFeatureToBatch
  2. Transformer
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Abstract Value Members

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

    Attributes
    protected

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

    Attributes
    protected

Concrete Value Members

  1. final def !=(arg0: AnyRef): Boolean

    Definition Classes
    AnyRef

  2. final def !=(arg0: Any): Boolean

    Definition Classes
    Any

  3. final def ##(): Int

    Definition Classes
    AnyRef → Any

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

    Definition Classes
    Transformer

  5. final def ==(arg0: AnyRef): Boolean

    Definition Classes
    AnyRef

  6. final def ==(arg0: Any): Boolean

    Definition Classes
    Any

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

    Definition Classes
    MTImageFeatureToBatch → Transformer

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

    Apply this transformer to rdd

    Apply this transformer to rdd

    dataset

    Definition Classes
    Transformer

  9. final def asInstanceOf[T0]: T0

    Definition Classes
    Any

  10. val batchSize: Int

    Attributes
    protected

  11. def clone(): AnyRef

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

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

    Definition Classes
    Transformer

  13. final def eq(arg0: AnyRef): Boolean

    Definition Classes
    AnyRef

  14. def equals(arg0: Any): Boolean

    Definition Classes
    AnyRef → Any

  15. def finalize(): Unit

    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )

  16. final def getClass(): Class[_]

    Definition Classes
    AnyRef → Any

  17. def hashCode(): Int

    Definition Classes
    AnyRef → Any

  18. final def isInstanceOf[T0]: Boolean

    Definition Classes
    Any

  19. final def ne(arg0: AnyRef): Boolean

    Definition Classes
    AnyRef

  20. final def notify(): Unit

    Definition Classes
    AnyRef

  21. final def notifyAll(): Unit

    Definition Classes
    AnyRef

  22. val parallelism: Int

    Attributes
    protected

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

    Definition Classes
    AnyRef

  24. def toString(): String

    Definition Classes
    AnyRef → Any

  25. final def wait(): Unit

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

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

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

  27. final def wait(arg0: Long): Unit

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

Inherited from Transformer[ImageFeature, MiniBatch[Float]]

Inherited from Serializable

Inherited from Serializable

Inherited from AnyRef

Inherited from Any

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