Python tensorflow.split() Examples

def is_generate_per_split(self):
    """A single call to `generate_samples` generates for all `dataset_splits`.

    Set to True if you already have distinct subsets of data for each dataset
    split specified in `self.dataset_splits`. `self.generate_samples` will be
    called once for each split.

    Set to False if you have a unified dataset that you'd like to have split out
    into training and evaluation data automatically. `self.generate_samples`
    will be called only once and the data will be sharded across the dataset
    splits specified in `self.dataset_splits`.

    Returns:
      bool
    """
    raise NotImplementedError() 

def __call__(self, inputs, state, scope=None):
    """GRU cell with layer normalization."""
    input_dim = inputs.get_shape().as_list()[1]
    num_units = self._num_units

    with tf.variable_scope(scope or "gru_cell"):
      with tf.variable_scope("gates"):
        w_h = tf.get_variable(
            "w_h", [num_units, 2 * num_units],
            initializer=self._w_h_initializer())
        w_x = tf.get_variable(
            "w_x", [input_dim, 2 * num_units],
            initializer=self._w_x_initializer(input_dim))
        z_and_r = (_layer_norm(tf.matmul(state, w_h), scope="layer_norm/w_h") +
                   _layer_norm(tf.matmul(inputs, w_x), scope="layer_norm/w_x"))
        z, r = tf.split(tf.sigmoid(z_and_r), 2, 1)
      with tf.variable_scope("candidate"):
        w = tf.get_variable(
            "w", [input_dim, num_units], initializer=self._w_initializer)
        u = tf.get_variable(
            "u", [num_units, num_units], initializer=self._u_initializer)
        h_hat = (r * _layer_norm(tf.matmul(state, u), scope="layer_norm/u") +
                 _layer_norm(tf.matmul(inputs, w), scope="layer_norm/w"))
      new_h = (1 - z) * state + z * self._activation(h_hat)
    return new_h, new_h 

def batch_random_flip(input_):
    """Simultaneous horizontal random flip."""
    if isinstance(input_, (float, int)):
        return input_
    shape = input_.get_shape().as_list()
    batch_size = shape[0]
    height = shape[1]
    width = shape[2]
    channels = shape[3]
    res = tf.split(axis=0, num_or_size_splits=batch_size, value=input_)
    res = [elem[0, :, :, :] for elem in res]
    res = [tf.image.random_flip_left_right(elem) for elem in res]
    res = [tf.reshape(elem, [1, height, width, channels]) for elem in res]
    res = tf.concat(axis=0, values=res)

    return res


# build a one hot representation corresponding to the integer tensor
# the one-hot dimension is appended to the integer tensor shape 

def preprocess_batch(images_batch, preproc_func=None):
    """
    Creates a preprocessing graph for a batch given a function that processes
    a single image.

    :param images_batch: A tensor for an image batch.
    :param preproc_func: (optional function) A function that takes in a
        tensor and returns a preprocessed input.
    """
    if preproc_func is None:
        return images_batch

    with tf.variable_scope('preprocess'):
        images_list = tf.split(images_batch, int(images_batch.shape[0]))
        result_list = []
        for img in images_list:
            reshaped_img = tf.reshape(img, img.shape[1:])
            processed_img = preproc_func(reshaped_img)
            result_list.append(tf.expand_dims(processed_img, axis=0))
        result_images = tf.concat(result_list, axis=0)
    return result_images 

def init_uninited_vars(vars=None):
    if vars is None: vars = tf.global_variables()
    test_vars = []; test_ops = []
    with tf.control_dependencies(None): # ignore surrounding control_dependencies
        for var in vars:
            assert is_tf_expression(var)
            try:
                tf.get_default_graph().get_tensor_by_name(var.name.replace(':0', '/IsVariableInitialized:0'))
            except KeyError:
                # Op does not exist => variable may be uninitialized.
                test_vars.append(var)
                with absolute_name_scope(var.name.split(':')[0]):
                    test_ops.append(tf.is_variable_initialized(var))
    init_vars = [var for var, inited in zip(test_vars, run(test_ops)) if not inited]
    run([var.initializer for var in init_vars])

