def enable_v2_behavior():
"""Enables TensorFlow 2.x behaviors.
This function can be called at the beginning of the program (before `Tensors`,
`Graphs` or other structures have been created, and before devices have been
initialized. It switches all global behaviors that are different between
TensorFlow 1.x and 2.x to behave as intended for 2.x.
This function is called in the main TensorFlow `__init__.py` file, user should
not need to call it, except during complex migrations.
"""
# TF2 behavior is enabled if either 1) enable_v2_behavior() is called or
# 2) the TF2_BEHAVIOR=1 environment variable is set. In the latter case,
# the modules below independently check if tf2.enabled().
tf2.enable()
ops.enable_eager_execution()
tensor_shape.enable_v2_tensorshape() # Also switched by tf2
variable_scope.enable_resource_variables()
ops.enable_tensor_equality()
# Enables TensorArrayV2 and control flow V2.
control_flow_v2_toggles.enable_control_flow_v2()
# Make sure internal uses of tf.data symbols map to V2 versions.
dataset_ops.Dataset = dataset_ops.DatasetV2
readers.FixedLengthRecordDataset = readers.FixedLengthRecordDatasetV2
readers.TFRecordDataset = readers.TFRecordDatasetV2
readers.TextLineDataset = readers.TextLineDatasetV2
counter.Counter = counter.CounterV2
interleave_ops.choose_from_datasets = interleave_ops.choose_from_datasets_v2
interleave_ops.sample_from_datasets = interleave_ops.sample_from_datasets_v2
random_ops.RandomDataset = random_ops.RandomDatasetV2
exp_readers.CsvDataset = exp_readers.CsvDatasetV2
exp_readers.SqlDataset = exp_readers.SqlDatasetV2
exp_readers.make_batched_features_dataset = (
exp_readers.make_batched_features_dataset_v2)
exp_readers.make_csv_dataset = exp_readers.make_csv_dataset_v2
def testEqualityBroadcast(self):
default = ops.Tensor._USE_EQUALITY
try:
tf_a = constant_op.constant([1, 1])
tf_b = constant_op.constant([1, 1])
tf_c = constant_op.constant([[1, 1], [1, 1]])
tf_d = constant_op.constant([[1, 2], [1, 2]])
tf_e = constant_op.constant([1, 1, 1])
np_a = np.array([1, 1])
np_b = np.array([1, 1])
np_c = np.array([[1, 1], [1, 1]])
np_d = np.array([[1, 2], [1, 2]])
np_e = np.array([1, 1, 1])
ops.disable_tensor_equality()
# We don't do element-wise comparison
self.assertNotEqual(tf_a, tf_b)
self.assertNotEqual(tf_a, tf_c)
self.assertNotEqual(tf_a, tf_d)
ops.enable_tensor_equality()
# We do element-wise comparison but can't convert results array to bool
with self.assertRaises(ValueError):
bool(tf_a == tf_b)
self.assertAllEqual(tf_a == tf_b, [True, True])
with self.assertRaises(ValueError):
bool(tf_a == tf_c)
self.assertAllEqual(tf_a == tf_c, [[True, True], [True, True]])
with self.assertRaises(ValueError):
bool(tf_a == tf_d)
self.assertAllEqual(tf_a == tf_d, [[True, False], [True, False]])
if compat.forward_compatible(2019, 9, 25):
self.assertFalse(bool(tf_a == tf_e))
self.assertTrue(bool(tf_a != tf_e))
self.assertNotAllEqual(tf_a, tf_e)
else:
with self.assertRaises(errors.InvalidArgumentError):
bool(tf_a != tf_e)
with self.assertRaises(ValueError):
bool(np_a == np_b)
self.assertAllEqual(np_a == np_b, [True, True])
with self.assertRaises(ValueError):
bool(np_a == np_c)
self.assertAllEqual(np_a == np_c, [[True, True], [True, True]])
self.assertAllEqual(np_a == np_d, [[True, False], [True, False]])
self.assertFalse(bool(np_a == np_e))
self.assertTrue(bool(np_a != np_e))
self.assertNotAllEqual(np_a, np_e)
finally:
if default:
ops.enable_tensor_equality()
else:
ops.disable_tensor_equality()
def testEqualityBroadcast(self):
default = ops.Tensor._USE_EQUALITY
try:
tf_a = constant_op.constant([1, 1])
tf_b = constant_op.constant([1, 1])
tf_c = constant_op.constant([[1, 1], [1, 1]])
tf_d = constant_op.constant([[1, 2], [1, 2]])
