5e854f25098ca40a4ac80197607bf883feaeb375,research/object_detection/utils/learning_schedules.py,,manual_stepping,#Any#Any#Any#,109

Before Change

  step_boundaries = tf.constant(boundaries, tf.int64)
  learning_rates = tf.constant(rates, tf.float32)
  unreached_boundaries = tf.reshape(
      tf.where(tf.greater(step_boundaries, global_step)), [-1])
  unreached_boundaries = tf.concat([unreached_boundaries, [len(boundaries)]], 0)
  index = tf.reshape(tf.reduce_min(unreached_boundaries), [1])
  return tf.reshape(tf.slice(learning_rates, index, [1]), [])

After Change

    raise ValueError("Number of provided learning rates must exceed "
                     "number of boundary points by exactly 1.")
  step_boundaries = tf.constant(boundaries, tf.int32)
  num_boundaries = len(boundaries)
  learning_rates = tf.constant(rates, tf.float32)
  index = tf.reduce_min(
      tf.where(
          // Casting global step to tf.int32 is dangerous, but necessary to be
          // compatible with TPU.
          tf.greater(step_boundaries, tf.cast(global_step, tf.int32)),
          tf.constant(range(num_boundaries), dtype=tf.int32),
          tf.constant([num_boundaries] * num_boundaries, dtype=tf.int32)))
  return tf.reduce_sum(learning_rates * tf.one_hot(index, len(rates),
                                                   dtype=tf.float32))

Instances