#----------------------------------------------------------------------------
# Set the values of given tf.Variables.
# Equivalent to the following, but more efficient and does not bloat the tf graph:
#   tfutil.run([tf.assign(var, value) for var, value in var_to_value_dict.items()] 

def create_session(config_dict=dict(), force_as_default=False):
    config = tf.ConfigProto()
    for key, value in config_dict.items():
        fields = key.split('.')
        obj = config
        for field in fields[:-1]:
            obj = getattr(obj, field)
        setattr(obj, fields[-1], value)
    session = tf.Session(config=config)
    if force_as_default:
        session._default_session = session.as_default()
        session._default_session.enforce_nesting = False
        session._default_session.__enter__()
    return session

#----------------------------------------------------------------------------
# Initialize all tf.Variables that have not already been initialized.
# Equivalent to the following, but more efficient and does not bloat the tf graph:
#   tf.variables_initializer(tf.report_unitialized_variables()).run() 

def get_layer_size(self, layer_name):
    if layer_name == 'logits':
      return self._component.num_actions

    if layer_name == 'last_layer':
      return self._hidden_layer_sizes[-1]

    if not layer_name.startswith('layer_'):
      logging.fatal(
          'Invalid layer name: "%s" Can only retrieve from "logits", '
          '"last_layer", and "layer_*".',
          layer_name)

    # NOTE(danielandor): Since get_layer_size is called before the
    # model has been built, we compute the layer size directly from
    # the hyperparameters rather than from self._layers.
    layer_index = int(layer_name.split('_')[1])
    return self._hidden_layer_sizes[layer_index] 

def clip_to_window(keypoints, window, scope=None):
  """Clips keypoints to a window.

  This op clips any input keypoints to a window.

  Args:
    keypoints: a tensor of shape [num_instances, num_keypoints, 2]
    window: a tensor of shape [4] representing the [y_min, x_min, y_max, x_max]
      window to which the op should clip the keypoints.
    scope: name scope.

  Returns:
    new_keypoints: a tensor of shape [num_instances, num_keypoints, 2]
  """
  with tf.name_scope(scope, 'ClipToWindow'):
    y, x = tf.split(value=keypoints, num_or_size_splits=2, axis=2)
    win_y_min, win_x_min, win_y_max, win_x_max = tf.unstack(window)
    y = tf.maximum(tf.minimum(y, win_y_max), win_y_min)
    x = tf.maximum(tf.minimum(x, win_x_max), win_x_min)
    new_keypoints = tf.concat([y, x], 2)
    return new_keypoints 

def scale(boxlist, y_scale, x_scale, scope=None):
  """scale box coordinates in x and y dimensions.

  Args:
    boxlist: BoxList holding N boxes
    y_scale: (float) scalar tensor
    x_scale: (float) scalar tensor
    scope: name scope.

  Returns:
    boxlist: BoxList holding N boxes
  """
  with tf.name_scope(scope, 'Scale'):
    y_scale = tf.cast(y_scale, tf.float32)
    x_scale = tf.cast(x_scale, tf.float32)
    y_min, x_min, y_max, x_max = tf.split(
        value=boxlist.get(), num_or_size_splits=4, axis=1)
    y_min = y_scale * y_min
    y_max = y_scale * y_max
    x_min = x_scale * x_min
    x_max = x_scale * x_max
    scaled_boxlist = box_list.BoxList(
        tf.concat([y_min, x_min, y_max, x_max], 1))
    return _copy_extra_fields(scaled_boxlist, boxlist) 

def intersection(boxlist1, boxlist2, scope=None):
  """Compute pairwise intersection areas between boxes.

  Args:
    boxlist1: BoxList holding N boxes
    boxlist2: BoxList holding M boxes
    scope: name scope.

  Returns:
    a tensor with shape [N, M] representing pairwise intersections
  """
  with tf.name_scope(scope, 'Intersection'):
    y_min1, x_min1, y_max1, x_max1 = tf.split(
        value=boxlist1.get(), num_or_size_splits=4, axis=1)
    y_min2, x_min2, y_max2, x_max2 = tf.split(
        value=boxlist2.get(), num_or_size_splits=4, axis=1)
    all_pairs_min_ymax = tf.minimum(y_max1, tf.transpose(y_max2))
    all_pairs_max_ymin = tf.maximum(y_min1, tf.transpose(y_min2))
    intersect_heights = tf.maximum(0.0, all_pairs_min_ymax - all_pairs_max_ymin)
    all_pairs_min_xmax = tf.minimum(x_max1, tf.transpose(x_max2))
    all_pairs_max_xmin = tf.maximum(x_min1, tf.transpose(x_min2))
    intersect_widths = tf.maximum(0.0, all_pairs_min_xmax - all_pairs_max_xmin)
    return intersect_heights * intersect_widths 

def matched_intersection(boxlist1, boxlist2, scope=None):
  """Compute intersection areas between corresponding boxes in two boxlists.