tf_e = constant_op.constant([1, 1, 1])
np_a = np.array([1, 1])
np_b = np.array([1, 1])
np_c = np.array([[1, 1], [1, 1]])
np_d = np.array([[1, 2], [1, 2]])
np_e = np.array([1, 1, 1])
ops.disable_tensor_equality()
# We don't do element-wise comparison
self.assertNotEqual(tf_a, tf_b)
self.assertNotEqual(tf_a, tf_c)
self.assertNotEqual(tf_a, tf_d)
ops.enable_tensor_equality()
# We do element-wise comparison but can't convert results array to bool
with self.assertRaises(ValueError):
bool(tf_a == tf_b)
self.assertAllEqual(tf_a == tf_b, [True, True])
with self.assertRaises(ValueError):
bool(tf_a == tf_c)
self.assertAllEqual(tf_a == tf_c, [[True, True], [True, True]])
with self.assertRaises(ValueError):
bool(tf_a == tf_d)
self.assertAllEqual(tf_a == tf_d, [[True, False], [True, False]])
# TODO(b/207402791): re-enable once incompatible shapes supported by XLA.
if not test_util.is_xla_enabled():
self.assertFalse(bool(tf_a == tf_e))
self.assertTrue(bool(tf_a != tf_e))
self.assertNotAllEqual(tf_a, tf_e)
with self.assertRaises(ValueError):
bool(np_a == np_b)
self.assertAllEqual(np_a == np_b, [True, True])
with self.assertRaises(ValueError):
bool(np_a == np_c)
self.assertAllEqual(np_a == np_c, [[True, True], [True, True]])
self.assertAllEqual(np_a == np_d, [[True, False], [True, False]])
self.assertFalse(bool(np_a == np_e))
self.assertTrue(bool(np_a != np_e))
self.assertNotAllEqual(np_a, np_e)
finally:
if default:
ops.enable_tensor_equality()
else:
ops.disable_tensor_equality()
def testEqualityBroadcast(self):
default = ops.Tensor._USE_EQUALITY
try:
tf_a = constant_op.constant([1, 1])
tf_b = constant_op.constant([1, 1])
tf_c = constant_op.constant([[1, 1], [1, 1]])
tf_d = constant_op.constant([[1, 2], [1, 2]])
tf_e = constant_op.constant([1, 1, 1])
np_a = np.array([1, 1])
np_b = np.array([1, 1])
np_c = np.array([[1, 1], [1, 1]])
np_d = np.array([[1, 2], [1, 2]])
np_e = np.array([1, 1, 1])
ops.disable_tensor_equality()
# We don't do element-wise comparison
self.assertNotEqual(tf_a, tf_b)
self.assertNotEqual(tf_a, tf_c)
self.assertNotEqual(tf_a, tf_d)
ops.enable_tensor_equality()
# We do element-wise comparison but can't convert results array to bool
with self.assertRaises(ValueError):
bool(tf_a == tf_b)
self.assertAllEqual(tf_a == tf_b, [True, True])
with self.assertRaises(ValueError):
bool(tf_a == tf_c)
self.assertAllEqual(tf_a == tf_c, [[True, True], [True, True]])
with self.assertRaises(ValueError):
bool(tf_a == tf_d)
self.assertAllEqual(tf_a == tf_d, [[True, False], [True, False]])
# TODO(b/120678848): If shapes do not match we should instead return False
with self.assertRaises(errors.InvalidArgumentError):
bool(tf_a != tf_e)
with self.assertRaises(ValueError):
bool(np_a == np_b)
self.assertAllEqual(np_a == np_b, [True, True])
with self.assertRaises(ValueError):
bool(np_a == np_c)
self.assertAllEqual(np_a == np_c, [[True, True], [True, True]])
self.assertAllEqual(np_a == np_d, [[True, False], [True, False]])
bool(np_a != np_e)
finally:
if default:
ops.enable_tensor_equality()
else:
ops.disable_tensor_equality()
def test_deprecated_arg_values_when_value_is_none(self, mock_warning):
@deprecation.deprecated_arg_values("2016-07-04",
"This is how you update...",
warn_once=True,
arg0=None)
def _fn(arg0): # pylint: disable=unused-argument
pass
ops.enable_tensor_equality()
initial_count = mock_warning.call_count
# Check that we avoid error from explicit `var == None` check.
_fn(arg0=variables.Variable(0))
self.assertEqual(initial_count, mock_warning.call_count)
_fn(arg0=None)
self.assertEqual(initial_count + 1, mock_warning.call_count)
ops.disable_tensor_equality()
def enable_v2_behavior():
"""Enables TensorFlow 2.x behaviors.
