class VisionEncoderDecoderForImageCaptioning extends AnnotatorModel[VisionEncoderDecoderForImageCaptioning] with HasBatchedAnnotateImage[VisionEncoderDecoderForImageCaptioning] with HasImageFeatureProperties with WriteTensorflowModel with HasEngine with HasRescaleFactor with HasGeneratorProperties

VisionEncoderDecoder model that converts images into text captions. It allows for the use of pretrained vision auto-encoding models, such as ViT, BEiT, or DeiT as the encoder, in combination with pretrained language models, like RoBERTa, GPT2, or BERT as the decoder.

Pretrained models can be loaded with pretrained of the companion object:

val imageClassifier = VisionEncoderDecoderForImageCaptioning.pretrained()
  .setInputCols("image_assembler")
  .setOutputCol("caption")

The default model is "image_captioning_vit_gpt2", if no name is provided.

For available pretrained models please see the Models Hub.

Models from the HuggingFace 🤗 Transformers library are also compatible with Spark NLP 🚀. To see which models are compatible and how to import them see https://github.com/JohnSnowLabs/spark-nlp/discussions/5669 and to see more extended examples, see VisionEncoderDecoderTestSpec.

Note:

This is a very computationally expensive module especially on larger batch sizes. The use of an accelerator such as GPU is recommended.

Example

import com.johnsnowlabs.nlp.annotator._
import com.johnsnowlabs.nlp.ImageAssembler
import org.apache.spark.ml.Pipeline

val imageDF: DataFrame = spark.read
  .format("image")
  .option("dropInvalid", value = true)
  .load("src/test/resources/image/")

val imageAssembler = new ImageAssembler()
  .setInputCol("image")
  .setOutputCol("image_assembler")

val imageCaptioning = VisionEncoderDecoderForImageCaptioning
  .pretrained()
  .setBeamSize(2)
  .setDoSample(false)
  .setInputCols("image_assembler")
  .setOutputCol("caption")

val pipeline = new Pipeline().setStages(Array(imageAssembler, imageCaptioning))
val pipelineDF = pipeline.fit(imageDF).transform(imageDF)

pipelineDF
  .selectExpr("reverse(split(image.origin, '/'))[0] as image_name", "caption.result")
  .show(truncate = false)

+-----------------+---------------------------------------------------------+
|image_name       |result                                                   |
+-----------------+---------------------------------------------------------+
|palace.JPEG      |[a large room filled with furniture and a large window]  |
|egyptian_cat.jpeg|[a cat laying on a couch next to another cat]            |
|hippopotamus.JPEG|[a brown bear in a body of water]                        |
|hen.JPEG         |[a flock of chickens standing next to each other]        |
|ostrich.JPEG     |[a large bird standing on top of a lush green field]     |
|junco.JPEG       |[a small bird standing on a wet ground]                  |
|bluetick.jpg     |[a small dog standing on a wooden floor]                 |
|chihuahua.jpg    |[a small brown dog wearing a blue sweater]               |
|tractor.JPEG     |[a man is standing in a field with a tractor]            |
|ox.JPEG          |[a large brown cow standing on top of a lush green field]|
+-----------------+---------------------------------------------------------+
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Inherited
  1. VisionEncoderDecoderForImageCaptioning
  2. HasGeneratorProperties
  3. HasRescaleFactor
  4. HasEngine
  5. WriteTensorflowModel
  6. HasImageFeatureProperties
  7. HasBatchedAnnotateImage
  8. AnnotatorModel
  9. CanBeLazy
  10. RawAnnotator
  11. HasOutputAnnotationCol
  12. HasInputAnnotationCols
  13. HasOutputAnnotatorType
  14. ParamsAndFeaturesWritable
  15. HasFeatures
  16. DefaultParamsWritable
  17. MLWritable
  18. Model
  19. Transformer
  20. PipelineStage
  21. Logging
  22. Params
  23. Serializable
  24. Serializable
  25. Identifiable
  26. AnyRef
  27. Any
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Visibility
  1. Public
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Instance Constructors

  1. new VisionEncoderDecoderForImageCaptioning()

    Annotator reference id.