  Args:
    boxlist1: BoxList holding N boxes
    boxlist2: BoxList holding N boxes
    scope: name scope.

  Returns:
    a tensor with shape [N] representing pairwise intersections
  """
  with tf.name_scope(scope, 'MatchedIntersection'):
    y_min1, x_min1, y_max1, x_max1 = tf.split(
        value=boxlist1.get(), num_or_size_splits=4, axis=1)
    y_min2, x_min2, y_max2, x_max2 = tf.split(
        value=boxlist2.get(), num_or_size_splits=4, axis=1)
    min_ymax = tf.minimum(y_max1, y_max2)
    max_ymin = tf.maximum(y_min1, y_min2)
    intersect_heights = tf.maximum(0.0, min_ymax - max_ymin)
    min_xmax = tf.minimum(x_max1, x_max2)
    max_xmin = tf.maximum(x_min1, x_min2)
    intersect_widths = tf.maximum(0.0, min_xmax - max_xmin)
    return tf.reshape(intersect_heights * intersect_widths, [-1]) 

def flip_boxes(boxes):
  """Left-right flip the boxes.

  Args:
    boxes: rank 2 float32 tensor containing the bounding boxes -> [N, 4].
           Boxes are in normalized form meaning their coordinates vary
           between [0, 1].
           Each row is in the form of [ymin, xmin, ymax, xmax].

  Returns:
    Flipped boxes.
  """
  # Flip boxes.
  ymin, xmin, ymax, xmax = tf.split(value=boxes, num_or_size_splits=4, axis=1)
  flipped_xmin = tf.subtract(1.0, xmax)
  flipped_xmax = tf.subtract(1.0, xmin)
  flipped_boxes = tf.concat([ymin, flipped_xmin, ymax, flipped_xmax], 1)
  return flipped_boxes 

def _Apply(self, *args):
    xtransform = self._TransformInputs(*args)
    depth_axis = len(self._output_shape) - 1

    if self.hidden is not None:
      htransform = self._TransformHidden(self.hidden)
      f, i, j, o = tf.split(
          value=htransform + xtransform, num_or_size_splits=4, axis=depth_axis)
    else:
      f, i, j, o = tf.split(
          value=xtransform, num_or_size_splits=4, axis=depth_axis)

    if self.cell is not None:
      self.cell = tf.sigmoid(f) * self.cell + tf.sigmoid(i) * tf.tanh(j)
    else:
      self.cell = tf.sigmoid(i) * tf.tanh(j)

    self.hidden = tf.sigmoid(o) * tf.tanh(self.cell)
    return self.hidden 

def _Apply(self, *args):
    xtransform = self._TransformInputs(*args)
    depth_axis = len(self._output_shape) - 1

    if self.hidden is not None:
      htransform = self._TransformHidden(self.hidden)
      f, i, j, o = tf.split(
          value=htransform + xtransform, num_or_size_splits=4, axis=depth_axis)
    else:
      f, i, j, o = tf.split(
          value=xtransform, num_or_size_splits=4, axis=depth_axis)

    if self.cell is not None:
      self.cell = tf.sigmoid(f) * self.cell + tf.sigmoid(i) * tf.tanh(j)
    else:
      self.cell = tf.sigmoid(i) * tf.tanh(j)

    self.hidden = tf.sigmoid(o) * tf.tanh(self.cell)

    self._iter += 1
    return self.hidden 

def call(self, inputs):
        mean_and_log_std = self.model(inputs)
        mean, log_std = tf.split(mean_and_log_std, num_or_size_splits=2, axis=1)
        log_std = tf.clip_by_value(log_std, -20., 2.)
        
        distribution = tfp.distributions.MultivariateNormalDiag(
            loc=mean,
            scale_diag=tf.exp(log_std)
        )
        
        raw_actions = distribution.sample()
        if not self._reparameterize:
            ### Problem 1.3.A
            ### YOUR CODE HERE
            raw_actions = tf.stop_gradient(raw_actions)
        log_probs = distribution.log_prob(raw_actions)
        log_probs -= self._squash_correction(raw_actions)

