def _apply_fn(dataset):
output_shapes = _merge_output_shapes(
dataset_ops.get_legacy_output_shapes(dataset), expected_shapes)
# pylint: disable=protected-access
return batching._RestructuredDataset(
dataset.map(_check_shape),
dataset_ops.get_legacy_output_types(dataset),
output_shapes=output_shapes,
output_classes=dataset_ops.get_legacy_output_classes(dataset))
def _apply_fn(dataset):
output_shapes = _merge_output_shapes(
dataset_ops.get_legacy_output_shapes(dataset), expected_shapes)
# pylint: disable=protected-access
return batching._RestructuredDataset(
dataset.map(_check_shape),
dataset_ops.get_legacy_output_types(dataset),
output_shapes=output_shapes,
output_classes=dataset_ops.get_legacy_output_classes(dataset))
def testRestructureDataset(self):
components = (array_ops.placeholder(dtypes.int32),
(array_ops.placeholder(dtypes.int32, shape=[None]),
array_ops.placeholder(dtypes.int32, shape=[20, 30])))
dataset = dataset_ops.Dataset.from_tensors(components)
i32 = dtypes.int32
test_cases = [((i32, i32, i32), None), (((i32, i32), i32), None),
((i32, i32, i32), (None, None, None)),
((i32, i32, i32), ([17], [17], [20, 30]))]
for new_types, new_shape_lists in test_cases:
# pylint: disable=protected-access
new = batching._RestructuredDataset(dataset, new_types,
new_shape_lists)
# pylint: enable=protected-access
self.assertEqual(new_types,
dataset_ops.get_legacy_output_types(new))
if new_shape_lists is not None:
for expected_shape_list, shape in zip(
nest.flatten(new_shape_lists),
nest.flatten(
dataset_ops.get_legacy_output_shapes(new))):
if expected_shape_list is None:
self.assertIs(None, shape.ndims)
else:
self.assertEqual(expected_shape_list, shape.as_list())
fail_cases = [((i32, dtypes.int64, i32), None),
((i32, i32, i32, i32), None),
((i32, i32, i32), ((None, None), None)),
((i32, i32, i32), (None, None, None, None)),
((i32, i32, i32), (None, [None], [21, 30]))]
for new_types, new_shape_lists in fail_cases:
with self.assertRaises(ValueError):
# pylint: disable=protected-access
new = batching._RestructuredDataset(dataset, new_types,
new_shape_lists)
def testRestructureDataset(self):
components = (array_ops.placeholder(dtypes.int32),
(array_ops.placeholder(dtypes.int32, shape=[None]),
array_ops.placeholder(dtypes.int32, shape=[20, 30])))
dataset = dataset_ops.Dataset.from_tensors(components)
i32 = dtypes.int32
test_cases = [((i32, i32, i32), None),
(((i32, i32), i32), None),
((i32, i32, i32), (None, None, None)),
((i32, i32, i32), ([17], [17], [20, 30]))]
for new_types, new_shape_lists in test_cases:
# pylint: disable=protected-access
new = batching._RestructuredDataset(dataset, new_types, new_shape_lists)
# pylint: enable=protected-access
self.assertEqual(new_types, dataset_ops.get_legacy_output_types(new))
if new_shape_lists is not None:
for expected_shape_list, shape in zip(
nest.flatten(new_shape_lists),
nest.flatten(dataset_ops.get_legacy_output_shapes(new))):
if expected_shape_list is None:
self.assertIs(None, shape.ndims)
else:
self.assertEqual(expected_shape_list, shape.as_list())
fail_cases = [((i32, dtypes.int64, i32), None),
((i32, i32, i32, i32), None),
((i32, i32, i32), ((None, None), None)),
((i32, i32, i32), (None, None, None, None)),
((i32, i32, i32), (None, [None], [21, 30]))]
for new_types, new_shape_lists in fail_cases:
with self.assertRaises(ValueError):
# pylint: disable=protected-access
new = batching._RestructuredDataset(dataset, new_types, new_shape_lists)