eval_util.py 文件源码

def calculate_gap(predictions, actuals, top_k=20):
  """Performs a local (numpy) calculation of the global average precision.

  Only the top_k predictions are taken for each of the videos.

  Args:
    predictions: Matrix containing the outputs of the model.
      Dimensions are 'batch' x 'num_classes'.
    actuals: Matrix containing the ground truth labels.
      Dimensions are 'batch' x 'num_classes'.
    top_k: How many predictions to use per video.

  Returns:
    float: The global average precision.
  """
  gap_calculator = ap_calculator.AveragePrecisionCalculator()
  sparse_predictions, sparse_labels, num_positives = top_k_by_class(predictions, actuals, top_k)
  gap_calculator.accumulate(flatten(sparse_predictions), flatten(sparse_labels), sum(num_positives))
  return gap_calculator.peek_ap_at_n()