    Annotator reference id. Used to identify elements in metadata or to refer to this annotator type

  2. new VisionEncoderDecoderForImageCaptioning(uid: String)

    uid

    required uid for storing annotator to disk

Type Members

  1. type AnnotationContent = Seq[Row]

    internal types to show Rows as a relevant StructType Should be deleted once Spark releases UserDefinedTypes to @developerAPI

    internal types to show Rows as a relevant StructType Should be deleted once Spark releases UserDefinedTypes to @developerAPI

    Attributes
    protected
    Definition Classes
    AnnotatorModel
  2. type AnnotatorType = String
    Definition Classes
    HasOutputAnnotatorType

Value Members

  1. final def !=(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  2. final def ##(): Int
    Definition Classes
    AnyRef → Any
  3. final def $[T](param: Param[T]): T
    Attributes
    protected
    Definition Classes
    Params
  4. def $$[T](feature: StructFeature[T]): T
    Attributes
    protected
    Definition Classes
    HasFeatures
  5. def $$[K, V](feature: MapFeature[K, V]): Map[K, V]
    Attributes
    protected
    Definition Classes
    HasFeatures
  6. def $$[T](feature: SetFeature[T]): Set[T]
    Attributes
    protected
    Definition Classes
    HasFeatures
  7. def $$[T](feature: ArrayFeature[T]): Array[T]
    Attributes
    protected
    Definition Classes
    HasFeatures
  8. final def ==(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  9. def _transform(dataset: Dataset[_], recursivePipeline: Option[PipelineModel]): DataFrame
    Attributes
    protected
    Definition Classes
    AnnotatorModel
  10. def afterAnnotate(dataset: DataFrame): DataFrame
    Attributes
    protected
    Definition Classes
    AnnotatorModel
  11. final def asInstanceOf[T0]: T0
    Definition Classes
    Any
  12. def batchAnnotate(batchedAnnotations: Seq[Array[AnnotationImage]]): Seq[Seq[Annotation]]

    Takes a document and annotations and produces new annotations of this annotator's annotation type

    Takes a document and annotations and produces new annotations of this annotator's annotation type

    batchedAnnotations

    Annotations that correspond to inputAnnotationCols generated by previous annotators if any

    returns

    any number of annotations processed for every input annotation. Not necessary one to one relationship

    Definition Classes
    VisionEncoderDecoderForImageCaptioningHasBatchedAnnotateImage
  13. def batchProcess(rows: Iterator[_]): Iterator[Row]
    Definition Classes
    HasBatchedAnnotateImage
  14. val batchSize: IntParam

    Size of every batch (Default depends on model).

    Size of every batch (Default depends on model).

    Definition Classes
    HasBatchedAnnotateImage
  15. val beamSize: IntParam

    Beam size for the beam search algorithm (Default: 4)

    Beam size for the beam search algorithm (Default: 4)

    Definition Classes
    HasGeneratorProperties
  16. def beforeAnnotate(dataset: Dataset[_]): Dataset[_]
    Attributes
    protected
    Definition Classes
    AnnotatorModel
  17. final def checkSchema(schema: StructType, inputAnnotatorType: String): Boolean
    Attributes
    protected
    Definition Classes
    HasInputAnnotationCols
  18. final def clear(param: Param[_]): VisionEncoderDecoderForImageCaptioning.this.type
    Definition Classes
    Params
  19. def clone(): AnyRef
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native()
  20. val configProtoBytes: IntArrayParam

    ConfigProto from tensorflow, serialized into byte array.

    ConfigProto from tensorflow, serialized into byte array. Get with config_proto.SerializeToString()

  21. def copy(extra: ParamMap): VisionEncoderDecoderForImageCaptioning

    requirement for annotators copies

    requirement for annotators copies

    Definition Classes
    RawAnnotator → Model → Transformer → PipelineStage → Params
  22. def copyValues[T <: Params](to: T, extra: ParamMap): T
    Attributes
    protected
    Definition Classes
    Params
  23. final def defaultCopy[T <: Params](extra: ParamMap): T
    Attributes
    protected
    Definition Classes
    Params
  24. val doNormalize: BooleanParam

    Whether or not to normalize the input with mean and standard deviation

    Whether or not to normalize the input with mean and standard deviation

    Definition Classes
    HasImageFeatureProperties
  25. val doRescale: BooleanParam

    Whether to rescale the image values by rescaleFactor.

