infer_util.py 文件源码

def _frame_metrics(frame_labels, frame_predictions):
  """Calculate frame-based metrics."""
  frame_labels_bool = tf.cast(frame_labels, tf.bool)
  frame_predictions_bool = tf.cast(frame_predictions, tf.bool)

  frame_true_positives = tf.reduce_sum(tf.to_float(tf.logical_and(
      tf.equal(frame_labels_bool, True),
      tf.equal(frame_predictions_bool, True))))
  frame_false_positives = tf.reduce_sum(tf.to_float(tf.logical_and(
      tf.equal(frame_labels_bool, False),
      tf.equal(frame_predictions_bool, True))))
  frame_false_negatives = tf.reduce_sum(tf.to_float(tf.logical_and(
      tf.equal(frame_labels_bool, True),
      tf.equal(frame_predictions_bool, False))))
  frame_accuracy = tf.reduce_sum(tf.to_float(
      tf.equal(frame_labels_bool, frame_predictions_bool)))

  frame_precision = tf.where(
      tf.greater(frame_true_positives + frame_false_positives, 0),
      tf.div(frame_true_positives,
             frame_true_positives + frame_false_positives),
      0)
  frame_recall = tf.where(
      tf.greater(frame_true_positives + frame_false_negatives, 0),
      tf.div(frame_true_positives,
             frame_true_positives + frame_false_negatives),
      0)
  frame_f1_score = f1_score(frame_precision, frame_recall)
  frame_accuracy_without_true_negatives = accuracy_without_true_negatives(
      frame_true_positives, frame_false_positives, frame_false_negatives)

  return {
      'true_positives': frame_true_positives,
      'false_positives': frame_false_positives,
      'false_negatives': frame_false_negatives,
      'accuracy': frame_accuracy,
      'accuracy_without_true_negatives': frame_accuracy_without_true_negatives,
      'precision': frame_precision,
      'recall': frame_recall,
      'f1_score': frame_f1_score,
  }