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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 common utilities in this package."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from tensorflow.contrib.layers.python.layers import layers
from tensorflow.contrib.quantize.python import common
from tensorflow.python.client import session
from tensorflow.python.framework import constant_op
from tensorflow.python.framework import ops
from tensorflow.python.framework import test_util
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import init_ops
from tensorflow.python.ops import math_ops
from tensorflow.python.ops import nn_ops
from tensorflow.python.ops import variable_scope
from tensorflow.python.ops import variables
from tensorflow.python.platform import googletest
batch_norm = layers.batch_norm
conv2d = layers.conv2d
class CommonTest(test_util.TensorFlowTestCase):
def testCreateOrGetQuantizationStep(self):
self._TestCreateOrGetQuantizationStep(False)
def testCreateOrGetQuantizationStepResourceVar(self):
self._TestCreateOrGetQuantizationStep(True)
def _TestCreateOrGetQuantizationStep(self, use_resource):
g = ops.Graph()
with session.Session(graph=g) as sess:
variable_scope.get_variable_scope().set_use_resource(use_resource)
quantization_step_tensor = common.CreateOrGetQuantizationStep()
# Check that operations are added to the graph.
num_nodes = len(g.get_operations())
self.assertGreater(num_nodes, 0)
# Check that getting the quantization step doesn't change the graph.
get_quantization_step_tensor = common.CreateOrGetQuantizationStep()
self.assertEqual(quantization_step_tensor, get_quantization_step_tensor)
self.assertEqual(num_nodes, len(g.get_operations()))
# Ensure that running the graph increments the quantization step.
sess.run(variables.global_variables_initializer())
step_val = sess.run(quantization_step_tensor)
self.assertEqual(step_val, 1)
# Ensure that even running a graph that depends on the quantization step
# multiple times only executes it once.
a = quantization_step_tensor + 1
b = a + quantization_step_tensor
_, step_val = sess.run([b, quantization_step_tensor])
self.assertEqual(step_val, 2)
def testRerouteTensor(self):
a = constant_op.constant(1, name='a')
b = constant_op.constant(2, name='b')
c = constant_op.constant(3, name='c')
d = constant_op.constant(4, name='d')
add_ac = math_ops.add(a, c)
add_ad = math_ops.add(a, d)
# Ensure that before rerouting the inputs are what we think.
self._CheckOpHasInputs(add_ac.op, [a, c])
self._CheckOpHasInputs(add_ad.op, [a, d])
# references to tensor a should be replaced with b for all ops in
# can_modify. This means add_ac will be changed but add_ad will not.
common.RerouteTensor(b, a, can_modify=[add_ac.op])
self._CheckOpHasInputs(add_ac.op, [b, c])
self._CheckOpHasInputs(add_ad.op, [a, d])
def _CheckOpHasInputs(self, op, inputs):
for i in inputs:
self.assertIn(i, op.inputs)
def testBatchNormScope(self):
batch_size, height, width, depth = 5, 128, 128, 3
g = ops.Graph()
with g.as_default():
inputs = array_ops.zeros((batch_size, height, width, depth))
stride = 1
out_depth = 32
scope = ''
node = conv2d(
inputs,
out_depth, [2, 2],
stride=stride,
padding='SAME',
weights_initializer=self._WeightInit(0.09),
activation_fn=None,
normalizer_fn=batch_norm,
normalizer_params=self._BatchNormParams(False),
scope=scope)
node = nn_ops.relu(node, name='Relu6')
bn_list = common.BatchNormGroups(g)
with open('/tmp/common_test.pbtxt', 'w') as f:
f.write(str(g.as_graph_def()))
# Exactly one batch norm layer with empty scope should be found
self.assertEqual(len(bn_list), 1)
self.assertEqual(bn_list[0], '')
def _BatchNormParams(self, fused=False, force_updates=False):
params = {
'center': True,
'scale': True,
'decay': 1.0 - 0.003,
'fused': fused
}
return params
def _WeightInit(self, stddev):
"""Returns a truncated normal variable initializer.
Function is defined purely to shorten the name so that it stops wrapping.
Args:
stddev: Standard deviation of normal variable.
Returns:
An initializer that initializes with a truncated normal variable.
"""
return init_ops.truncated_normal_initializer(stddev=stddev, seed=1234)
if __name__ == '__main__':
googletest.main()
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