    Whether to rescale the image values by rescaleFactor.

    Definition Classes
    HasRescaleFactor
  26. val doResize: BooleanParam

    Whether to resize the input to a certain size

    Whether to resize the input to a certain size

    Definition Classes
    HasImageFeatureProperties
  27. val doSample: BooleanParam

    Whether or not to use sampling, use greedy decoding otherwise (Default: false)

    Whether or not to use sampling, use greedy decoding otherwise (Default: false)

    Definition Classes
    HasGeneratorProperties
  28. val engine: Param[String]

    This param is set internally once via loadSavedModel.

    This param is set internally once via loadSavedModel. That's why there is no setter

    Definition Classes
    HasEngine
  29. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  30. def equals(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  31. def explainParam(param: Param[_]): String
    Definition Classes
    Params
  32. def explainParams(): String
    Definition Classes
    Params
  33. def extraValidate(structType: StructType): Boolean
    Attributes
    protected
    Definition Classes
    RawAnnotator
  34. def extraValidateMsg: String

    Override for additional custom schema checks

    Override for additional custom schema checks

    Attributes
    protected
    Definition Classes
    RawAnnotator
  35. final def extractParamMap(): ParamMap
    Definition Classes
    Params
  36. final def extractParamMap(extra: ParamMap): ParamMap
    Definition Classes
    Params
  37. val featureExtractorType: Param[String]

    Name of model's architecture for feature extraction

    Name of model's architecture for feature extraction

    Definition Classes
    HasImageFeatureProperties
  38. val features: ArrayBuffer[Feature[_, _, _]]
    Definition Classes
    HasFeatures
  39. def finalize(): Unit
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  40. val generationConfig: StructFeature[GenerationConfig]
    Attributes
    protected[nlp]
  41. def get[T](feature: StructFeature[T]): Option[T]
    Attributes
    protected
    Definition Classes
    HasFeatures
  42. def get[K, V](feature: MapFeature[K, V]): Option[Map[K, V]]
    Attributes
    protected
    Definition Classes
    HasFeatures
  43. def get[T](feature: SetFeature[T]): Option[Set[T]]
    Attributes
    protected
    Definition Classes
    HasFeatures
  44. def get[T](feature: ArrayFeature[T]): Option[Array[T]]
    Attributes
    protected
    Definition Classes
    HasFeatures
  45. final def get[T](param: Param[T]): Option[T]
    Definition Classes
    Params
  46. def getBatchSize: Int

    Size of every batch.

    Size of every batch.

    Definition Classes
    HasBatchedAnnotateImage
  47. def getBeamSize: Int

    Definition Classes
    HasGeneratorProperties
  48. final def getClass(): Class[_]
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  49. def getConfigProtoBytes: Option[Array[Byte]]

    ConfigProto from tensorflow, serialized into byte array.

    ConfigProto from tensorflow, serialized into byte array. Get with config_proto.SerializeToString()

  50. final def getDefault[T](param: Param[T]): Option[T]
    Definition Classes
    Params
  51. def getDoNormalize: Boolean

    Definition Classes
    HasImageFeatureProperties
  52. def getDoRescale: Boolean

    Definition Classes
    HasRescaleFactor
  53. def getDoResize: Boolean

    Definition Classes
    HasImageFeatureProperties
  54. def getDoSample: Boolean

    Definition Classes
    HasGeneratorProperties
  55. def getEngine: String

    Definition Classes
    HasEngine
  56. def getFeatureExtractorType: String

    Definition Classes
    HasImageFeatureProperties
  57. def getGenerationConfig: GenerationConfig
    Attributes
    protected[nlp]
  58. def getImageMean: Array[Double]

    Definition Classes
    HasImageFeatureProperties
  59. def getImageStd: Array[Double]