This function can be called at the beginning of the program (before `Tensors`,
`Graphs` or other structures have been created, and before devices have been
initialized. It switches all global behaviors that are different between
TensorFlow 1.x and 2.x to behave as intended for 2.x.
This function is called in the main TensorFlow `__init__.py` file, user should
not need to call it, except during complex migrations.
"""
tf2.enable()
ops.enable_eager_execution()
tensor_shape.enable_v2_tensorshape() # Also switched by tf2
variable_scope.enable_resource_variables()
ops.enable_tensor_equality()
# Enables TensorArrayV2 and control flow V2.
control_flow_v2_toggles.enable_control_flow_v2()
def testV2BehaviorLogging(self):
with self.assertLogs(level='INFO') as logs:
try:
ops.enable_eager_execution()
# Ignore this exception to test log output successfully
except ValueError as e:
if 'must be called at program startup' not in str(e):
raise e
self.assertIn('Enabling eager execution', ''.join(logs.output))
with self.assertLogs(level='INFO') as logs:
ops.disable_eager_execution()
self.assertIn('Disabling eager execution', ''.join(logs.output))
with self.assertLogs(level='INFO') as logs:
tensor_shape.enable_v2_tensorshape()
self.assertIn('Enabling v2 tensorshape', ''.join(logs.output))
with self.assertLogs(level='INFO') as logs:
tensor_shape.disable_v2_tensorshape()
self.assertIn('Disabling v2 tensorshape', ''.join(logs.output))
with self.assertLogs(level='INFO') as logs:
variable_scope.enable_resource_variables()
self.assertIn('Enabling resource variables', ''.join(logs.output))
with self.assertLogs(level='INFO') as logs:
variable_scope.disable_resource_variables()
self.assertIn('Disabling resource variables', ''.join(logs.output))
with self.assertLogs(level='INFO') as logs:
ops.enable_tensor_equality()
self.assertIn('Enabling tensor equality', ''.join(logs.output))
with self.assertLogs(level='INFO') as logs:
ops.disable_tensor_equality()
self.assertIn('Disabling tensor equality', ''.join(logs.output))
with self.assertLogs(level='INFO') as logs:
control_flow_v2_toggles.enable_control_flow_v2()
self.assertIn('Enabling control flow v2', ''.join(logs.output))
with self.assertLogs(level='INFO') as logs:
control_flow_v2_toggles.disable_control_flow_v2()
self.assertIn('Disabling control flow v2', ''.join(logs.output))
def enable_v2_behavior():
"""Enables TensorFlow 2.x behaviors.
This function can be called at the beginning of the program (before `Tensors`,
`Graphs` or other structures have been created, and before devices have been
initialized. It switches all global behaviors that are different between
TensorFlow 1.x and 2.x to behave as intended for 2.x.
This function is called in the main TensorFlow `__init__.py` file, user should
not need to call it, except during complex migrations.
"""
# TF2 behavior is enabled if either 1) enable_v2_behavior() is called or
# 2) the TF2_BEHAVIOR=1 environment variable is set. In the latter case,
# the modules below independently check if tf2.enabled().
tf2.enable()
ops.enable_eager_execution()
tensor_shape.enable_v2_tensorshape() # Also switched by tf2
variable_scope.enable_resource_variables()
ops.enable_tensor_equality()
# Enables TensorArrayV2 and control flow V2.
control_flow_v2_toggles.enable_control_flow_v2()
