org.apache.mxnet.infer.javaapi

Predictor

Related Doc: package javaapi

class Predictor extends AnyRef

Implementation of prediction routines.

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Instance Constructors

  1. new Predictor(modelPathPrefix: String, inputDescriptors: Iterable[javaapi.DataDesc], contexts: Iterable[javaapi.Context], epoch: Int)

    modelPathPrefix

    Path prefix from where to load the model artifacts. These include the symbol, parameters, and synset.txt Example: file://model-dir/resnet-152 (containing resnet-152-symbol.json, resnet-152-0000.params, and synset.txt).

    inputDescriptors

    Descriptors defining the input node names, shape, layout and type parameters

    Note: If the input Descriptors is missing batchSize ('N' in layout), a batchSize of 1 is assumed for the model.

    contexts

    Device contexts on which you want to run inference; defaults to CPU

    epoch

    Model epoch to load; defaults to 0

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  15. def predict(input: List[List[Float]]): List[List[Float]]

    Takes input as List of one dimensional iterables and creates the NDArray needed for inference The array will be reshaped based on the input descriptors.

    Takes input as List of one dimensional iterables and creates the NDArray needed for inference The array will be reshaped based on the input descriptors.

    input

    A List of a one-dimensional iterables of DType Float. An extra List is needed for when the model has more than one input.

    returns

    Indexed sequence array of outputs

  16. def predict(input: Array[Array[Double]]): Array[Array[Double]]

    Takes input as Array of one dimensional arrays and creates the NDArray needed for inference The array will be reshaped based on the input descriptors.

    Takes input as Array of one dimensional arrays and creates the NDArray needed for inference The array will be reshaped based on the input descriptors. Example of calling in Java:

    double tmp[][] = new double[1][224];
    for (int x = 0; x < 1; x++)
      for (int y = 0; y < 224; y++)
        tmp[x][y] = (int)(Math.random()*10);
    predictor.predict(tmp);
    input

    An Array of a one-dimensional array. An extra Array is needed for when the model has more than one input.

    returns

    Indexed sequence array of outputs

  17. def predict(input: Array[Array[Float]]): Array[Array[Float]]

    Takes input as Array of one dimensional arrays and creates the NDArray needed for inference The array will be reshaped based on the input descriptors.

    Takes input as Array of one dimensional arrays and creates the NDArray needed for inference The array will be reshaped based on the input descriptors. Example of calling in Java:

    float tmp[][] = new float[1][224];
    for (int x = 0; x < 1; x++)
      for (int y = 0; y < 224; y++)
        tmp[x][y] = (int)(Math.random()*10);
    predictor.predict(tmp);
    input

    An Array of a one-dimensional array. An extra Array is needed for when the model has more than one input.

    returns

    Indexed sequence array of outputs

  18. def predictWithNDArray(input: Iterable[javaapi.NDArray]): List[javaapi.NDArray]

    Predict using NDArray as input This method is useful when the input is a batch of data Note: User is responsible for managing allocation/deallocation of input/output NDArrays.

    Predict using NDArray as input This method is useful when the input is a batch of data Note: User is responsible for managing allocation/deallocation of input/output NDArrays.

    input

    Iterable of NDArrays

    returns

    Output of predictions as NDArrays

  19. val predictor: infer.Predictor

    The underlying Scala predictor

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