TypeError: 'NoneType' object is not iterable

[odakiese]

After installing all packages I get the error:

compare_gan/gans/modular_gan.py:400 _preprocess_fn  *
    features = {
gin/config.py:407 wrapper  *
    operative_parameter_values = _get_default_configurable_parameter_values(
gin/config.py:738 _get_default_configurable_parameter_values  *
    representable = _is_literally_representable(arg_vals[k])
gin/config.py:537 _is_literally_representable  *
    return _format_value(value) is not None
gin/config.py:520 _format_value  *
    if parse_value(literal) == value:
gin/config.py:1480 parse_value  *
    return config_parser.ConfigParser(value, ParserDelegate()).parse_value()
gin/config_parser.py:250 parse_value  *
    self._raise_syntax_error('Unable to parse value.')
gin/config_parser.py:287 _raise_syntax_error  *
    raise SyntaxError(msg, location)
tensorflow_core/python/autograph/impl/api.py:396 converted_call
    return py_builtins.overload_of(f)(*args)

TypeError: 'NoneType' object is not iterable

[a7b23]

+1

[ilyakava]

Happening on python 3.6.9 and tf 1.15.0. For some reason any dataset appears as empty.

What worked for me to avoid this error is replacing the train_input_fn here with:

  def train_input_fn(self, params=None, preprocess_fn=None):
    """Input function for reading data.

    Args:
      params: Python dictionary with parameters. Must contain the key
        "batch_size". TPUEstimator will set this for you!
      preprocess_fn: Function to process single examples. This is allowed to
        have a `seed` argument.

    Returns:
      `tf.data.Dataset` with preprocessed and batched examples.
    """
    if params is None:
      params = {}
    seed = self._get_per_host_random_seed(params.get("context", None))
    logging.info("train_input_fn(): params=%s seed=%s", params, seed)

    ds = self._load_dataset(split=self._train_split)
    # ds = ds.filter(self._train_filter_fn)
    ds = ds.repeat()
    def one_function(image, label):
      images, labels = self._train_transform_fn(image, label, seed=seed)
      features = {
          "images": images,
          "z": np.random.randn(120).astype(np.float32),
      }
      features["sampled_labels"] = labels

      return features, labels
    ds = ds.map(one_function)

    # ds = ds.map(functools.partial(self._train_transform_fn, seed=seed))
    # if preprocess_fn is not None:
      # if "seed" in inspect.getargspec(preprocess_fn).args:
      #   preprocess_fn = functools.partial(preprocess_fn, seed=seed)
      # ds = ds.map(one_function)
      # Add a feature for the random offset of operations in tpu_random.py.
    ds = tpu_random.add_random_offset_to_features(ds)
    # ds = ds.shuffle(FLAGS.data_shuffle_buffer_size, seed=seed)
    if "batch_size" in params:
      ds = ds.batch(params["batch_size"], drop_remainder=True)
    return ds.prefetch(tf.contrib.data.AUTOTUNE)