# Make sure internal uses of the `dataset_ops.Dataset` map to DatasetV2.
dataset_ops.Dataset = dataset_ops.DatasetV2
def testEquality(self):
default = ops.Tensor._USE_EQUALITY
try:
def _v1_check(a, b):
self.assertEqual(a, a)
self.assertIs(a, a)
self.assertNotEqual(a, 1.0)
self.assertIsNot(a, 1.0)
self.assertNotEqual(a, b)
self.assertIsNot(a, b)
def _v2_check(a, b):
self.assertEqual(a, a)
self.assertIs(a, a)
self.assertEqual(a, 1.0)
self.assertIsNot(a, 1.0)
self.assertEqual(a, b)
self.assertIsNot(a, b)
constant_a = constant_op.constant(1.0)
constant_b = constant_op.constant(1.0)
ops.disable_tensor_equality()
self._test_hashable(constant_a, constant_b, True)
_v1_check(constant_a, constant_b)
ops.enable_tensor_equality()
_v2_check(constant_a, constant_b)
self._test_hashable(constant_a, constant_b, False)
variable_a = variables.Variable(1.0)
variable_b = variables.Variable(1.0)
ops.disable_tensor_equality()
_v1_check(variable_a, variable_b)
self._test_hashable(variable_a, variable_b, True)
ops.enable_tensor_equality()
_v2_check(variable_a, variable_b)
self._test_hashable(variable_a, variable_b, False)
# We only test numpy behaviour in v2 mode since we'd like to match that.
numpy_a = np.array(1.0)
numpy_b = np.array(1.0)
_v2_check(numpy_a, numpy_b)
self._test_hashable(numpy_a, numpy_b, False)
finally:
if default:
ops.enable_tensor_equality()
else:
ops.disable_tensor_equality()
def testEqualityNan(self):
default = ops.Tensor._USE_EQUALITY
try:
def _v1_check(a, b):
self.assertEqual(a, a)
self.assertIs(a, a)
self.assertNotEqual(a, float('nan'))
self.assertIsNot(a, float('nan'))
self.assertNotEqual(a, b)
self.assertIsNot(a, b)
def _v2_check(a, b):
self.assertNotEqual(a, a)
self.assertIs(a, a)
self.assertNotEqual(a, float('nan'))
self.assertIsNot(a, float('nan'))
self.assertNotEqual(a, b)
self.assertIsNot(a, b)
constant_a = constant_op.constant(float('nan'))
constant_b = constant_op.constant(float('nan'))
ops.disable_tensor_equality()
self._test_hashable(constant_a, constant_b, True)
_v1_check(constant_a, constant_b)
ops.enable_tensor_equality()
_v2_check(constant_a, constant_b)
self._test_hashable(constant_a, constant_b, False)
variable_a = variables.Variable(float('nan'))
variable_b = variables.Variable(float('nan'))
ops.disable_tensor_equality()
_v1_check(variable_a, variable_b)
self._test_hashable(variable_a, variable_b, True)
ops.enable_tensor_equality()
_v2_check(variable_a, variable_b)
self._test_hashable(variable_a, variable_b, False)
numpy_a = np.array(float('nan'))
numpy_b = np.array(float('nan'))
_v2_check(numpy_a, numpy_b)
self._test_hashable(numpy_a, numpy_b, False)
finally:
if default:
ops.enable_tensor_equality()
else:
ops.disable_tensor_equality()
def test_binary_cwise_ops(self):
# Enable tensor equality to test `equal` and `not_equal` ops below.
default_equality = framework_ops.Tensor._USE_EQUALITY
framework_ops.enable_tensor_equality()
try:
logical_ops = [
math_ops.logical_and, math_ops.logical_or, math_ops.logical_xor
]
# Wrapper functions restricting the range of inputs of zeta and polygamma.
def safe_polygamma(x, y):
return math_ops.polygamma(
math_ops.round(clip_ops.clip_by_value(y, 1, 10)), x * x + 1)
def safe_zeta(x, y):
return math_ops.zeta(x * x + 1, y * y)
float_ops = [
math_ops.add,
math_ops.add_v2,
math_ops.atan2,
math_ops.complex,
math_ops.div,
math_ops.divide,
math_ops.div_no_nan,
math_ops.equal,
lambda x, y: framework_ops.convert_to_tensor(x == y),
lambda x, y: framework_ops.convert_to_tensor(x != y),
math_ops.floor_mod,
math_ops.greater,
math_ops.greater_equal,
math_ops.igamma,
math_ops.igammac,
math_ops.igamma_grad_a,
math_ops.less,
math_ops.less_equal,
math_ops.maximum,
math_ops.minimum,
math_ops.mod,
math_ops.multiply,
math_ops.not_equal,
math_ops.pow,
math_ops.squared_difference,
math_ops.subtract,
math_ops.truncate_mod,
safe_polygamma,
safe_zeta,
]
