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# Copyright 2015 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.
# ==============================================================================
"""Tests for training_util."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import ops
from tensorflow.python.ops import variables
from tensorflow.python.platform import test
from tensorflow.python.training import monitored_session
from tensorflow.python.training import training_util
class GlobalStepTest(test.TestCase):
def _assert_global_step(self, global_step, expected_dtype=dtypes.int64):
self.assertEqual('%s:0' % ops.GraphKeys.GLOBAL_STEP, global_step.name)
self.assertEqual(expected_dtype, global_step.dtype.base_dtype)
self.assertEqual([], global_step.get_shape().as_list())
def test_invalid_dtype(self):
with ops.Graph().as_default() as g:
self.assertIsNone(training_util.get_global_step())
variables.Variable(
0.0,
trainable=False,
dtype=dtypes.float32,
name=ops.GraphKeys.GLOBAL_STEP)
self.assertRaisesRegexp(TypeError, 'does not have integer type',
training_util.get_global_step)
self.assertRaisesRegexp(TypeError, 'does not have integer type',
training_util.get_global_step, g)
def test_invalid_shape(self):
with ops.Graph().as_default() as g:
self.assertIsNone(training_util.get_global_step())
variables.VariableV1(
[0],
trainable=False,
dtype=dtypes.int32,
name=ops.GraphKeys.GLOBAL_STEP)
self.assertRaisesRegexp(TypeError, 'not scalar',
training_util.get_global_step)
self.assertRaisesRegexp(TypeError, 'not scalar',
training_util.get_global_step, g)
def test_create_global_step(self):
self.assertIsNone(training_util.get_global_step())
with ops.Graph().as_default() as g:
global_step = training_util.create_global_step()
self._assert_global_step(global_step)
self.assertRaisesRegexp(ValueError, 'already exists',
training_util.create_global_step)
self.assertRaisesRegexp(ValueError, 'already exists',
training_util.create_global_step, g)
self._assert_global_step(training_util.create_global_step(ops.Graph()))
def test_get_global_step(self):
with ops.Graph().as_default() as g:
self.assertIsNone(training_util.get_global_step())
variables.VariableV1(
0,
trainable=False,
dtype=dtypes.int32,
name=ops.GraphKeys.GLOBAL_STEP)
self._assert_global_step(
training_util.get_global_step(), expected_dtype=dtypes.int32)
self._assert_global_step(
training_util.get_global_step(g), expected_dtype=dtypes.int32)
def test_get_or_create_global_step(self):
with ops.Graph().as_default() as g:
self.assertIsNone(training_util.get_global_step())
self._assert_global_step(training_util.get_or_create_global_step())
self._assert_global_step(training_util.get_or_create_global_step(g))
class GlobalStepReadTest(test.TestCase):
def test_global_step_read_is_none_if_there_is_no_global_step(self):
with ops.Graph().as_default():
self.assertIsNone(training_util._get_or_create_global_step_read())
training_util.create_global_step()
self.assertIsNotNone(training_util._get_or_create_global_step_read())
def test_reads_from_cache(self):
with ops.Graph().as_default():
training_util.create_global_step()
first = training_util._get_or_create_global_step_read()
second = training_util._get_or_create_global_step_read()
self.assertEqual(first, second)
def test_reads_before_increments(self):
with ops.Graph().as_default():
training_util.create_global_step()
read_tensor = training_util._get_or_create_global_step_read()
inc_op = training_util._increment_global_step(1)
inc_three_op = training_util._increment_global_step(3)
with monitored_session.MonitoredTrainingSession() as sess:
read_value, _ = sess.run([read_tensor, inc_op])
self.assertEqual(0, read_value)
read_value, _ = sess.run([read_tensor, inc_three_op])
self.assertEqual(1, read_value)
read_value = sess.run(read_tensor)
self.assertEqual(4, read_value)
if __name__ == '__main__':
test.main()
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