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# Copyright 2017 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 third_party.tensorflow.contrib.quantize.python.quant_ops."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from tensorflow.contrib.quantize.python import quant_ops
from tensorflow.python.client import session
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import ops
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import variables
from tensorflow.python.platform import googletest
_MIN_MAX_VARS = 'min_max_vars'
class QuantOpsTest(googletest.TestCase):
def testLastValueQuantizeTrainingAssign(self):
g = ops.Graph()
with session.Session(graph=g) as sess:
x = array_ops.placeholder(dtypes.float32, shape=[2])
y = quant_ops.LastValueQuantize(
x,
init_min=0.0,
init_max=0.0,
is_training=True,
vars_collection=_MIN_MAX_VARS)
# Run the step.
sess.run(variables.global_variables_initializer())
sess.run(y, feed_dict={x: [-1.0, 1.0]})
# Now check that the min_max_vars were, in fact, updated.
min_value, max_value = self._GetMinMaxValues(sess)
self.assertEqual(min_value, -1.0)
self.assertEqual(max_value, 1.0)
def testMovingAvgQuantizeTrainingAssign(self):
g = ops.Graph()
with session.Session(graph=g) as sess:
x = array_ops.placeholder(dtypes.float32, shape=[2])
y = quant_ops.MovingAvgQuantize(
x,
init_min=0.0,
init_max=0.0,
is_training=True,
vars_collection=_MIN_MAX_VARS)
# Run the step.
sess.run(variables.global_variables_initializer())
# Do two runs to avoid zero debias.
sess.run(y, feed_dict={x: [-1.0, 1.0]})
sess.run(y, feed_dict={x: [0.0, 0.0]})
# Now check that the min_max_vars were, in fact, updated.
min_value, max_value = self._GetMinMaxValues(sess)
self.assertGreater(min_value, -1.0)
self.assertLess(min_value, 0.0)
self.assertGreater(max_value, 0.0)
self.assertLess(max_value, 1.0)
def _GetMinMaxValues(self, sess):
min_max_vars = ops.get_collection(_MIN_MAX_VARS)
self.assertEqual(len(min_max_vars), 2)
min_idx = 0 if 'min' in min_max_vars[0].name else 1
max_idx = (min_idx + 1) % 2
min_var, max_var = min_max_vars[min_idx], min_max_vars[max_idx]
min_max_values = sess.run([min_var, max_var])
return min_max_values[0], min_max_values[1]
if __name__ == '__main__':
googletest.main()
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