Python tensorflow.python.framework.tensor_shape.Dimension() Examples

def testEquality(self):
    self.assertTrue(tensor_shape.Dimension(12) == tensor_shape.Dimension(12))
    self.assertFalse(tensor_shape.Dimension(12) == tensor_shape.Dimension(13))
    self.assertIs(None,
                  tensor_shape.Dimension(12) == tensor_shape.Dimension(None))
    self.assertIs(None,
                  tensor_shape.Dimension(None) == tensor_shape.Dimension(12))
    self.assertIs(None,
                  tensor_shape.Dimension(None) == tensor_shape.Dimension(None))
    self.assertTrue(tensor_shape.Dimension(12) == "12")
    self.assertTrue(tensor_shape.Dimension(12) == 24.0 / 2)

    # None indicates ambiguous comparison, but comparison vs the wrong type
    # is unambigously False.
    self.assertIsNotNone(tensor_shape.Dimension(12) == "_")
    self.assertIsNotNone(tensor_shape.Dimension(None) == 12.99)
    self.assertFalse(tensor_shape.Dimension(12) == "_")
    self.assertFalse(tensor_shape.Dimension(None) == 12.99)

    self.assertIs(None, tensor_shape.Dimension(None) == "13")
    self.assertIs(None, tensor_shape.Dimension(None) == None)  # pylint: disable=g-equals-none
    self.assertFalse(tensor_shape.Dimension(12) == 12.99) 

def _dimension_tensor_conversion_function(d,
                                          dtype=None,
                                          name=None,
                                          as_ref=False):
  """Function to convert Dimension to Tensor."""
  _ = as_ref
  if d.value is None:
    raise ValueError("Cannot convert an unknown Dimension to a Tensor: %s" % d)
  if dtype is not None:
    if dtype not in (dtypes.int32, dtypes.int64):
      raise TypeError("Cannot convert a TensorShape to dtype: %s" % dtype)
  else:
    dtype = dtypes.int32
  if name is None:
    name = "shape_as_tensor"
  return constant(d.value, dtype=dtype, name=name) 

def testConcatenate(self):
    tensor_shape.TensorShape([1, 2, 3, 4]).assert_is_compatible_with(
        tensor_shape.TensorShape([1, 2]).concatenate(
            tensor_shape.TensorShape([3, 4])))
    tensor_shape.TensorShape([1, 2, 3, 4]).assert_is_compatible_with(
        tensor_shape.TensorShape([1, 2]).concatenate(
            tensor_shape.TensorShape(None)))
    tensor_shape.TensorShape([1, 2, 3, 4]).assert_is_compatible_with(
        tensor_shape.TensorShape(None).concatenate(
            tensor_shape.TensorShape([3, 4])))
    tensor_shape.TensorShape([1, 2, 3, 4]).assert_is_compatible_with(
        tensor_shape.TensorShape(None).concatenate(
            tensor_shape.TensorShape(None)))
    tensor_shape.TensorShape([1, 2, 3]).assert_is_compatible_with(
        tensor_shape.TensorShape([1, 2]).concatenate(
            tensor_shape.Dimension(3))) 

def testFullyDefinedShape(self):
    s = tensor_shape.TensorShape([tensor_shape.Dimension(
        3), tensor_shape.Dimension(4), tensor_shape.Dimension(7)])
    s.assert_is_fully_defined()
    self.assertEqual(3, s.ndims)
    self.assertEqual(3, len(s))
    self.assertTrue(s)
    s.assert_has_rank(3)
    self.assertEqual([tensor_shape.Dimension(3),
                      tensor_shape.Dimension(4),
                      tensor_shape.Dimension(7)], s.dims)
    self.assertEqual(tensor_shape.Dimension(3), s[0])
    self.assertEqual(tensor_shape.Dimension(4), s[1])
    self.assertEqual(tensor_shape.Dimension(7), s[2])
    self.assertEqual([3, 4, 7], s.as_list())
    s.assert_is_compatible_with([3, 4, 7])
    s.assert_same_rank([6, 3, 7])
    for d1, d2 in zip(s, [3, 4, 7]):
      assert d1.value == d2 

