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Standard

Standard processors

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ControlMap

paz.processors.standard.ControlMap(processor, intro_indices=[0], outro_indices=[0], keep=None)

Controls which inputs are passed ''processor'' and the order of its outputs.

Arguments

  • processor: Function e.g. a ''paz.processor''
  • intro_indices: List of Ints.
  • outro_indices: List of Ints.
  • keep: ''None'' or dictionary. If None control maps operates without explicitly retaining an input. If dict it must contain as keys the input args to be kept and as values where they should be located at the end.

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ExpandDomain

paz.processors.standard.ExpandDomain(processor)

Extends number of inputs a function can take applying the identity function to all new/extended inputs. e.g. For a given function f(x) = y. If g = ExtendInputs(f), we can now have g(x, x1, x2, ..., xn) = y, x1, x2, ..., xn.

Arguments

  • processor: Function e.g. any procesor in ''paz.processors''.

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CopyDomain

paz.processors.standard.CopyDomain(intro_indices, outro_indices)

Copies ''intro_indices'' and places it ''outro_indices''.

Arguments

  • intro_indices: List of Ints.
  • outro_indices: List of Ints.

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ExtendInputs

paz.processors.standard.ExtendInputs(processor)

Extends number of inputs a function can take applying the identity function to all new/extended inputs. e.g. For a given function f(x) = y. If g = ExtendInputs(f), we can now have g(x, x1, x2, ..., xn) = y, x1, x2, ..., xn.

Arguments

  • processor: Function e.g. any procesor in ''paz.processors''.

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SequenceWrapper

paz.processors.standard.SequenceWrapper(inputs_info, labels_info)

Wraps arguments to directly use ''paz.abstract.ProcessingSequence'' or ''paz.abstract.GeneratingSequence''.

Arguments

  • inputs_info: Dictionary containing an integer per key representing the argument to grab, and as value a dictionary containing the tensor name as key and the tensor shape of a single sample as value e.g. {0: {'input_image': [300, 300, 3]}, 1: {'depth': [300, 300]}}. The values given here are for the inputs of the model.
  • labels_info: Dictionary containing an integer per key representing the argument to grab, and as value a dictionary containing the tensor name as key and the tensor shape of a single sample as value e.g. {2: {'classes': [10]}}. The values given here are for the labels of the model.

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Predict

paz.processors.standard.Predict(model, preprocess=None, postprocess=None)

Perform input preprocessing, model prediction and output postprocessing.

Arguments

  • model: Class with a ''predict'' method e.g. a Keras model.
  • preprocess: Function applied to given inputs.
  • postprocess: Function applied to outputted predictions from model.

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PredictWithNones

paz.processors.standard.PredictWithNones(model, preprocess=None, postprocess=None)

Perform input preprocessing, model prediction and output postprocessing based on batches.

Arguments

  • model: Class with a ''predict'' method e.g. a Keras model.
  • preprocess: Function applied to given inputs.
  • postprocess: Function applied to outputted predictions from model.

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ToClassName

paz.processors.standard.ToClassName(labels)

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ExpandDims

paz.processors.standard.ExpandDims(axis)

Expand dimension of given array.

Arguments

  • axis: Int.

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BoxClassToOneHotVector

paz.processors.standard.BoxClassToOneHotVector(num_classes)

Transform box data with class index to a one-hot encoded vector.

Arguments

  • num_classes: Integer. Total number of classes.

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Squeeze

paz.processors.standard.Squeeze(axis)

Wrap around numpy squeeze due to common use before model predict. Arguments

  • expand_dims: Int or list of Ints.
  • topic: String.

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Copy

paz.processors.standard.Copy()

Copies value passed to function.

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Lambda

paz.processors.standard.Lambda(function)

Applies a lambda function as a processor transformation.

Arguments

  • function: Function.

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UnpackDictionary

paz.processors.standard.UnpackDictionary(order)

Unpacks dictionary into a tuple. Arguments

  • order: List of strings containing the keys of the dictionary. The order of the list is the order in which the tuple would be ordered.

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WrapOutput

paz.processors.standard.WrapOutput(keys)

Wraps arguments in dictionary

Arguments

  • keys: List of strings representing the keys used to wrap the inputs. The order of the list must correspond to the same order of inputs (''args'').

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Concatenate

paz.processors.standard.Concatenate(axis)

Concatenates a list of arrays in given ''axis''.

Arguments

  • axis: Int.

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SelectElement

paz.processors.standard.SelectElement(index)

Selects element of input value.

Arguments

  • index: Int. argument to select from ''inputs''.

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StochasticProcessor

paz.processors.standard.StochasticProcessor(probability=0.5, name=None)

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Stochastic

paz.processors.standard.Stochastic(function, probability=0.5, name=None)

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UnwrapDictionary

paz.processors.standard.UnwrapDictionary(keys)

Unwraps a dictionry into a list given the key order.

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Scale

paz.processors.standard.Scale(scales)

Scales an input.

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AppendValues

paz.processors.standard.AppendValues(keys)

Append dictionary values to lists

Arguments

  • keys: Keys to dictionary values

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BooleanToTextMessage

paz.processors.standard.BooleanToTextMessage(true_message, false_message)

Convert a boolean to text message. Arguments

  • true_message: String. Message for true case.
  • false_message: String. Message for false case.
  • Flag: Boolean.

Returns

  • message: String.

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PrintTopics

paz.processors.standard.PrintTopics(topics)

Prints topics Arguments

  • topics: List of keys to the inputted dictionary

Returns

Returns same dictionary but outputs to terminal topic values.

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FloatToBoolean

paz.processors.standard.FloatToBoolean(threshold=0.5)

Converts a float to a boolean. Arguments

  • threshold: Float. Threshold value to convert to boolean.
  • value: Float.

Returns

Boolean.

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NoneConverter

paz.processors.standard.NoneConverter(default_value=0.0)

Converts a None value to the last valid or a default value. Arguments

  • default_value: Any. Default value to convert to until a first valid value is stored.
  • value: Any Noneable value.

Returns

Any.

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AveragePredictions

paz.processors.standard.AveragePredictions(window_size=1, weighted=False)

Averages the last n predictions Arguments

  • window_size: Int. Number of predictions to average over.
  • weighted: Bool. If True, the average is weighted by the index of the prediction.
  • value: Bool, Int or Float value. Value to average over. Returns

Bool, Int or Float value. Averaged value.