        ### Problem 2.A
        ### YOUR CODE HERE
        self.actions = tf.tanh(raw_actions)
            
        return self.actions, log_probs 

def conv(input, kernel, biases, k_h, k_w, c_o, s_h, s_w,  padding="VALID", group=1):
    '''From https://github.com/ethereon/caffe-tensorflow
    '''
    c_i = input.get_shape()[-1]
    assert c_i%group==0
    assert c_o%group==0
    convolve = lambda i, k: tf.nn.conv2d(i, k, [1, s_h, s_w, 1], padding=padding)


    if group==1:
        conv = convolve(input, kernel)
    else:
        input_groups = tf.split(input, group, 3)
        kernel_groups = tf.split(kernel, group, 3)
        output_groups = [convolve(i, k) for i,k in zip(input_groups, kernel_groups)]
        conv = tf.concat(output_groups, 3)
    return  tf.reshape(tf.nn.bias_add(conv, biases), [-1]+conv.get_shape().as_list()[1:]) 

def read_and_decode(filename_queue):
    reader = tf.TFRecordReader()
    _, serialized_example = reader.read(filename_queue)
    features = tf.parse_single_example(
        serialized_example,
      # Defaults are not specified since both keys are required.
        features={
            'image_raw': tf.FixedLenFeature([], tf.string),
        })

    image = tf.decode_raw(features['image_raw'], tf.uint8)
    image = tf.reshape(image, [227, 227, 6])

  # Convert from [0, 255] -> [-0.5, 0.5] floats.
    image = tf.cast(image, tf.float32) * (1. / 255) - 0.5
    return tf.split(image, 2, 2) # 3rd dimension two parts 

def read_and_decode_aug(filename_queue):
    reader = tf.TFRecordReader()
    _, serialized_example = reader.read(filename_queue)
    features = tf.parse_single_example(
        serialized_example,
      # Defaults are not specified since both keys are required.
        features={
            'image_raw': tf.FixedLenFeature([], tf.string),
        })

    image = tf.decode_raw(features['image_raw'], tf.uint8)
    image = tf.image.random_flip_left_right(tf.reshape(image, [227, 227, 6]))
  # Convert from [0, 255] -> [-0.5, 0.5] floats.
    image = tf.cast(image, tf.float32) * (1. / 255) - 0.5
    image = tf.image.random_brightness(image, 0.01)
    image = tf.image.random_contrast(image, 0.95, 1.05)
    return tf.split(image, 2, 2) # 3rd dimension two parts 

def simulate(self, action):
    with tf.name_scope("environment/simulate"):  # Do we need this?
      initializer = (tf.zeros(self.old_shape, dtype=tf.float32),
                     tf.fill((len(self),), 0.0), tf.fill((len(self),), False))

      def not_done_step(a, _):
        reward, done = self._batch_env.simulate(action)
        with tf.control_dependencies([reward, done]):
          r0 = self._batch_env.observ + 0
          r1 = tf.add(a[1], reward)
          r2 = tf.logical_or(a[2], done)
          return (r0, r1, r2)

      simulate_ret = tf.scan(not_done_step, tf.range(self.skip),
                             initializer=initializer, parallel_iterations=1,
                             infer_shape=False)
      observations, rewards, dones = simulate_ret
      split_observations = tf.split(observations, self.skip, axis=0)
      split_observations = [tf.squeeze(o, axis=0) for o in split_observations]
      observation = tf.concat(split_observations, axis=-1)
      with tf.control_dependencies([self._observ.assign(observation)]):
        return tf.identity(rewards[-1, ...]), tf.identity(dones[-1, ...]) 

def combine(self, expert_out, multiply_by_gates=True):
    """Sum together the expert output, multiplied by the corresponding gates.

    Args:
      expert_out: a list of `num_experts` `Tensor`s, each with shape
        `[expert_batch_size_i, <extra_output_dims>]`.
      multiply_by_gates: a boolean.