def testUnknownDimension(self):
    dim = tensor_shape.Dimension(None)
    self.assertIs(None, dim.value)
    self.assertEqual(dim.value, tensor_shape.Dimension(None).value)
    self.assertEqual(tensor_shape.Dimension(None).value,
                     (dim + tensor_shape.Dimension(None)).value)
    self.assertEqual(tensor_shape.Dimension(None).value,
                     (dim * tensor_shape.Dimension(None)).value)
    self.assertEqual(
        tensor_shape.Dimension(None).value,
        (dim // tensor_shape.Dimension(None)).value)
    self.assertEqual(tensor_shape.Dimension(None).value,
                     dim.merge_with(tensor_shape.Dimension(None)).value)
    self.assertIs(None,
                  tensor_shape.Dimension(None) < tensor_shape.Dimension(None))
    self.assertIs(None,
                  tensor_shape.Dimension(None) <= tensor_shape.Dimension(None))
    self.assertIs(None,
                  tensor_shape.Dimension(None) > tensor_shape.Dimension(None))
    self.assertIs(None,
                  tensor_shape.Dimension(None) >= tensor_shape.Dimension(None)) 

def testInequality(self):
    self.assertTrue(tensor_shape.Dimension(12) != tensor_shape.Dimension(13))
    self.assertFalse(tensor_shape.Dimension(12) != tensor_shape.Dimension(12))
    self.assertIs(None,
                  tensor_shape.Dimension(12) != tensor_shape.Dimension(None))
    self.assertIs(None,
                  tensor_shape.Dimension(None) != tensor_shape.Dimension(12))
    self.assertIs(None,
                  tensor_shape.Dimension(None) != tensor_shape.Dimension(None))

    # None indicates ambiguous comparison, but comparison vs the wrong type
    # is unambigously False.
    self.assertIsNotNone(tensor_shape.Dimension(12) != "_")
    self.assertIsNotNone(tensor_shape.Dimension(None) != 12.99)
    self.assertTrue(tensor_shape.Dimension(12) != "_")
    self.assertTrue(tensor_shape.Dimension(None) != 12.99)

    self.assertIs(None, tensor_shape.Dimension(None) != "13")
    self.assertIs(None, tensor_shape.Dimension(None) != None)  # pylint: disable=g-equals-none
    self.assertTrue(tensor_shape.Dimension(12) != 12.99) 

def dequeue(self, ctx, queue_ref, component_types=None, name=None):
    if name is None:
      name = tf.get_default_graph().unique_name("Dequeue", False).split("/")[-1]

    ret = gen_data_flow_ops._queue_dequeue_v2(
        queue_ref, component_types=component_types, name=name)

    # NOTE(mrry): Not using a shape function because we need access to
    # the Queue object.
    # op = ret[0].op
    # batch_dim = tensor_shape.Dimension(tensor_util.constant_value(op.inputs[1]))
    # for output, shape in zip(op.values(), shapes):
    #   output.set_shape(tensor_shape.TensorShape([batch_dim]).concatenate(shape))

    return ret 

def output_shapes(self):
    indices_shape = self._sparse_tensor.indices.get_shape()
    shape_shape = self._sparse_tensor.dense_shape.get_shape()
    rank = (indices_shape[1] - 1).merge_with(shape_shape[0] - 1)
    num_values = tensor_shape.Dimension(None)
    return (tensor_shape.TensorShape([num_values, rank]),
            tensor_shape.TensorShape([num_values]), tensor_shape.TensorShape(
                [rank])) 

def padded_batch(self, batch_size, padded_shapes, padding_values=None):
    """Combines consecutive elements of this dataset into padded batches.

    Like `Dataset.dense_to_sparse_batch()`, this method combines
    multiple consecutive elements of this dataset, which might have
    different shapes, into a single element. The tensors in the
    resulting element have an additional outer dimension, and are
    padded to the respective shape in `padded_shapes`.

    Args:
      batch_size: A `tf.int64` scalar `tf.Tensor`, representing the number of
        consecutive elements of this dataset to combine in a single batch.
      padded_shapes: A nested structure of `tf.TensorShape` or
        `tf.int64` vector tensor-like objects representing the shape
        to which the respective component of each input element should
        be padded prior to batching. Any unknown dimensions
        (e.g. `tf.Dimension(None)` in a `tf.TensorShape` or `-1` in a
        tensor-like object) will be padded to the maximum size of that
        dimension in each batch.
      padding_values: (Optional.) A nested structure of scalar-shaped
        `tf.Tensor`, representing the padding values to use for the
        respective components.  Defaults are `0` for numeric types and
        the empty string for string types.

    Returns:
      A `Dataset`.
    """
    return PaddedBatchDataset(self, batch_size, padded_shapes, padding_values) 

def testRepr(self):
    self.assertEqual(repr(tensor_shape.Dimension(7)), "Dimension(7)")
    self.assertEqual(repr(tensor_shape.Dimension(None)), "Dimension(None)") 

def dequeue_many(self, n, name=None):
    """Dequeues and concatenates `n` elements from this queue.