    Definition Classes
    HasImageFeatureProperties
  60. def getInputCols: Array[String]

    returns

    input annotations columns currently used

    Definition Classes
    HasInputAnnotationCols
  61. def getLazyAnnotator: Boolean
    Definition Classes
    CanBeLazy
  62. def getMaxOutputLength: Int

    Definition Classes
    HasGeneratorProperties
  63. def getMerges: Map[(String, String), Int]

    Attributes
    protected[nlp]
  64. def getMinOutputLength: Int

    Definition Classes
    HasGeneratorProperties
  65. def getModelIfNotSet: VisionEncoderDecoder

  66. def getNReturnSequences: Int

    Definition Classes
    HasGeneratorProperties
  67. def getNoRepeatNgramSize: Int

    Definition Classes
    HasGeneratorProperties
  68. final def getOrDefault[T](param: Param[T]): T
    Definition Classes
    Params
  69. final def getOutputCol: String

    Gets annotation column name going to generate

    Gets annotation column name going to generate

    Definition Classes
    HasOutputAnnotationCol
  70. def getParam(paramName: String): Param[Any]
    Definition Classes
    Params
  71. def getRandomSeed: Option[Long]

    Definition Classes
    HasGeneratorProperties
  72. def getRepetitionPenalty: Double

    Definition Classes
    HasGeneratorProperties
  73. def getResample: Int

    Definition Classes
    HasImageFeatureProperties
  74. def getRescaleFactor: Double

    Definition Classes
    HasRescaleFactor
  75. def getSignatures: Option[Map[String, String]]

  76. def getSize: Int

    Definition Classes
    HasImageFeatureProperties
  77. def getTask: Option[String]

    Definition Classes
    HasGeneratorProperties
  78. def getTemperature: Double

    Definition Classes
    HasGeneratorProperties
  79. def getTopK: Int

    Definition Classes
    HasGeneratorProperties
  80. def getTopP: Double

    Definition Classes
    HasGeneratorProperties
  81. def getVocabulary: Map[String, Int]

    Attributes
    protected[nlp]
  82. final def hasDefault[T](param: Param[T]): Boolean
    Definition Classes
    Params
  83. def hasParam(paramName: String): Boolean
    Definition Classes
    Params
  84. def hasParent: Boolean
    Definition Classes
    Model
  85. def hashCode(): Int
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  86. val imageMean: DoubleArrayParam

    The sequence of means for each channel, to be used when normalizing images

    The sequence of means for each channel, to be used when normalizing images

    Definition Classes
    HasImageFeatureProperties
  87. val imageStd: DoubleArrayParam

    The sequence of standard deviations for each channel, to be used when normalizing images

    The sequence of standard deviations for each channel, to be used when normalizing images

    Definition Classes
    HasImageFeatureProperties
  88. def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean
    Attributes
    protected
    Definition Classes
    Logging
  89. def initializeLogIfNecessary(isInterpreter: Boolean): Unit
    Attributes
    protected
    Definition Classes
    Logging
  90. val inputAnnotatorTypes: Array[AnnotatorType]

    Input annotator type : IMAGE

    Input annotator type : IMAGE

    Definition Classes
    VisionEncoderDecoderForImageCaptioningHasInputAnnotationCols
  91. final val inputCols: StringArrayParam

    columns that contain annotations necessary to run this annotator AnnotatorType is used both as input and output columns if not specified

    columns that contain annotations necessary to run this annotator AnnotatorType is used both as input and output columns if not specified

    Attributes
    protected
    Definition Classes
    HasInputAnnotationCols
  92. final def isDefined(param: Param[_]): Boolean
    Definition Classes
    Params
  93. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  94. final def isSet(param: Param[_]): Boolean
    Definition Classes
    Params
  95. def isTraceEnabled(): Boolean
    Attributes
    protected
    Definition Classes
    Logging
  96. val lazyAnnotator: BooleanParam
    Definition Classes
    CanBeLazy
  97. def log: Logger
    Attributes
    protected
    Definition Classes
    Logging
  98. def logDebug(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  99. def logDebug(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  100. def logError(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  101. def logError(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  102. def logInfo(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  103. def logInfo(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  104. def logName: String
    Attributes
    protected
    Definition Classes
    Logging
  105. def logTrace(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  106. def logTrace(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  107. def logWarning(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  108. def logWarning(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  109. val maxInputLength: IntParam

    max length of the input sequence (Default: 0)

    max length of the input sequence (Default: 0)

