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