b17d53c0cd
In order to support tests running in eager mode we need to avoid unnecessary use of Sessions in tests. This moves to remove some of the uses of the `run` function in favor of `evaluate`. PiperOrigin-RevId: 223009795
73 lines
2.3 KiB
Python
73 lines
2.3 KiB
Python
# Copyright 2015 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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"""Tests for division with division imported from __future__.
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This file should be exactly the same as division_past_test.py except
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for the __future__ division line.
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"""
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from __future__ import absolute_import
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# from __future__ import division # Intentionally skip this import
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from __future__ import print_function
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import numpy as np
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from tensorflow.python.framework import constant_op
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from tensorflow.python.framework import ops
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from tensorflow.python.platform import test
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class DivisionTestCase(test.TestCase):
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def testDivision(self):
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"""Test all the different ways to divide."""
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values = [1, 2, 7, 11]
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functions = (lambda x: x), constant_op.constant
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dtypes = np.int8, np.int16, np.int32, np.int64, np.float32, np.float64
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tensors = []
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checks = []
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def check(x, y):
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x = ops.convert_to_tensor(x)
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y = ops.convert_to_tensor(y)
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tensors.append((x, y))
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def f(x, y):
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self.assertEqual(x.dtype, y.dtype)
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self.assertEqual(x, y)
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checks.append(f)
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with self.cached_session() as sess:
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for dtype in dtypes:
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for x in map(dtype, values):
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for y in map(dtype, values):
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for fx in functions:
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for fy in functions:
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tf_x = fx(x)
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tf_y = fy(y)
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div = x / y
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tf_div = tf_x / tf_y
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check(div, tf_div)
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floordiv = x // y
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tf_floordiv = tf_x // tf_y
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check(floordiv, tf_floordiv)
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# Do only one sess.run for speed
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for f, (x, y) in zip(checks, self.evaluate(tensors)):
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f(x, y)
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if __name__ == "__main__":
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test.main()
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