    Definition Classes
    HasGeneratorProperties
  110. val maxOutputLength: IntParam

    Maximum length of the sequence to be generated (Default: 20)

    Maximum length of the sequence to be generated (Default: 20)

    Definition Classes
    HasGeneratorProperties
  111. val merges: MapFeature[(String, String), Int]

    Holding merges.txt for BPE Tokenization

    Holding merges.txt for BPE Tokenization

    Attributes
    protected[nlp]
  112. val minOutputLength: IntParam

    Minimum length of the sequence to be generated (Default: 0)

    Minimum length of the sequence to be generated (Default: 0)

    Definition Classes
    HasGeneratorProperties
  113. def msgHelper(schema: StructType): String
    Attributes
    protected
    Definition Classes
    HasInputAnnotationCols
  114. val nReturnSequences: IntParam

    The number of sequences to return from the beam search.

    The number of sequences to return from the beam search.

    Definition Classes
    HasGeneratorProperties
  115. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  116. val noRepeatNgramSize: IntParam

    If set to int > 0, all ngrams of that size can only occur once (Default: 0)

    If set to int > 0, all ngrams of that size can only occur once (Default: 0)

    Definition Classes
    HasGeneratorProperties
  117. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  118. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  119. def onWrite(path: String, spark: SparkSession): Unit
  120. val optionalInputAnnotatorTypes: Array[String]
    Definition Classes
    HasInputAnnotationCols
  121. val outputAnnotatorType: AnnotatorType

    Output annotator type : CATEGORY

    Output annotator type : CATEGORY

    Definition Classes
    VisionEncoderDecoderForImageCaptioningHasOutputAnnotatorType
  122. final val outputCol: Param[String]
    Attributes
    protected
    Definition Classes
    HasOutputAnnotationCol
  123. lazy val params: Array[Param[_]]
    Definition Classes
    Params
  124. var parent: Estimator[VisionEncoderDecoderForImageCaptioning]
    Definition Classes
    Model
  125. val randomSeed: Option[Long]

    Optional Random seed for the model.

    Optional Random seed for the model. Needs to be of type Int.

    Definition Classes
    HasGeneratorProperties
  126. val repetitionPenalty: DoubleParam

    The parameter for repetition penalty (Default: 1.0).

    The parameter for repetition penalty (Default: 1.0). 1.0 means no penalty. See this paper for more details.

    Definition Classes
    HasGeneratorProperties
  127. val resample: IntParam

    An optional resampling filter.

    An optional resampling filter. This can be one of PIL.Image.NEAREST, PIL.Image.BOX, PIL.Image.BILINEAR, PIL.Image.HAMMING, PIL.Image.BICUBIC or PIL.Image.LANCZOS. Only has an effect if do_resize is set to True

    Definition Classes
    HasImageFeatureProperties
  128. val rescaleFactor: DoubleParam

    Factor to scale the image values (Default: 1 / 255.0).

    Factor to scale the image values (Default: 1 / 255.0).

    Definition Classes
    HasRescaleFactor
  129. def save(path: String): Unit
    Definition Classes
    MLWritable
    Annotations
    @Since( "1.6.0" ) @throws( ... )
  130. def set[T](feature: StructFeature[T], value: T): VisionEncoderDecoderForImageCaptioning.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  131. def set[K, V](feature: MapFeature[K, V], value: Map[K, V]): VisionEncoderDecoderForImageCaptioning.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  132. def set[T](feature: SetFeature[T], value: Set[T]): VisionEncoderDecoderForImageCaptioning.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  133. def set[T](feature: ArrayFeature[T], value: Array[T]): VisionEncoderDecoderForImageCaptioning.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  134. final def set(paramPair: ParamPair[_]): VisionEncoderDecoderForImageCaptioning.this.type
    Attributes
    protected
    Definition Classes
    Params
  135. final def set(param: String, value: Any): VisionEncoderDecoderForImageCaptioning.this.type
    Attributes
    protected
    Definition Classes
    Params
  136. final def set[T](param: Param[T], value: T): VisionEncoderDecoderForImageCaptioning.this.type
    Definition Classes
    Params
  137. def setBatchSize(size: Int): VisionEncoderDecoderForImageCaptioning.this.type

    Size of every batch.