    Returns:
      a list of num_datashards `Tensor`s with shapes
        `[batch_size[d], <extra_output_dims>]`.
    """
    expert_part_sizes = tf.unstack(
        tf.stack([d.part_sizes for d in self._dispatchers]),
        num=self._ep.n,
        axis=1)
    # list of lists of shape [num_experts][num_datashards]
    expert_output_parts = self._ep(tf.split, expert_out, expert_part_sizes)
    expert_output_parts_t = transpose_list_of_lists(expert_output_parts)
    def my_combine(dispatcher, parts):
      return dispatcher.combine(
          common_layers.convert_gradient_to_tensor(tf.concat(parts, 0)),
          multiply_by_gates=multiply_by_gates)
    return self._dp(my_combine, self._dispatchers, expert_output_parts_t) 

def conv_lstm(x,
              kernel_size,
              filters,
              padding="SAME",
              dilation_rate=(1, 1),
              name=None,
              reuse=None):
  """Convolutional LSTM in 1 dimension."""
  with tf.variable_scope(
      name, default_name="conv_lstm", values=[x], reuse=reuse):
    gates = conv(
        x,
        4 * filters,
        kernel_size,
        padding=padding,
        dilation_rate=dilation_rate)
    g = tf.split(layer_norm(gates, 4 * filters), 4, axis=3)
    new_cell = tf.sigmoid(g[0]) * x + tf.sigmoid(g[1]) * tf.tanh(g[3])
    return tf.sigmoid(g[2]) * tf.tanh(new_cell) 

def gated_linear_unit_layer(x, name=None):
  """Gated linear unit layer.

  Paper: Language Modeling with Gated Convolutional Networks.
  Link: https://arxiv.org/abs/1612.08083
  x = Wx * sigmoid(W'x).

  Args:
    x: A tensor
    name: A string

  Returns:
    A tensor of the same shape as x.
  """
  with tf.variable_scope(name, default_name="glu_layer", values=[x]):
    depth = shape_list(x)[-1]
    x = tf.layers.dense(x, depth * 2, activation=None)
    x, gating_x = tf.split(x, 2, axis=-1)
    return x * tf.nn.sigmoid(gating_x) 

def testSymbolModalityInputs(self):
    batch_size = 10
    num_datashards = 5
    length = 5
    vocab_size = 5000
    hidden_size = 9
    model_hparams = common_hparams.basic_params1()
    model_hparams.hidden_size = hidden_size
    model_hparams.mode = tf.estimator.ModeKeys.TRAIN
    x = -1 + np.random.random_integers(
        vocab_size, size=(batch_size, length, 1, 1))
    m = modalities.SymbolModality(model_hparams, vocab_size)
    data_parallelism = expert_utils.Parallelism(
        ["/device:CPU:0"] * num_datashards)
    with self.test_session() as session:
      xs = tf.split(x, num_datashards)
      sharded_output = m.bottom_sharded(xs, data_parallelism)
      output = tf.concat(sharded_output, 0)
      session.run(tf.global_variables_initializer())
      res = session.run(output)
    self.assertEqual(res.shape, (batch_size, length, 1, hidden_size)) 

def testForwardBackward(self):

    def f(x):
      return tf.layers.dense(x, self.CHANNELS // 2, use_bias=True)

    def g(x):
      return tf.layers.dense(x, self.CHANNELS // 2, use_bias=True)

    x = tf.random_uniform([self.BATCH_SIZE, self.CHANNELS], dtype=tf.float32)
    x1, x2 = tf.split(x, 2, axis=-1)

    block = rev_block.RevBlock(f, g, num_layers=3)
    y1, y2 = block.forward(x1, x2)
    x1_inv, x2_inv = block.backward(y1, y2)

    with self.test_session() as sess:
      sess.run(tf.global_variables_initializer())
      x1, x2, x1_inv, x2_inv = sess.run([x1, x2, x1_inv, x2_inv])

      self.assertAllClose(x1, x1_inv)
      self.assertAllClose(x2, x2_inv) 

def testBackwardForward(self):

    def f(x):
      return tf.layers.dense(x, self.CHANNELS // 2, use_bias=True)

    def g(x):
      return tf.layers.dense(x, self.CHANNELS // 2, use_bias=True)

    y = tf.random_uniform([self.BATCH_SIZE, self.CHANNELS], dtype=tf.float32)
    y1, y2 = tf.split(y, 2, axis=-1)

    block = rev_block.RevBlock(f, g, num_layers=3)
    x1, x2 = block.backward(y1, y2)
    y1_inv, y2_inv = block.forward(x1, x2)

