Python list_ops.tensor_list_from_tensor函数代码示例

 def testAddN(self):
   l1 = list_ops.tensor_list_from_tensor([1.0, 2.0], element_shape=[])
   l2 = list_ops.tensor_list_from_tensor([3.0, 4.0], element_shape=[])
   l3 = list_ops.tensor_list_from_tensor([5.0, 6.0], element_shape=[])
   result = math_ops.add_n((l1, l2, l3))
   result_t = list_ops.tensor_list_stack(result, element_dtype=dtypes.float32)
   self.assertAllEqual(self.evaluate(result_t), [9., 12.])

  def testConcat(self):
    c = constant_op.constant([1.0, 2.0], dtype=dtypes.float32)
    l0 = list_ops.tensor_list_from_tensor(c, element_shape=scalar_shape())
    l1 = list_ops.tensor_list_from_tensor([-1.0], element_shape=scalar_shape())
    l_batch_0 = array_ops.stack([l0, l1])
    l_batch_1 = array_ops.stack([l1, l0])

    l_concat_01 = list_ops.tensor_list_concat_lists(
        l_batch_0, l_batch_1, element_dtype=dtypes.float32)
    l_concat_10 = list_ops.tensor_list_concat_lists(
        l_batch_1, l_batch_0, element_dtype=dtypes.float32)
    l_concat_00 = list_ops.tensor_list_concat_lists(
        l_batch_0, l_batch_0, element_dtype=dtypes.float32)
    l_concat_11 = list_ops.tensor_list_concat_lists(
        l_batch_1, l_batch_1, element_dtype=dtypes.float32)

    expected_00 = [[1.0, 2.0, 1.0, 2.0], [-1.0, -1.0]]
    expected_01 = [[1.0, 2.0, -1.0], [-1.0, 1.0, 2.0]]
    expected_10 = [[-1.0, 1.0, 2.0], [1.0, 2.0, -1.0]]
    expected_11 = [[-1.0, -1.0], [1.0, 2.0, 1.0, 2.0]]

    for i, (concat, expected) in enumerate(zip(
        [l_concat_00, l_concat_01, l_concat_10, l_concat_11],
        [expected_00, expected_01, expected_10, expected_11])):
      splitted = array_ops.unstack(concat)
      splitted_stacked_ret = self.evaluate(
          (list_ops.tensor_list_stack(splitted[0], dtypes.float32),
           list_ops.tensor_list_stack(splitted[1], dtypes.float32)))
      print("Test concat %d: %s, %s, %s, %s"
            % (i, expected[0], splitted_stacked_ret[0],
               expected[1], splitted_stacked_ret[1]))
      self.assertAllClose(expected[0], splitted_stacked_ret[0])
      self.assertAllClose(expected[1], splitted_stacked_ret[1])

    # Concatenating mismatched shapes fails.
    with self.assertRaises((errors.InvalidArgumentError, ValueError)):
      self.evaluate(
          list_ops.tensor_list_concat_lists(
              l_batch_0,
              list_ops.empty_tensor_list(scalar_shape(), dtypes.float32),
              element_dtype=dtypes.float32))

    with self.assertRaisesRegexp(errors.InvalidArgumentError,
                                 "element shapes are not identical at index 0"):
      l_batch_of_vec_tls = array_ops.stack(
          [list_ops.tensor_list_from_tensor([[1.0]], element_shape=[1])] * 2)
      self.evaluate(
          list_ops.tensor_list_concat_lists(l_batch_0, l_batch_of_vec_tls,
                                            element_dtype=dtypes.float32))

    with self.assertRaisesRegexp(errors.InvalidArgumentError,
                                 r"input_b\[0\].dtype != element_dtype."):
      l_batch_of_int_tls = array_ops.stack(
          [list_ops.tensor_list_from_tensor([1], element_shape=scalar_shape())]
          * 2)
      self.evaluate(
          list_ops.tensor_list_concat_lists(l_batch_0, l_batch_of_int_tls,
                                            element_dtype=dtypes.float32))

  def testSerialize(self):
    # pylint: disable=g-import-not-at-top
    try:
      import portpicker
    except ImportError:
      return
    with context.graph_mode():
      worker_port = portpicker.pick_unused_port()
      ps_port = portpicker.pick_unused_port()
      cluster_dict = {
          "worker": ["localhost:%s" % worker_port],
          "ps": ["localhost:%s" % ps_port]
      }
      cs = server_lib.ClusterSpec(cluster_dict)