    Size of every batch.

    Definition Classes
    HasBatchedAnnotateImage
  138. def setBeamSize(beamNum: Int): VisionEncoderDecoderForImageCaptioning.this.type

    Definition Classes
    HasGeneratorProperties
  139. def setConfigProtoBytes(bytes: Array[Int]): VisionEncoderDecoderForImageCaptioning.this.type

    ConfigProto from tensorflow, serialized into byte array.

    ConfigProto from tensorflow, serialized into byte array. Get with config_proto.SerializeToString()

  140. def setDefault[T](feature: StructFeature[T], value: () ⇒ T): VisionEncoderDecoderForImageCaptioning.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  141. def setDefault[K, V](feature: MapFeature[K, V], value: () ⇒ Map[K, V]): VisionEncoderDecoderForImageCaptioning.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  142. def setDefault[T](feature: SetFeature[T], value: () ⇒ Set[T]): VisionEncoderDecoderForImageCaptioning.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  143. def setDefault[T](feature: ArrayFeature[T], value: () ⇒ Array[T]): VisionEncoderDecoderForImageCaptioning.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  144. final def setDefault(paramPairs: ParamPair[_]*): VisionEncoderDecoderForImageCaptioning.this.type
    Attributes
    protected
    Definition Classes
    Params
  145. final def setDefault[T](param: Param[T], value: T): VisionEncoderDecoderForImageCaptioning.this.type
    Attributes
    protected[org.apache.spark.ml]
    Definition Classes
    Params
  146. def setDoNormalize(value: Boolean): VisionEncoderDecoderForImageCaptioning.this.type

    Definition Classes
    HasImageFeatureProperties
  147. def setDoRescale(value: Boolean): VisionEncoderDecoderForImageCaptioning.this.type

    Definition Classes
    HasRescaleFactor
  148. def setDoResize(value: Boolean): VisionEncoderDecoderForImageCaptioning.this.type

    Definition Classes
    HasImageFeatureProperties
  149. def setDoSample(value: Boolean): VisionEncoderDecoderForImageCaptioning.this.type

    Definition Classes
    HasGeneratorProperties
  150. def setFeatureExtractorType(value: String): VisionEncoderDecoderForImageCaptioning.this.type

    Definition Classes
    HasImageFeatureProperties
  151. def setGenerationConfig(value: GenerationConfig): VisionEncoderDecoderForImageCaptioning.this.type
    Attributes
    protected[nlp]
  152. def setImageMean(value: Array[Double]): VisionEncoderDecoderForImageCaptioning.this.type

    Definition Classes
    HasImageFeatureProperties
  153. def setImageStd(value: Array[Double]): VisionEncoderDecoderForImageCaptioning.this.type

    Definition Classes
    HasImageFeatureProperties
  154. final def setInputCols(value: String*): VisionEncoderDecoderForImageCaptioning.this.type
    Definition Classes
    HasInputAnnotationCols
  155. def setInputCols(value: Array[String]): VisionEncoderDecoderForImageCaptioning.this.type

    Overrides required annotators column if different than default

    Overrides required annotators column if different than default

    Definition Classes
    HasInputAnnotationCols
  156. def setLazyAnnotator(value: Boolean): VisionEncoderDecoderForImageCaptioning.this.type
    Definition Classes
    CanBeLazy
  157. def setMaxInputLength(value: Int): VisionEncoderDecoderForImageCaptioning.this.type
    Definition Classes
    HasGeneratorProperties
  158. def setMaxOutputLength(value: Int): VisionEncoderDecoderForImageCaptioning.this.type

