model_utils.py 文件源码

def SampleRandomSequence(model_input, num_frames, num_samples):
  """Samples a random sequence of frames of size num_samples.

  Args:
    model_input: A tensor of size batch_size x max_frames x feature_size
    num_frames: A tensor of size batch_size x 1
    num_samples: A scalar

  Returns:
    `model_input`: A tensor of size batch_size x num_samples x feature_size
  """

  batch_size = tf.shape(model_input)[0]
  frame_index_offset = tf.tile(
      tf.expand_dims(tf.range(num_samples), 0), [batch_size, 1])
  max_start_frame_index = tf.maximum(num_frames - num_samples, 0)
  start_frame_index = tf.cast(
      tf.multiply(
          tf.random_uniform([batch_size, 1]),
          tf.cast(max_start_frame_index + 1, tf.float32)), tf.int32)
  frame_index = tf.minimum(start_frame_index + frame_index_offset,
                           tf.cast(num_frames - 1, tf.int32))
  batch_index = tf.tile(
      tf.expand_dims(tf.range(batch_size), 1), [1, num_samples])
  index = tf.stack([batch_index, frame_index], 2)
  return tf.gather_nd(model_input, index)