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# Copyright 2018 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.moving_averages when using a DistributionStrategy."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from absl.testing import parameterized
from tensorflow.contrib.distribute.python import combinations
from tensorflow.python.eager import test
from tensorflow.python.framework import constant_op
from tensorflow.python.framework import dtypes
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import variables
from tensorflow.python.training import moving_averages
all_combinations = combinations.combine(
distribution=[combinations.default_strategy,
combinations.one_device_strategy,
combinations.mirrored_strategy_with_gpu_and_cpu],
mode=["graph"])
class AssignMovingAveragesTest(test.TestCase, parameterized.TestCase):
@combinations.generate(all_combinations)
def testTowerModeWithoutZeroDebias(self, distribution):
tower_id = [0]
def tower_fn():
var = variables.Variable([10.0, 11.0])
val = constant_op.constant([1.0 + tower_id[0], 2.0 - tower_id[0]])
tower_id[0] += 1
decay = 0.25
assign = moving_averages.assign_moving_average(
var, val, decay, zero_debias=False)
return var, assign
with distribution.scope(), self.cached_session() as sess:
var, assign = distribution.call_for_each_tower(tower_fn)
variables.global_variables_initializer().run()
self.assertAllClose([10.0, 11.0], var.eval())
sess.run(distribution.unwrap(assign))
# Mean of val across calls to tower_fn().
average_val = [1.0 + 0.5 * (tower_id[0] - 1),
2.0 - 0.5 * (tower_id[0] - 1)]
val_weight = 1.0 - 0.25
self.assertAllClose(
[10.0 * 0.25 + average_val[0] * val_weight,
11.0 * 0.25 + average_val[1] * val_weight],
var.eval())
@combinations.generate(all_combinations)
def testTowerMode(self, distribution):
tower_id = [0]
def tower_fn():
var = variables.Variable([0.0, 0.0])
val = constant_op.constant([1.0 + tower_id[0], 2.0 - tower_id[0]])
tower_id[0] += 1
decay = 0.25
assign = moving_averages.assign_moving_average(var, val, decay)
return var, assign.op
with distribution.scope(), self.cached_session() as sess:
var, assign_op = distribution.call_for_each_tower(tower_fn)
variables.global_variables_initializer().run()
self.assertAllClose([0.0, 0.0], var.eval())
sess.run(distribution.unwrap(assign_op))
# Mean of val across calls to tower_fn().
average_val = [1.0 + 0.5 * (tower_id[0] - 1),
2.0 - 0.5 * (tower_id[0] - 1)]
self.assertAllClose(average_val, var.eval())
@combinations.generate(all_combinations)
def testCrossTowerWithoutZeroDebias(self, distribution):
with distribution.scope(), self.cached_session() as sess:
var = variables.Variable([10.0, 11.0])
val = constant_op.constant([1.0, 2.0])
decay = 0.25
# NOTE(josh11b): We currently generate an error if val is a PerDevice value.
assign = moving_averages.assign_moving_average(
var, val, decay, zero_debias=False)
variables.global_variables_initializer().run()
self.assertAllClose([10.0, 11.0], var.eval())
sess.run(assign)
average_val = [1.0, 2.0]
val_weight = 1.0 - 0.25
self.assertAllClose(
[10.0 * 0.25 + average_val[0] * val_weight,
11.0 * 0.25 + average_val[1] * val_weight],
var.eval())
# Also try assign.op.
sess.run(assign.op)
orig_weight = 0.25 * 0.25
val_weight = 1.0 - orig_weight
self.assertAllClose(
[10.0 * orig_weight + average_val[0] * val_weight,
11.0 * orig_weight + average_val[1] * val_weight],
var.eval())
@combinations.generate(all_combinations)
def testCrossTower(self, distribution):
with distribution.scope(), self.cached_session() as sess:
var = variables.Variable([0.0, 0.0])
val = array_ops.placeholder(dtypes.float32)
decay = 0.25
# NOTE(josh11b): We currently generate an error if val is a PerDevice value.
assign = moving_averages.assign_moving_average(var, val, decay)
variables.global_variables_initializer().run()
self.assertAllClose([0.0, 0.0], var.eval())
sess.run(assign, feed_dict={val: [1.0, 2.0]})
self.assertAllClose([1.0, 2.0], var.eval())
# Also try assign.op.
sess.run(assign.op, feed_dict={val: [10.0, 0.0]})
self.assertAllClose(
[(1.0 * 0.25 + 10.0) / (1.0 * 0.25 + 1.0),
(2.0 * 0.25 + 0.0) / (1.0 * 0.25 + 1.0)],
var.eval())
if __name__ == "__main__":
test.main()
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