def create_function_unit_test(
function_name: str,
unit_test_spec: tf_test_utils.UnitTestSpec) -> tf.function:
"""Creates a tf_function_unit_test from the provided UnitTestSpec."""
function = getattr(tf.math, function_name)
signature = unit_test_spec.input_signature
if tf_utils.is_complex(signature):
function, signature = tf_utils.rewrite_complex_signature(
function, signature)
if function_name == "top_k":
function = _wrap_top_k(function)
wrapped_function = lambda *args: function(*args, **unit_test_spec.kwargs)
if FLAGS.dynamic_dims:
signature = tf_utils.apply_function(signature,
tf_utils.make_dims_dynamic)
return tf_test_utils.tf_function_unit_test(
input_signature=signature,
input_generator=unit_test_spec.input_generator,
input_args=unit_test_spec.input_args,
name=unit_test_spec.unit_test_name,
rtol=1e-5,
atol=1e-5)(wrapped_function)
def create_layer_unit_test(
model: tf.keras.Model,
unit_test_spec: tf_test_utils.UnitTestSpec) -> tf.function:
"""Wrap the model's __call__ function in a tf.function for testing."""
static_signature = unit_test_spec.input_signature
dynamic_signature = static_signature
if FLAGS.dynamic_dims:
dynamic_signature = tf_utils.apply_function(dynamic_signature,
tf_utils.make_dims_dynamic)
if len(static_signature) > 1:
static_signature = [static_signature]
dynamic_signature = [dynamic_signature]
call = lambda *args: model(*args, training=FLAGS.training)
return tf_test_utils.tf_function_unit_test(
input_signature=dynamic_signature,
static_signature=static_signature,
input_generator=unit_test_spec.input_generator,
input_args=unit_test_spec.input_args,
name=unit_test_spec.unit_test_name)(call)
def unit_test_specs_from_args(
names_to_input_args: Dict[str, Sequence[Any]],
kwargs_to_values: Dict[str,
Sequence[Any]] = None) -> List[UnitTestSpec]:
"""Generates a Cartesian product of UnitTestSpecs from the given arguments.
Args:
signature_shapes:
A dict mapping names for input arguments to the arguments themselves.
kwargs_to_values:
A dict mapping kwarg names to sequences of values that they can take.
Returns:
A list of 'UnitTestSpec's generated from the provided arguments.
"""
# Validate and parse 'kwargs_to_values'
names_to_kwargs = _named_kwargs_product(kwargs_to_values)
# Create a Cartesian product through all specifications and their names.
specs = [names_to_input_args, names_to_kwargs]
key_product = itertools.product(*[list(spec.keys()) for spec in specs])
value_product = itertools.product(*[list(spec.values()) for spec in specs])
# Generate a UnitTestSpec for each element in the above product.
unit_tests = []
for keys, (input_args, kwargs) in zip(key_product, value_product):
unit_test_name = "__".join(key for key in keys if key)
input_signature = tf_utils.apply_function(
input_args,
lambda x: tf.TensorSpec.from_tensor(tf.convert_to_tensor(x)))
unit_tests.append(
UnitTestSpec(
unit_test_name=unit_test_name,
input_signature=input_signature,
input_generator=None,
input_args=input_args,
kwargs=kwargs,
))
return unit_tests
def test_apply_function(self):
inputs = [1, [2, 3], (4, 5), {'6': 6, '78': [7, 8]}]
expected = [0, [1, 2], (3, 4), {'6': 5, '78': [6, 7]}]
result = tf_utils.apply_function(inputs, lambda x: x - 1)
self.assertEqual(result, expected)
self.assertNotEqual(inputs, expected)
