Method list_files

tensorflow/python/data/ops/dataset_ops.py:842–899

A dataset of all files matching one or more glob patterns. NOTE: The default behavior of this method is to return filenames in a non-deterministic random shuffled order. Pass a `seed` or `shuffle=False` to get results in a deterministic order. Example: If we had the following

(file_pattern, shuffle=None, seed=None)

  @staticmethod
  def list_files(file_pattern, shuffle=None, seed=None):
    """A dataset of all files matching one or more glob patterns.

    NOTE: The default behavior of this method is to return filenames in
    a non-deterministic random shuffled order. Pass a `seed` or `shuffle=False`
    to get results in a deterministic order.

    Example:
      If we had the following files on our filesystem:
        - /path/to/dir/a.txt
        - /path/to/dir/b.py
        - /path/to/dir/c.py
      If we pass "/path/to/dir/*.py" as the directory, the dataset
      would produce:
        - /path/to/dir/b.py
        - /path/to/dir/c.py

    Args:
      file_pattern: A string, a list of strings, or a `tf.Tensor` of string type
        (scalar or vector), representing the filename glob (i.e. shell wildcard)
        pattern(s) that will be matched.
      shuffle: (Optional.) If `True`, the file names will be shuffled randomly.
        Defaults to `True`.
      seed: (Optional.) A `tf.int64` scalar `tf.Tensor`, representing the random
        seed that will be used to create the distribution. See
        `tf.compat.v1.set_random_seed` for behavior.

    Returns:
     Dataset: A `Dataset` of strings corresponding to file names.
    """
    with ops.name_scope("list_files"):
      if shuffle is None:
        shuffle = True
      file_pattern = ops.convert_to_tensor(
          file_pattern, dtype=dtypes.string, name="file_pattern")
      matching_files = gen_io_ops.matching_files(file_pattern)

      # Raise an exception if `file_pattern` does not match any files.
      condition = math_ops.greater(array_ops.shape(matching_files)[0], 0,
                                   name="match_not_empty")

      message = math_ops.add(
          "No files matched pattern: ",
          string_ops.reduce_join(file_pattern, separator=", "), name="message")

      assert_not_empty = control_flow_ops.Assert(
          condition, [message], summarize=1, name="assert_not_empty")
      with ops.control_dependencies([assert_not_empty]):
        matching_files = array_ops.identity(matching_files)

      dataset = Dataset.from_tensor_slices(matching_files)
      if shuffle:
        # NOTE(mrry): The shuffle buffer size must be greater than zero, but the
        # list of files might be empty.
        buffer_size = math_ops.maximum(
            array_ops.shape(matching_files, out_type=dtypes.int64)[0], 1)
        dataset = dataset.shuffle(buffer_size, seed=seed)
      return dataset