83 lines
3.0 KiB
Python
83 lines
3.0 KiB
Python
|
# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
|
||
|
|
#
|
||
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
||
|
|
# you may not use this file except in compliance with the License.
|
||
|
|
# You may obtain a copy of the License at
|
||
|
|
#
|
||
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
||
|
|
#
|
||
|
|
# Unless required by applicable law or agreed to in writing, software
|
||
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
||
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||
|
|
# See the License for the specific language governing permissions and
|
||
|
|
# limitations under the License.
|
||
|
|
# ==============================================================================
|
||
|
|
"""Exception handling statements: assert, etc."""
|
||
|
|
|
||
|
|
from tensorflow.python.framework import tensor_util
|
||
|
|
from tensorflow.python.ops import control_flow_assert
|
||
|
|
from tensorflow.python.util import tf_inspect
|
||
|
|
|
||
|
|
|
||
|
|
def assert_stmt(expression1, expression2):
|
||
|
|
"""Functional form of an assert statement.
|
||
|
|
|
||
|
|
This follows the semantics of the Python assert statement, however the
|
||
|
|
concrete implementations may deviate from it. See the respective
|
||
|
|
implementation for details.
|
||
|
|
|
||
|
|
In general, the assert statement should not be used for control flow.
|
||
|
|
Furthermore, it is encouraged that the assertion expressions should not have
|
||
|
|
side effects.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
expression1: Any
|
||
|
|
expression2: Callable[[], Any], returns the expression to include in the
|
||
|
|
error message when expression1 evaluates to False. When expression1 is
|
||
|
|
True, the result of expression2 will not be evaluated, however,
|
||
|
|
expression2 itself may be evaluated in some implementations.
|
||
|
|
|
||
|
|
Returns:
|
||
|
|
Any, implementation-dependent.
|
||
|
|
|
||
|
|
Raises:
|
||
|
|
ValueError: if any arguments are illegal.
|
||
|
|
"""
|
||
|
|
if not callable(expression2):
|
||
|
|
raise ValueError('{} must be a callable'.format(expression2))
|
||
|
|
args, _, keywords, _ = tf_inspect.getargspec(expression2)
|
||
|
|
if args or keywords:
|
||
|
|
raise ValueError('{} may not have any arguments'.format(expression2))
|
||
|
|
|
||
|
|
if tensor_util.is_tf_type(expression1):
|
||
|
|
return _tf_assert_stmt(expression1, expression2)
|
||
|
|
else:
|
||
|
|
return _py_assert_stmt(expression1, expression2)
|
||
|
|
|
||
|
|
|
||
|
|
def _tf_assert_stmt(expression1, expression2):
|
||
|
|
"""Overload of assert_stmt that stages a TF Assert.
|
||
|
|
|
||
|
|
This implementation deviates from Python semantics as follows:
|
||
|
|
(1) the assertion is verified regardless of the state of __debug__
|
||
|
|
(2) on assertion failure, the graph execution will fail with
|
||
|
|
tensorflow.errors.ValueError, rather than AssertionError.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
expression1: tensorflow.Tensor, must evaluate to a tf.bool scalar
|
||
|
|
expression2: Callable[[], Union[tensorflow.Tensor, List[tensorflow.Tensor]]]
|
||
|
|
|
||
|
|
Returns:
|
||
|
|
tensorflow.Operation
|
||
|
|
"""
|
||
|
|
expression2_tensors = expression2()
|
||
|
|
if not isinstance(expression2_tensors, list):
|
||
|
|
expression2_tensors = [expression2_tensors]
|
||
|
|
return control_flow_assert.Assert(expression1, expression2_tensors)
|
||
|
|
|
||
|
|
|
||
|
|
def _py_assert_stmt(expression1, expression2):
|
||
|
|
"""Overload of assert_stmt that executes a Python assert statement."""
|
||
|
|
assert expression1, expression2()
|
||
|
|
return None
|