    This operation concatenates queue-element component tensors along
    the 0th dimension to make a single component tensor.  All of the
    components in the dequeued tuple will have size `n` in the 0th dimension.

    If the queue is closed and there are less than `n` elements left, then an
    `OutOfRange` exception is raised.

    At runtime, this operation may raise an error if the queue is
    [closed](#QueueBase.close) before or during its execution. If the
    queue is closed, the queue contains fewer than `n` elements, and
    there are no pending enqueue operations that can fulfill this
    request, `tf.errors.OutOfRangeError` will be raised. If the
    session is [closed](../../api_docs/python/client.md#Session.close),
    `tf.errors.CancelledError` will be raised.

    Args:
      n: A scalar `Tensor` containing the number of elements to dequeue.
      name: A name for the operation (optional).

    Returns:
      The tuple of concatenated tensors that was dequeued.
    """
    if name is None:
      name = "%s_DequeueMany" % self._name

    ret = gen_data_flow_ops._queue_dequeue_many_v2(
        self._queue_ref, n=n, component_types=self._dtypes, name=name)

    # NOTE(mrry): Not using a shape function because we need access to
    # the Queue object.
    op = ret[0].op
    batch_dim = tensor_shape.Dimension(tensor_util.constant_value(op.inputs[1]))
    for output, shape in zip(op.values(), self._shapes):
      output.set_shape(tensor_shape.TensorShape([batch_dim]).concatenate(shape))

    return self._dequeue_return_value(ret) 

def __repr__(self):
    # <LabeledTensor 'foo' shape=(2, 3, 4) dtype=float32
    #  axes=[('x', Dimension(2)),
    #        ('y', ('a', 'b', 'c'),
    #        ('z', Dimension(4))]>
    axes = ["('%s', %r)" % (v.name, v.value) for v in self.axes.values()]
    axes_repr = (',\n' + ' ' * len(' axes=[')).join(axes)
    return ("<%s '%s' shape=%s dtype=%s\n axes=[%s]>" %
            (type(self).__name__, self.tensor.name, self.tensor.get_shape(),
             self.tensor.dtype.name, axes_repr)) 

def setUp(self):
    d_7 = tensor_shape.Dimension(7)
    p_rgb = ['red', 'green', 'blue']

    self.i_7 = core.Axis('7', d_7)
    self.i_7p = core.Axis('7prime', d_7)
    self.i_rgb = core.Axis('rgb', p_rgb)
    self.i_range = core.Axis('range', range(7))
    self.i_unknown = core.Axis('unknown', None) 

def __init__(self, name, value):
    """Construct an Axis.

    Args:
      name: Name of the axis.
      value: Either None, an int or tf.Dimension giving the size of the axis,
        or a sequence that is not a string additionally providing coordinate
        (tick) labels.

    Raises:
      ValueError: If the user provides labels with duplicate values.
    """
    if isinstance(value, tensor_shape.Dimension):
      dimension = value
      labels = None
    elif isinstance(value, int) or value is None:
      dimension = tensor_shape.Dimension(value)
      labels = None
    else:
      dimension = tensor_shape.Dimension(len(value))
      labels = tuple(value)

    if dimension.value == 0:
      # Treat a zero-length axis as if it has labels.
      labels = ()

    if labels is not None:
      index = dict(zip(labels, range(len(labels))))
      if len(index) != len(labels):
        raise ValueError('Tick labels must be unique, but got {}'
                         .format(labels))
    else:
      index = None

    self._name = name  # type: string_types
    self._dimension = dimension  # type: tensor_shape.Dimension
    self._labels = labels  # type: Optional[tuple]
    self._index = index  # type: Optional[Dict[Any, int]] 

def test_repr(self):
    self.assertEqual("Axis('7', Dimension(7))", repr(self.i_7)) 

def setUp(self):
    d_7 = tensor_shape.Dimension(7)
    d_8 = tensor_shape.Dimension(8)
    p_rgb = ['red', 'green', 'blue']
    p_range = range(7)

    self.i_8 = core.Axis('8', d_8)

    self.a0 = core.Axes([('d7', d_7)])
    self.a1 = core.Axes([('d7', d_7)])
    self.a2 = core.Axes([('d7', d_7), ('rgb', p_rgb)])
    self.a3 = core.Axes([('8', d_8), ('range', p_range)]) 

