def _test(self, kwargs, expected_values=None, expected_err=None):
with self.test_session() as sess:
if expected_err:
with self.assertRaisesWithPredicateMatch(
expected_err[0], expected_err[1]):
out = parsing_ops.parse_single_example_v2(**kwargs)
sess.run(flatten_values_tensors_or_sparse(out.values()))
else:
# Returns dict w/ Tensors and SparseTensors.
out = parsing_ops.parse_single_example_v2(**kwargs)
# Check values.
tf_result = sess.run(
flatten_values_tensors_or_sparse(out.values()))
_compare_output_to_expected(self, out, expected_values,
tf_result)
# Check shapes.
for k, f in kwargs["features"].items():
if isinstance(
f,
parsing_ops.FixedLenFeature) and f.shape is not None:
self.assertEqual(tuple(out[k].get_shape()),
tensor_shape.as_shape(f.shape))
elif isinstance(f, parsing_ops.VarLenFeature):
self.assertEqual(
tuple(out[k].indices.get_shape().as_list()), (None, 1))
self.assertEqual(
tuple(out[k].values.get_shape().as_list()), (None, ))
self.assertEqual(
tuple(out[k].dense_shape.get_shape().as_list()), (1, ))
def _test(self, kwargs, expected_values=None, expected_err=None):
with self.test_session() as sess:
if expected_err:
with self.assertRaisesWithPredicateMatch(expected_err[0],
expected_err[1]):
out = parsing_ops.parse_single_example_v2(**kwargs)
sess.run(flatten_values_tensors_or_sparse(out.values()))
return
else:
# Returns dict w/ Tensors and SparseTensors.
out = parsing_ops.parse_single_example_v2(**kwargs)
result = flatten_values_tensors_or_sparse(out.values())
# Check values.
tf_result = sess.run(result)
_compare_output_to_expected(self, out, expected_values, tf_result)
for k, f in kwargs["features"].items():
if isinstance(f, parsing_ops.FixedLenFeature) and f.shape is not None:
self.assertEqual(tuple(out[k].get_shape().as_list()), f.shape)
elif isinstance(f, parsing_ops.VarLenFeature):
self.assertEqual(
tuple(out[k].indices.get_shape().as_list()), (None, 1))
self.assertEqual(tuple(out[k].values.get_shape().as_list()), (None,))
self.assertEqual(
tuple(out[k].dense_shape.get_shape().as_list()), (1,))