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class PostProcessing extends AnyRef

PostProssing PostProcessing contains two steps step 1 is filter, which is optional, used to transform output tensor to type wanted step 2 is to ndarray string, which is mandatory to parse tensor into readable string this string could be parsed by json in Python to a list

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Instance Constructors

  1. new PostProcessing(tensor: Tensor[Float], filter: String = "")

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  15. def pickTopN(topN: Int): String

    Pick TopN value of output tensor only (1) * record_size * box_value_number is supported thus only 2 or 3 dimension is valid for now

  16. def processTensor(): String
  17. def rankTopN(topN: Int): String

    TopN filter, take 1-D size (n) tensor as input

    TopN filter, take 1-D size (n) tensor as input

    returns

    string, representing 2-D size (topN, 2) tensor

  18. final def synchronized[T0](arg0: ⇒ T0): T0
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  19. var t: Tensor[Float]
  20. def tensorToNdArrayString(): String

    Transform tensor into readable string, could apply to any shape of tensor

  21. def toString(): String
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  22. val totalSize: Int
  23. final def wait(): Unit
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