def test_repr(self):
    self.assertEqual("Axes([('d7', Dimension(7))])", repr(self.a0)) 

def test_typecheck_error_message(self):
    pattern = ('List(Union(labeled_tensor.Axis, Tuple(..., '
               'Union(Union(numpy.ndarray, %s, list, tuple), '
               'Optional(Union(tensorflow.Dimension, int))))))' %
               range.__name__)
    regexp = re.escape(pattern).replace(re.escape('...'), '.*')
    with self.assertRaisesRegexp(tc.Error, 'allowed type ' + regexp):
      core.Axes(None) 

def setUp(self):
    tensor = array_ops.ones([7, 3, 8, 1])
    a0 = ('x', range(7))
    a1 = ('channel', ['red', 'green', 'blue'])
    a2 = ('y', 8)
    a3 = ('z', tensor_shape.Dimension(1))

    self.lt = core.LabeledTensor(tensor, [a0, a1, a2, a3]) 

def __repr__(self):
    # Axes([('x', Dimension(2)),
    #       ('y', ['a', 'b', 'c']),
    #       ('z', Dimension(4))])
    cls_name = type(self).__name__
    values = ["('%s', %r)" % (v.name, v.value) for v in self._axes.values()]
    values_repr = (',\n' + ' ' * len(cls_name + '([')).join(values)
    return '%s([%s])' % (cls_name, values_repr) 

def enqueue_many(self, ctx, queue_ref, components, name=None):
    if name is None:
      name = tf.get_default_graph().unique_name("EnqueueMany", False).split("/")[-1]

    ret = gen_data_flow_ops._queue_enqueue_many_v2(
        queue_ref, components=components, name=name)

    # NOTE(mrry): Not using a shape function because we need access to
    # the Queue object.
    # op = ret[0].op
    # batch_dim = tensor_shape.Dimension(tensor_util.constant_value(op.inputs[1]))
    # for output, shape in zip(op.values(), shapes):
    #   output.set_shape(tensor_shape.TensorShape([batch_dim]).concatenate(shape))

    return ret 

def testAttr(self):
    self._add_op("name: 'Attr' attr { name: 'a' type: 'int' }")
    op = self._lib.apply_op("Attr", a=12, name="t")
    self.assertProtoEquals("""
      name: 't' op: 'Attr' attr { key: 'a' value { i: 12 } }
      """, op.node_def)

    op = self._lib.apply_op("Attr", a=tensor_shape.Dimension(13), name="u")
    self.assertProtoEquals("""
      name: 'u' op: 'Attr' attr { key: 'a' value { i: 13 } }
      """, op.node_def)

    with self.assertRaises(TypeError) as cm:
      self._lib.apply_op("Attr", a="bad")
    self.assertEqual(str(cm.exception),
                     "Expected int for argument 'a' not 'bad'.")

    with self.assertRaises(TypeError) as cm:
      self._lib.apply_op("Attr", a=[12])
    self.assertEqual(str(cm.exception),
                     "Expected int for argument 'a' not [12].")

    with self.assertRaises(TypeError) as cm:
      self._lib.apply_op("Attr", a=None)
    self.assertEqual(str(cm.exception),
                     "Expected int for argument 'a' not None.")

    with self.assertRaises(TypeError) as cm:
      self._lib.apply_op("Attr")
    self.assertEqual(str(cm.exception), "No argument for attr a") 

def _dimension_tensor_conversion_function(d, dtype=None, name=None,
                                          as_ref=False):
  _ = as_ref
  if d.value is None:
    raise ValueError("Cannot convert an unknown Dimension to a Tensor: %s" % d)
  if dtype is not None:
    if dtype not in (dtypes.int32, dtypes.int64):
      raise TypeError("Cannot convert a TensorShape to dtype: %s" % dtype)
  else:
    dtype = dtypes.int32
  if name is None:
    name = "shape_as_tensor"
  return constant(d.value, dtype=dtype, name=name) 

def testDivSucceeds(self):
    """Without from __future__ import division, __div__ should work."""
    if six.PY2:  # Old division exists only in Python 2
      values = [tensor_shape.Dimension(x) for x in (3, 7, 11, None)]
      for x in values:
        for y in values:
          self.assertEqual((x / y).value, (x // y).value) 

def testEquality(self):
    s1 = tensor_shape.TensorShape([tensor_shape.Dimension(
        3), tensor_shape.Dimension(4), tensor_shape.Dimension(7)])
    s2 = tensor_shape.TensorShape([tensor_shape.Dimension(
        3), tensor_shape.Dimension(4), tensor_shape.Dimension(7)])
    s3 = tensor_shape.TensorShape([tensor_shape.Dimension(3),
                                   tensor_shape.Dimension(4), None])

    self.assertTrue(s1 == s2)
    self.assertFalse(s1 != s2)
    self.assertFalse(s1 == "a string")
    self.assertTrue(s1 != "a string")
    self.assertNotEqual(s1, "347", "Should not equal an ambiguous string.")
    self.assertEqual(s1, ["3", "4", "7"])