    Definition Classes
    HasGeneratorProperties
  159. def setMerges(value: Map[(String, String), Int]): VisionEncoderDecoderForImageCaptioning.this.type

    Attributes
    protected[nlp]
  160. def setMinOutputLength(value: Int): VisionEncoderDecoderForImageCaptioning.this.type

    Definition Classes
    HasGeneratorProperties
  161. def setModelIfNotSet(spark: SparkSession, tensorflow: TensorflowWrapper, preprocessor: Preprocessor): VisionEncoderDecoderForImageCaptioning.this.type

  162. def setNReturnSequences(beamNum: Int): VisionEncoderDecoderForImageCaptioning.this.type

    Definition Classes
    HasGeneratorProperties
  163. def setNoRepeatNgramSize(value: Int): VisionEncoderDecoderForImageCaptioning.this.type

    Definition Classes
    HasGeneratorProperties
  164. final def setOutputCol(value: String): VisionEncoderDecoderForImageCaptioning.this.type

    Overrides annotation column name when transforming

    Overrides annotation column name when transforming

    Definition Classes
    HasOutputAnnotationCol
  165. def setParent(parent: Estimator[VisionEncoderDecoderForImageCaptioning]): VisionEncoderDecoderForImageCaptioning
    Definition Classes
    Model
  166. def setRandomSeed(value: Long): VisionEncoderDecoderForImageCaptioning.this.type

    Definition Classes
    HasGeneratorProperties
  167. def setRepetitionPenalty(value: Double): VisionEncoderDecoderForImageCaptioning.this.type

    Definition Classes
    HasGeneratorProperties
  168. def setResample(value: Int): VisionEncoderDecoderForImageCaptioning.this.type

    Definition Classes
    HasImageFeatureProperties
  169. def setRescaleFactor(value: Double): VisionEncoderDecoderForImageCaptioning.this.type

    Definition Classes
    HasRescaleFactor
  170. def setSignatures(value: Map[String, String]): VisionEncoderDecoderForImageCaptioning.this.type

  171. def setSize(value: Int): VisionEncoderDecoderForImageCaptioning.this.type

    Definition Classes
    HasImageFeatureProperties
  172. def setTask(value: String): VisionEncoderDecoderForImageCaptioning.this.type

    Definition Classes
    HasGeneratorProperties
  173. def setTemperature(value: Double): VisionEncoderDecoderForImageCaptioning.this.type

    Definition Classes
    HasGeneratorProperties
  174. def setTopK(value: Int): VisionEncoderDecoderForImageCaptioning.this.type

    Definition Classes
    HasGeneratorProperties
  175. def setTopP(value: Double): VisionEncoderDecoderForImageCaptioning.this.type

    Definition Classes
    HasGeneratorProperties
  176. def setVocabulary(value: Map[String, Int]): VisionEncoderDecoderForImageCaptioning.this.type

    Attributes
    protected[nlp]
  177. val signatures: MapFeature[String, String]

    It contains TF model signatures for the laded saved model

  178. val size: IntParam

    Resize the input to the given size.

    Resize the input to the given size. If a tuple is provided, it should be (width, height). If only an integer is provided, then the input will be resized to (size, size). Only has an effect if do_resize is set to True.

    Definition Classes
    HasImageFeatureProperties
  179. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  180. val task: Param[String]

    Set transformer task, e.g.

    Set transformer task, e.g. "summarize:" (Default: "").

    Definition Classes
    HasGeneratorProperties
  181. val temperature: DoubleParam

    The value used to module the next token probabilities (Default: 1.0)

    The value used to module the next token probabilities (Default: 1.0)

    Definition Classes
    HasGeneratorProperties
  182. def toString(): String
    Definition Classes
    Identifiable → AnyRef → Any
  183. val topK: IntParam

    The number of highest probability vocabulary tokens to keep for top-k-filtering (Default: 50)

    The number of highest probability vocabulary tokens to keep for top-k-filtering (Default: 50)

    Definition Classes
    HasGeneratorProperties
  184. val topP: DoubleParam

    If set to float < 1.0, only the most probable tokens with probabilities that add up to topP or higher are kept for generation (Default: 1.0)

