b30c40a4e1
It was difficult to change internal op counts because there are many tests that depend on the specific seed that's currently based on op counts. With this change, get_seed() doesn't depend on the internal op count but the number of calls to get_seed() PiperOrigin-RevId: 294523232 Change-Id: I3dc05a8aed6d42dcc372b734615312eb94aea81d
83 lines
3.4 KiB
Python
83 lines
3.4 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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"""Tests for utilities working with arbitrarily nested structures."""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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from tensorflow.python.data.util import random_seed as data_random_seed
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from tensorflow.python.eager import context
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from tensorflow.python.framework import constant_op
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from tensorflow.python.framework import dtypes
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from tensorflow.python.framework import random_seed
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from tensorflow.python.framework import test_util
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from tensorflow.python.platform import test
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class RandomSeedTest(test.TestCase):
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@test_util.run_in_graph_and_eager_modes
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def testRandomSeed(self):
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zero_t = constant_op.constant(0, dtype=dtypes.int64, name='zero')
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one_t = constant_op.constant(1, dtype=dtypes.int64, name='one')
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intmax_t = constant_op.constant(
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2**31 - 1, dtype=dtypes.int64, name='intmax')
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test_cases = [
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# Each test case is a tuple with input to get_seed:
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# (input_graph_seed, input_op_seed)
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# and output from get_seed:
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# (output_graph_seed, output_op_seed)
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((None, None), (0, 0)),
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((None, 1), (random_seed.DEFAULT_GRAPH_SEED, 1)),
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((1, 1), (1, 1)),
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((0, 0), (0, 2**31 - 1)), # Avoid nondeterministic (0, 0) output
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((2**31 - 1, 0), (0, 2**31 - 1)), # Don't wrap to (0, 0) either
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((0, 2**31 - 1), (0, 2**31 - 1)), # Wrapping for the other argument
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# Once more, with tensor-valued arguments
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((None, one_t), (random_seed.DEFAULT_GRAPH_SEED, 1)),
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((1, one_t), (1, 1)),
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((0, zero_t), (0, 2**31 - 1)), # Avoid nondeterministic (0, 0) output
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((2**31 - 1, zero_t), (0, 2**31 - 1)), # Don't wrap to (0, 0) either
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((0, intmax_t), (0, 2**31 - 1)), # Wrapping for the other argument
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]
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for tc in test_cases:
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tinput, toutput = tc[0], tc[1]
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random_seed.set_random_seed(tinput[0])
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g_seed, op_seed = data_random_seed.get_seed(tinput[1])
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g_seed = self.evaluate(g_seed)
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op_seed = self.evaluate(op_seed)
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msg = 'test_case = {0}, got {1}, want {2}'.format(
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tinput, (g_seed, op_seed), toutput)
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self.assertEqual((g_seed, op_seed), toutput, msg=msg)
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random_seed.set_random_seed(None)
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if not context.executing_eagerly():
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random_seed.set_random_seed(1)
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for i in range(10):
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tinput = (1, None)
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toutput = (1, i)
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random_seed.set_random_seed(tinput[0])
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g_seed, op_seed = data_random_seed.get_seed(tinput[1])
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g_seed = self.evaluate(g_seed)
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op_seed = self.evaluate(op_seed)
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msg = 'test_case = {0}, got {1}, want {2}'.format(
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1, (g_seed, op_seed), toutput)
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self.assertEqual((g_seed, op_seed), toutput, msg=msg)
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if __name__ == '__main__':
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test.main()
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