    # Test with an unknown shape in s3
    self.assertTrue(s1 != s3)
    self.assertFalse(s3 == "a string")
    self.assertTrue(s3 != "a string")

    # eq and neq are not symmetric for unknown shapes.
    unk0 = tensor_shape.unknown_shape()
    self.assertFalse(unk0 == s1)
    self.assertFalse(s1 == unk0)
    with self.assertRaises(ValueError):
      unk0 != s1  # pylint: disable=pointless-statement
    with self.assertRaises(ValueError):
      s1 != unk0  # pylint: disable=pointless-statement
    unk1 = tensor_shape.unknown_shape()
    self.assertTrue(unk0 == unk1)
    self.assertTrue(unk1 == unk0)
    with self.assertRaises(ValueError):
      unk0 != unk1  # pylint: disable=pointless-statement
    with self.assertRaises(ValueError):
      unk1 != unk0  # pylint: disable=pointless-statement 

def testTruedivFails(self):
    unknown = tensor_shape.Dimension(None)
    self.assertEqual((unknown // unknown).value, None)
    with self.assertRaisesRegexp(TypeError, r"unsupported operand type"):
      unknown / unknown  # pylint: disable=pointless-statement 

def testSlice(self):
    known = tensor_shape.TensorShape([0, 1, 2, 3, 4])
    self.assertEqual(tensor_shape.Dimension(2), known[2])
    tensor_shape.TensorShape([1, 2, 3]).assert_is_compatible_with(known[1:4])

    unknown = tensor_shape.TensorShape(None)
    self.assertEqual(tensor_shape.Dimension(None).value, unknown[2].value)
    tensor_shape.TensorShape(
        [None, None, None]).assert_is_compatible_with(unknown[1:4]) 

def testMergePartialShapes(self):
    s1 = tensor_shape.TensorShape([tensor_shape.Dimension(
        3), tensor_shape.Dimension(None), tensor_shape.Dimension(7)])
    s2 = tensor_shape.TensorShape([tensor_shape.Dimension(
        None), tensor_shape.Dimension(4), tensor_shape.Dimension(7)])
    self.assertEqual([3, 4, 7], s1.merge_with(s2).as_list()) 

def testStr(self):
    self.assertEqual(str(tensor_shape.Dimension(7)), "7")
    self.assertEqual(str(tensor_shape.Dimension(None)), "?") 

def dequeue_many(self, n, name=None):
    """Dequeues and concatenates `n` elements from this queue.

    This operation concatenates queue-element component tensors along
    the 0th dimension to make a single component tensor.  All of the
    components in the dequeued tuple will have size `n` in the 0th dimension.

    If the queue is closed and there are less than `n` elements left, then an
    `OutOfRange` exception is raised.

    At runtime, this operation may raise an error if the queue is
    @{tf.QueueBase.close} before or during its execution. If the
    queue is closed, the queue contains fewer than `n` elements, and
    there are no pending enqueue operations that can fulfill this
    request, `tf.errors.OutOfRangeError` will be raised. If the
    session is @{tf.Session.close},
    `tf.errors.CancelledError` will be raised.

    Args:
      n: A scalar `Tensor` containing the number of elements to dequeue.
      name: A name for the operation (optional).

    Returns:
      The tuple of concatenated tensors that was dequeued.
    """
    if name is None:
      name = "%s_DequeueMany" % self._name

    ret = gen_data_flow_ops._queue_dequeue_many_v2(
        self._queue_ref, n=n, component_types=self._dtypes, name=name)

    # NOTE(mrry): Not using a shape function because we need access to
    # the Queue object.
    op = ret[0].op
    batch_dim = tensor_shape.Dimension(tensor_util.constant_value(op.inputs[1]))
    for output, shape in zip(op.values(), self._shapes):
      output.set_shape(tensor_shape.TensorShape([batch_dim]).concatenate(shape))

    return self._dequeue_return_value(ret)