def testIsSequence(self):
self.assertFalse(nest.is_sequence("1234"))
self.assertFalse(nest.is_sequence([1, 3, [4, 5]]))
self.assertTrue(nest.is_sequence(((7, 8), (5, 6))))
self.assertFalse(nest.is_sequence([]))
self.assertFalse(nest.is_sequence(set([1, 2])))
ones = array_ops.ones([2, 3])
self.assertFalse(nest.is_sequence(ones))
self.assertFalse(nest.is_sequence(math_ops.tanh(ones)))
self.assertFalse(nest.is_sequence(np.ones((4, 5))))
self.assertTrue(nest.is_sequence({"foo": 1, "bar": 2}))
self.assertFalse(
nest.is_sequence(sparse_tensor.SparseTensorValue([[0]], [0], [1])))
def testIsSequence(self):
self.assertFalse(nest.is_sequence("1234"))
self.assertFalse(nest.is_sequence([1, 3, [4, 5]]))
self.assertTrue(nest.is_sequence(((7, 8), (5, 6))))
self.assertFalse(nest.is_sequence([]))
self.assertFalse(nest.is_sequence(set([1, 2])))
ones = array_ops.ones([2, 3])
self.assertFalse(nest.is_sequence(ones))
self.assertFalse(nest.is_sequence(math_ops.tanh(ones)))
self.assertFalse(nest.is_sequence(np.ones((4, 5))))
self.assertTrue(nest.is_sequence({"foo": 1, "bar": 2}))
self.assertFalse(
nest.is_sequence(sparse_tensor.SparseTensorValue([[0]], [0], [1])))
def tf_map_func(*args):
"""A wrapper for Defun that facilitates shape inference."""
# Pass in shape information from the input_dataset.
for arg, shape in zip(args, nest.flatten(input_dataset.output_shapes)):
arg.set_shape(shape)
nested_args = nest.pack_sequence_as(input_dataset.output_types, args)
if nest.is_sequence(nested_args):
dataset = map_func(*nested_args)
else:
dataset = map_func(nested_args)
if not isinstance(dataset, dataset_ops.Dataset):
raise TypeError("`map_func` must return a `Dataset` object.")
self._output_types = dataset.output_types
self._output_shapes = dataset.output_shapes
return dataset.make_dataset_resource()
def tf_map_func(*args):
"""A wrapper for Defun that facilitates shape inference."""
# Pass in shape information from the input_dataset.
for arg, shape in zip(args,
nest.flatten(input_dataset.output_shapes)):
arg.set_shape(shape)
nested_args = nest.pack_sequence_as(input_dataset.output_types,
args)
if nest.is_sequence(nested_args):
dataset = map_func(*nested_args)
else:
dataset = map_func(nested_args)
if not isinstance(dataset, dataset_ops.Dataset):
raise TypeError("`map_func` must return a `Dataset` object.")
self._output_types = dataset.output_types
self._output_shapes = dataset.output_shapes
return dataset._as_variant_tensor() # pylint: disable=protected-access
