object MlUtils
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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
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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
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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
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- val imageSize: Int
- def imagesLoad(paths: Array[LocalLabeledImagePath], scaleTo: Int): Array[ByteImage]
- def imagesLoadSeq(url: String, sc: SparkContext, classNum: Int): RDD[ByteImage]
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def
isInstanceOf[T0]: Boolean
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- def loadModel[T](param: PredictParams)(implicit arg0: ClassTag[T], ev: TensorNumeric[T]): Module[T]
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- val predictParser: OptionParser[PredictParams]
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synchronized[T0](arg0: ⇒ T0): T0
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- val testMean: (Double, Double, Double)
- val testStd: (Double, Double, Double)
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toString(): String
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- def transformDF(data: DataFrame, f: Transformer[Row, DenseVector]): DataFrame
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wait(arg0: Long, arg1: Int): Unit
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wait(arg0: Long): Unit
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- object BigDlModel extends ModelType with Product with Serializable
- object TorchModel extends ModelType with Product with Serializable