# FloorDiv fails on XLA due floor's discontinuities exacerbating small
# division differences.
if not test_util.is_xla_enabled():
float_ops += [math_ops.floor_div]
for op in logical_ops + float_ops:
x = random_ops.random_uniform([7, 3, 5])
y = random_ops.random_uniform([3, 5])
if op in logical_ops:
x = x > 0
y = y > 0
output_dtypes = []
# pylint: disable=cell-var-from-loop
def loop_fn(i):
x1 = array_ops.gather(x, i)
y1 = array_ops.gather(y, i)
outputs = [op(x, y), op(x1, y), op(x, y1), op(x1, y1), op(x1, x1)]
del output_dtypes[:]
output_dtypes.extend(t.dtype for t in outputs)
return outputs
# pylint: enable=cell-var-from-loop
self._test_loop_fn(loop_fn, 3)
finally:
if not default_equality:
framework_ops.disable_tensor_equality()
def testEqualityCompare(self):
default = ops.Tensor._USE_EQUALITY
try:
tf_a = constant_op.constant([1, 2])
tf_b = constant_op.constant([1, 2])
tf_c = constant_op.constant([1, 1])
np_a = np.array([1, 2])
np_b = np.array([1, 2])
np_c = np.array([1, 1])
ops.disable_tensor_equality()
# We don't do element-wise comparison
self.assertNotEqual(tf_a, tf_b)
self.assertNotEqual(tf_a, tf_c)
# We can compare list of tensors
self.assertEqual([tf_a, tf_b], [tf_a, tf_b])
self.assertNotEqual([tf_a, tf_b], [tf_b, tf_b])
# We can compare existence in a list
self.assertIn(tf_a, [tf_a, tf_b])
self.assertIn(tf_a, [tf_b, tf_a])
self.assertNotIn(tf_a, [tf_b, tf_c])
ops.enable_tensor_equality()
# We do element-wise comparison but can't convert results array to bool
with self.assertRaises(ValueError):
bool(tf_a == tf_b)
self.assertAllEqual(tf_a == tf_b, [True, True])
with self.assertRaises(ValueError):
bool(tf_a == tf_c)
self.assertAllEqual(tf_a == tf_c, [True, False])
self.assertNotAllEqual(tf_a, tf_c)
with self.assertRaises(ValueError):
bool(np_a == np_b)
self.assertAllEqual(np_a == np_b, [True, True])
with self.assertRaises(ValueError):
bool(np_a == np_c)
self.assertAllEqual(np_a == np_c, [True, False])
self.assertNotAllEqual(np_a, np_c)
# Warning even though we technically shouldn't be able to compare here,
# since the id is the same both TF & numpy will handle lists with the same
# value without raising an error
self.assertEqual([tf_a, tf_b], [tf_a, tf_b])
with self.assertRaises(ValueError):
bool([tf_a, tf_b] == [tf_b, tf_b])
self.assertEqual([np_a, np_b], [np_a, np_b])
with self.assertRaises(ValueError):
bool([np_a, np_b] == [np_b, np_b])
# Similar to lists we shouldn't be able to do a `in` check such as
# `if a in [a,b]`. However if `a` is the first element, it works due to
# short circuiting
self.assertIn(tf_a, [tf_a, tf_b])
with self.assertRaises(ValueError):
bool(tf_a in [tf_b, tf_a])
with self.assertRaises(ValueError):
bool(tf_a in [tf_b, tf_c])
self.assertIn(np_a, [np_a, np_b])
with self.assertRaises(ValueError):
bool(np_a in [np_b, np_a])
with self.assertRaises(ValueError):
bool(np_a in [np_b, np_c])
# rank 0
self.assertAllEqual(
constant_op.constant(1) == constant_op.constant(1), True)
self.assertAllEqual(
constant_op.constant(1) == constant_op.constant(2), False)
self.assertAllEqual(np.array(1) == np.array(1), True)
self.assertAllEqual(np.array(1) == np.array(2), False)
finally:
if default:
ops.enable_tensor_equality()
else:
ops.disable_tensor_equality()