    If set to float < 1.0, only the most probable tokens with probabilities that add up to topP or higher are kept for generation (Default: 1.0)

    Definition Classes
    HasGeneratorProperties
  185. final def transform(dataset: Dataset[_]): DataFrame

    Given requirements are met, this applies ML transformation within a Pipeline or stand-alone Output annotation will be generated as a new column, previous annotations are still available separately metadata is built at schema level to record annotations structural information outside its content

    Given requirements are met, this applies ML transformation within a Pipeline or stand-alone Output annotation will be generated as a new column, previous annotations are still available separately metadata is built at schema level to record annotations structural information outside its content

    dataset

    Dataset[Row]

    Definition Classes
    AnnotatorModel → Transformer
  186. def transform(dataset: Dataset[_], paramMap: ParamMap): DataFrame
    Definition Classes
    Transformer
    Annotations
    @Since( "2.0.0" )
  187. def transform(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): DataFrame
    Definition Classes
    Transformer
    Annotations
    @Since( "2.0.0" ) @varargs()
  188. final def transformSchema(schema: StructType): StructType

    requirement for pipeline transformation validation.

    requirement for pipeline transformation validation. It is called on fit()

    Definition Classes
    RawAnnotator → PipelineStage
  189. def transformSchema(schema: StructType, logging: Boolean): StructType
    Attributes
    protected
    Definition Classes
    PipelineStage
    Annotations
    @DeveloperApi()
  190. val uid: String
    Definition Classes
    VisionEncoderDecoderForImageCaptioning → Identifiable
  191. def validate(schema: StructType): Boolean

    takes a Dataset and checks to see if all the required annotation types are present.

    takes a Dataset and checks to see if all the required annotation types are present.

    schema

    to be validated

    returns

    True if all the required types are present, else false

    Attributes
    protected
    Definition Classes
    RawAnnotator
  192. val vocabulary: MapFeature[String, Int]

    Vocabulary used to encode the words to ids with bpeTokenizer.encode

    Vocabulary used to encode the words to ids with bpeTokenizer.encode

    Attributes
    protected[nlp]
  193. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  194. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  195. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native()
  196. def wrapColumnMetadata(col: Column): Column
    Attributes
    protected
    Definition Classes
    RawAnnotator
  197. def write: MLWriter
    Definition Classes
    ParamsAndFeaturesWritable → DefaultParamsWritable → MLWritable
  198. def writeTensorflowHub(path: String, tfPath: String, spark: SparkSession, suffix: String = "_use"): Unit
    Definition Classes
    WriteTensorflowModel
  199. def writeTensorflowModel(path: String, spark: SparkSession, tensorflow: TensorflowWrapper, suffix: String, filename: String, configProtoBytes: Option[Array[Byte]] = None): Unit
    Definition Classes
    WriteTensorflowModel
  200. def writeTensorflowModelV2(path: String, spark: SparkSession, tensorflow: TensorflowWrapper, suffix: String, filename: String, configProtoBytes: Option[Array[Byte]] = None, savedSignatures: Option[Map[String, String]] = None): Unit
    Definition Classes
    WriteTensorflowModel

Inherited from HasGeneratorProperties

Inherited from HasRescaleFactor

Inherited from HasEngine

Inherited from WriteTensorflowModel

Inherited from HasImageFeatureProperties

Inherited from CanBeLazy

Inherited from HasOutputAnnotationCol

Inherited from HasInputAnnotationCols

Inherited from HasOutputAnnotatorType

Inherited from ParamsAndFeaturesWritable

Inherited from HasFeatures

Inherited from DefaultParamsWritable

Inherited from MLWritable

Inherited from Transformer

Inherited from PipelineStage

Inherited from Logging

Inherited from Params

Inherited from Serializable

Inherited from Serializable

Inherited from Identifiable

Inherited from AnyRef

Inherited from Any

Parameters

A list of (hyper-)parameter keys this annotator can take. Users can set and get the parameter values through setters and getters, respectively.

Annotator types

Required input and expected output annotator types

Members

Parameter setters

Parameter getters