      worker = server_lib.Server(
          cs, job_name="worker", protocol="grpc", task_index=0, start=True)
      unused_ps = server_lib.Server(
          cs, job_name="ps", protocol="grpc", task_index=0, start=True)
      with ops.Graph().as_default(), session.Session(target=worker.target):
        with ops.device("/job:worker"):
          t = constant_op.constant([[1.0], [2.0]])
          l = list_ops.tensor_list_from_tensor(t, element_shape=[1])
        with ops.device("/job:ps"):
          l_ps = array_ops.identity(l)
          l_ps, e = list_ops.tensor_list_pop_back(
              l_ps, element_dtype=dtypes.float32)
        with ops.device("/job:worker"):
          worker_e = array_ops.identity(e)
        self.assertAllEqual(worker_e.eval(), [2.0])

 def testTensorListFromTensor(self):
   t = constant_op.constant([1.0, 2.0])
   l = list_ops.tensor_list_from_tensor(t, element_shape=[])
   l, e = list_ops.tensor_list_pop_back(l, element_dtype=dtypes.float32)
   self.assertAllEqual(self.evaluate(e), 2.0)
   l, e = list_ops.tensor_list_pop_back(l, element_dtype=dtypes.float32)
   self.assertAllEqual(self.evaluate(e), 1.0)
   self.assertAllEqual(self.evaluate(list_ops.tensor_list_length(l)), 0)

 def testGetSetItem(self):
   t = constant_op.constant([1.0, 2.0])
   l = list_ops.tensor_list_from_tensor(t, element_shape=[])
   e0 = list_ops.tensor_list_get_item(l, 0, element_dtype=dtypes.float32)
   self.assertAllEqual(self.evaluate(e0), 1.0)
   l = list_ops.tensor_list_set_item(l, 0, 3.0)
   t = list_ops.tensor_list_stack(l, element_dtype=dtypes.float32)
   self.assertAllEqual(self.evaluate(t), [3.0, 2.0])

  def test_get_item_tensor_list(self):
    initial_list = constant_op.constant([[1, 2], [3, 4]])
    elem_shape = constant_op.constant([2])
    l = list_ops.tensor_list_from_tensor(initial_list, element_shape=elem_shape)
    t = slices.get_item(
        l, 1, slices.GetItemOpts(element_dtype=initial_list.dtype))

    with self.cached_session() as sess:
      self.assertAllEqual(self.evaluate(t), [3, 4])

def tf_tensor_list_new(elements, element_dtype=None, element_shape=None):
  """Overload of new_list that stages a Tensor list creation."""
  if tensor_util.is_tensor(elements):
    if element_shape is not None:
      raise ValueError(
          'element shape may not be specified when creating list from tensor')
    element_shape = array_ops.shape(elements)[1:]
    l = list_ops.tensor_list_from_tensor(elements, element_shape=element_shape)
    return l

  elements = tuple(ops.convert_to_tensor(el) for el in elements)

  all_dtypes = set(el.dtype for el in elements)
  if len(all_dtypes) == 1:
    inferred_dtype = tuple(all_dtypes)[0]
    if element_dtype is not None and element_dtype != inferred_dtype:
      raise ValueError(
          'incompatible dtype; specified: {}, inferred from {}: {}'.format(
              element_dtype, elements, inferred_dtype))
  elif all_dtypes:
    # Heterogeneous lists are ok.
    if element_dtype is not None:
      raise ValueError(
          'specified dtype {} is inconsistent with that of elements {}'.format(
              element_dtype, elements))
    inferred_dtype = dtypes.variant
  else:
    inferred_dtype = dtypes.variant

  all_shapes = set(tuple(el.shape.as_list()) for el in elements)
  if len(all_shapes) == 1:
    inferred_shape = array_ops.shape(elements[0])
    if element_shape is not None and element_shape != inferred_shape:
      raise ValueError(
          'incompatible shape; specified: {}, inferred from {}: {}'.format(
              element_shape, elements, inferred_shape))
  elif all_shapes:
    # Heterogeneous lists are ok.
    if element_shape is not None:
      raise ValueError(
          'specified shape {} is inconsistent with that of elements {}'.format(
              element_shape, elements))
    inferred_shape = constant_op.constant(-1)  # unknown shape, by convention
  else:
    inferred_shape = constant_op.constant(-1)  # unknown shape, by convention

  if element_dtype is None:
    element_dtype = inferred_dtype
  if element_shape is None:
    element_shape = inferred_shape

  element_shape = ops.convert_to_tensor(element_shape, dtype=dtypes.int32)
  l = list_ops.empty_tensor_list(
      element_shape=element_shape, element_dtype=element_dtype)
  for el in elements:
    l = list_ops.tensor_list_push_back(l, el)
  return l

  def test_set_item_tensor_list(self):
    initial_list = constant_op.constant([[1, 2], [3, 4]])
    elem_shape = constant_op.constant([2])
    l = list_ops.tensor_list_from_tensor(initial_list, element_shape=elem_shape)
    l = slices.set_item(l, 0, [5, 6])

    with self.cached_session() as sess:
      t = list_ops.tensor_list_stack(l, element_dtype=initial_list.dtype)
      self.assertAllEqual(self.evaluate(t), [[5, 6], [3, 4]])