    with self.test_session() as sess:
      sess.run(tf.global_variables_initializer())
      y1, y2, y1_inv, y2_inv = sess.run([y1, y2, y1_inv, y2_inv])

      self.assertAllClose(y1, y1_inv)
      self.assertAllClose(y2, y2_inv) 

def get_channel_embeddings(io_depth, targets, hidden_size, name="channel"):
  """Get separate embedding for each of the channels."""
  targets_split = tf.split(targets, io_depth, axis=3)
  rgb_embedding_var = tf.get_variable("rgb_target_emb_%s" % name,
                                      [256 * io_depth, hidden_size])
  rgb_embedding_var = tf.identity(rgb_embedding_var)
  rgb_embedding_var *= float(hidden_size)**0.5
  channel_target_embs = []
  for i in range(io_depth):
    # Adding the channel offsets to get the right embedding since the
    # embedding tensor has shape 256 * io_depth, hidden_size
    target_ids = tf.squeeze(targets_split[i], axis=3) + i * 256
    target_embs = common_layers.gather(rgb_embedding_var, target_ids)
    channel_target_embs.append(target_embs)

  return tf.concat(channel_target_embs, axis=-1) 

def _CrossConv(self, encoded_images):
    """Apply the motion kernel on the encoded_images."""
    cross_conved_images = []
    kernels = tf.split(axis=3, num_or_size_splits=4, value=self.kernel)
    for (i, encoded_image) in enumerate(encoded_images):
      with tf.variable_scope('cross_conv_%d' % i):
        kernel = kernels[i]

        encoded_image = tf.unstack(encoded_image, axis=0)
        kernel = tf.unstack(kernel, axis=0)
        assert len(encoded_image) == len(kernel)
        assert len(encoded_image) == self.params['batch_size']
        conved_image = []
        for j in xrange(len(encoded_image)):
          conved_image.append(self._CrossConvHelper(
              encoded_image[j], kernel[j]))
        cross_conved_images.append(tf.concat(axis=0, values=conved_image))
        sys.stderr.write('cross_conved shape: %s\n' %
                         cross_conved_images[-1].get_shape())
    return cross_conved_images 

def subseparable_conv(inputs, filters, kernel_size, **kwargs):
  """Sub-separable convolution. If separability == 0 it's a separable_conv."""

  def conv_fn(inputs, filters, kernel_size, **kwargs):
    """Sub-separable convolution, splits into separability-many blocks."""
    separability = None
    if "separability" in kwargs:
      separability = kwargs.pop("separability")
    if separability:
      parts = []
      abs_sep = separability if separability > 0 else -1 * separability
      for split_idx, split in enumerate(tf.split(inputs, abs_sep, axis=3)):
        with tf.variable_scope("part_%d" % split_idx):
          if separability > 0:
            parts.append(
                tf.layers.conv2d(split, filters // separability, kernel_size,
                                 **kwargs))
          else:
            parts.append(
                tf.layers.separable_conv2d(split, filters // abs_sep,
                                           kernel_size, **kwargs))
      if separability > 1:
        result = tf.layers.conv2d(tf.concat(parts, axis=3), filters, (1, 1))
      elif abs_sep == 1:  # If we have just one block, return it.
        assert len(parts) == 1
        result = parts[0]
      else:
        result = tf.concat(parts, axis=3)
    else:
      result = tf.layers.separable_conv2d(inputs, filters, kernel_size,
                                          **kwargs)
    if separability is not None:
      kwargs["separability"] = separability
    return result

  return conv_internal(conv_fn, inputs, filters, kernel_size, **kwargs) 

def __call__(self, inputs_t, state_t):
    cur_x_times_one_minus_f, cur_f = tf.split(inputs_t, 2, axis=-1)
    state_next = cur_f * state_t + cur_x_times_one_minus_f
    outputs_t = state_next
    return outputs_t, state_next 

def import_module(module_or_obj_name):
    parts = module_or_obj_name.split('.')
    parts[0] = {'np': 'numpy', 'tf': 'tensorflow'}.get(parts[0], parts[0])
    for i in range(len(parts), 0, -1):
        try:
            module = importlib.import_module('.'.join(parts[:i]))
            relative_obj_name = '.'.join(parts[i:])
            return module, relative_obj_name
        except ImportError:
            pass
    raise ImportError(module_or_obj_name)