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Metrics

File: metrics.py

Metric classes for evaluating model performance.

Accuracy

Bases: MeanMetric

Subclass of MeanMetric which defines a standard accuracy update function.

Source code in torchplate/metrics.py 
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class Accuracy(MeanMetric):
    """
    Subclass of MeanMetric which defines 
    a standard accuracy update function. 
    """

    def update(self, logits, labels):
        return calculate_accuracy(logits, labels)

MeanMetric

Scalar metric designed to use on a per-epoch basis and updated on per-batch basis. For getting average across the epoch.

Source code in torchplate/metrics.py 
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class MeanMetric:
  """
  Scalar metric designed to use on a per-epoch basis 
  and updated on per-batch basis. For getting average across
  the epoch. 
  """

  def __init__(self):
    self.vals = []

  def update(self, new_val):
    self.vals.append(new_val)

  def reset(self):
    self.vals = []

  def get(self):
    mean_value = sum(self.vals)/len(self.vals)
    return mean_value

MeanMetricCustom

Bases: ABC, MeanMetric

Abstract scalar metric. Must provide calculation given logits and y.

Source code in torchplate/metrics.py 
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class MeanMetricCustom(ABC, MeanMetric):
  """
  Abstract scalar metric. Must provide calculation given logits and y. 
  """

  def __init__(self):
    self.vals = []

  @abstractmethod
  def calculate(self, logits, y):
    # returns a value
    pass

  def update(self, logits, y):    
    self.vals.append(self.calculate(logits,y))