 def testGetSet(self):
   with self.cached_session(), self.test_scope():
     t = constant_op.constant([1.0, 2.0])
     l = list_ops.tensor_list_from_tensor(t, element_shape=[])
     e0 = list_ops.tensor_list_get_item(l, 0, element_dtype=dtypes.float32)
     self.assertAllEqual(e0, 1.0)
     l = list_ops.tensor_list_set_item(l, 0, 3.0)
     t = list_ops.tensor_list_stack(l, element_dtype=dtypes.float32)
     self.assertAllEqual(t, [3.0, 2.0])

 def testStackFromTensorGradients(self):
   with backprop.GradientTape() as tape:
     c = constant_op.constant([1.0, 2.0])
     tape.watch(c)
     l = list_ops.tensor_list_from_tensor(c, element_shape=scalar_shape())
     c2 = list_ops.tensor_list_stack(
         l, element_dtype=dtypes.float32)
     result = c2 * 2.0
   self.assertAllEqual(tape.gradient(result, [c])[0], [2.0, 2.0])

 def unstack(self, value, name=None):
   """See TensorArray."""
   with ops.name_scope(name, "TensorArrayUnstack", [self._flow, value]):
     value = ops.convert_to_tensor(value, name="value")
     if self._infer_shape and not context.executing_eagerly():
       self._merge_element_shape(value.shape[1:])
     flow_out = list_ops.tensor_list_from_tensor(
         tensor=value, element_shape=value.shape[1:])
     return build_ta_with_new_flow(self, flow_out)

 def testStackFromTensorGradients(self):
   with backprop.GradientTape() as tape:
     c = constant_op.constant([1.0, 2.0])
     tape.watch(c)
     l = list_ops.tensor_list_from_tensor(c, element_shape=[])
     c2 = list_ops.tensor_list_stack(
         l, element_dtype=dtypes.float32, num_elements=2)
     result = c2 * 2.0
   grad = tape.gradient(result, [c])[0]
   self.assertAllEqual(self.evaluate(grad), [2.0, 2.0])

  def test_stack_tensor_list(self):
    initial_list = constant_op.constant([[1, 2], [3, 4]])
    elem_shape = constant_op.constant([2])
    l = list_ops.tensor_list_from_tensor(initial_list, element_shape=elem_shape)

    opts = data_structures.ListStackOpts(
        element_dtype=initial_list.dtype, original_call=None)

    with self.test_session() as sess:
      t = data_structures.list_stack(l, opts)
      self.assertAllEqual(sess.run(t), sess.run(initial_list))

 def testListFromTensor(self):
   with self.cached_session(), self.test_scope():
     t = constant_op.constant([1.0, 2.0])
     l = list_ops.tensor_list_from_tensor(t, element_shape=[])
     e = list_ops.tensor_list_get_item(l, 0, element_dtype=dtypes.float32)
     self.assertAllEqual(e, 1.0)
     l, e0 = list_ops.tensor_list_pop_back(l, element_dtype=dtypes.float32)
     self.assertAllEqual(e0, 2.0)
     l, e1 = list_ops.tensor_list_pop_back(l, element_dtype=dtypes.float32)
     self.assertAllEqual(e1, 1.0)
     self.assertAllEqual(list_ops.tensor_list_length(l), 0)

 def testResourceVariableScatterGather(self):
   c = constant_op.constant([1.0, 2.0], dtype=dtypes.float32)
   l = list_ops.tensor_list_from_tensor(c, element_shape=[])
   v = vs.get_variable("var", initializer=[l] * 10, use_resource=True)
   v_r_0_stacked = list_ops.tensor_list_stack(v[0], dtypes.float32)
   self.evaluate(v.initializer)
   self.assertAllEqual([1.0, 2.0], self.evaluate(v_r_0_stacked))
   v_r_sparse_stacked = list_ops.tensor_list_stack(
       v.sparse_read(0), dtypes.float32)
   self.assertAllEqual([1.0, 2.0], self.evaluate(v_r_sparse_stacked))
   l_new_0 = list_ops.tensor_list_from_tensor([3.0, 4.0], element_shape=[])
   l_new_1 = list_ops.tensor_list_from_tensor([5.0, 6.0], element_shape=[])
   updated_v = state_ops.scatter_update(v, [3, 5], [l_new_0, l_new_1])
   updated_v_elems = array_ops.unstack(updated_v)
   updated_v_stacked = [
       list_ops.tensor_list_stack(el, dtypes.float32) for el in updated_v_elems
   ]
   expected = ([[1.0, 2.0]] * 3 + [[3.0, 4.0], [1.0, 2.0], [5.0, 6.0]] +
               [[1.0, 2.0]] * 4)
   self.assertAllEqual(self.evaluate(updated_v_stacked), expected)