def _check_target_program(self, benchmark):
"""Checks that a benchmark's target program is consistent with its examples.
Args:
benchmark: A Benchmark to verify.
"""
self.assertIsNotNone(benchmark.target_program)
for example in benchmark.examples:
# Turn inputs into constant tensors and assign them to variables using a
# new global namespace.
global_namespace = {'tf': tf}
input_names_to_objects = value_search._input_names_to_objects(
example.inputs)
for input_name, input_object in input_names_to_objects.items():
input_value = value_module.InputValue(input_object,
name='dummy_name')
global_namespace[input_name] = input_value.value
# Evaluate the target program, which uses the canonical variables.
target_program_output = eval(benchmark.target_program,
global_namespace) # pylint: disable=eval-used
# Check that the two outputs have equal string representation.
expected_output = tf_coder_utils.convert_to_tensor(example.output)
self.assertEqual(
tf_coder_utils.object_to_string(expected_output),
tf_coder_utils.object_to_string(target_program_output))
def __init__(self, value, name, skip_tensor_conversion=False):
"""Initializes an InputValue to contain `value` with name `name`."""
if (not skip_tensor_conversion
and not isinstance(value, tf_coder_utils.PRIMITIVE_TYPES)):
try:
value = tf_coder_utils.convert_to_tensor(value)
except (TypeError, ValueError):
pass
super(InputValue, self).__init__(value)
self.name = name
def test_convert_to_tensor_for_scalar(self):
scalar = 1.23
tensor = tf_coder_utils.convert_to_tensor(scalar)
self.assertIsInstance(tensor, tf.Tensor)
self.assertAlmostEqual(scalar, tensor.numpy().tolist())
def test_convert_to_tensor_for_list(self):
list_2d = [[1, 2, 3], [44, 55, 66]]
tensor = tf_coder_utils.convert_to_tensor(list_2d)
self.assertIsInstance(tensor, tf.Tensor)
self.assertEqual(list_2d, tensor.numpy().tolist())
def test_convert_to_tensor_for_tensor(self):
original_tensor = tf.constant([1, 2, 3])
tensor = tf_coder_utils.convert_to_tensor(original_tensor)
self.assertIsInstance(tensor, tf.Tensor)
self.assertEqual(original_tensor.numpy().tolist(),
tensor.numpy().tolist())
def __init__(self, value):
"""Initializes an OutputValue to contain `value`."""
value = tf_coder_utils.convert_to_tensor(value)
super(OutputValue, self).__init__(value)