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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.
# ==============================================================================
"""Unit tests for quantizing a Tensorflow graph."""
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 quantize
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 control_flow_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.platform import googletest
conv2d = layers.conv2d
separable_conv2d = layers.separable_conv2d
class QuantizeTest(test_util.TensorFlowTestCase):
def _RunTestOverParameters(self, test_fn):
params = [True, False]
for is_training in params:
test_fn(is_training)
def testInsertQuantOpFailsWhenOpsNotConnected(self):
pass
def _TestInsertQuantOpFailsWhenOpsNotConnected(self, is_training):
graph = ops.Graph()
with graph.as_default():
batch_size, height, width, depth = 5, 128, 128, 3
inputs = array_ops.zeros((batch_size, height, width, depth))
conv = conv2d(inputs, 32, [5, 5], stride=2, padding='SAME',
weights_initializer=self._WeightInit(0.09),
activation_fn=None, scope='test')
relu = nn_ops.relu6(inputs)
# Inserting a quantization op between two unconnected ops should fail with
# ValueError.
with self.assertRaises(ValueError) as err:
quantize._InsertQuantOp('test', is_training, conv.op, [relu.op],
'FailingQuantOp')
self.assertEqual(
str(err.exception), 'Some inputs not quantized for ops: [Relu6]')
def testInsertQuantOpForAddAfterConv2d(self):
self._RunTestOverParameters(self._TestInsertQuantOpForAddAfterConv2d)
def _TestInsertQuantOpForAddAfterConv2d(self, is_training):
graph = ops.Graph()
with graph.as_default():
batch_size, height, width, depth = 5, 128, 128, 3
input1 = array_ops.zeros((batch_size, height, width, depth))
input2 = array_ops.zeros((batch_size, height / 2, width / 2, 32))
conv = conv2d(input1, 32, [5, 5], stride=2, padding='SAME',
weights_initializer=self._WeightInit(0.09),
activation_fn=None, scope='test/test')
node = math_ops.add(conv, input2, name='test/add')
node = array_ops.identity(node, name='test/identity')
update_barrier = control_flow_ops.no_op(name='update_barrier')
with ops.control_dependencies([update_barrier]):
array_ops.identity(node, name='control_dependency')
quantize.Quantize(graph, is_training, weight_bits=8, activation_bits=8)
quantization_node_name = 'FakeQuantWithMinMaxVars'
add_quant = graph.get_operation_by_name('test/add_quant/' +
quantization_node_name)
self.assertEqual(add_quant.type, quantization_node_name)
def testInsertQuantOpForAddAfterSeparableConv2d(self):
self._RunTestOverParameters(
self._TestInsertQuantOpForAddAfterSeparableConv2d)
def _TestInsertQuantOpForAddAfterSeparableConv2d(self, is_training):
graph = ops.Graph()
with graph.as_default():
batch_size, height, width, depth = 5, 128, 128, 3
input1 = array_ops.zeros((batch_size, height, width, depth))
input2 = array_ops.zeros((batch_size, height / 2, width / 2, depth))
conv = separable_conv2d(input1, None, [5, 5], stride=2,
depth_multiplier=1.0, padding='SAME',
weights_initializer=self._WeightInit(0.09),
activation_fn=None, scope='test/test')
node = math_ops.add(conv, input2, name='test/add')
node = array_ops.identity(node, name='test/identity')
update_barrier = control_flow_ops.no_op(name='update_barrier')
with ops.control_dependencies([update_barrier]):
array_ops.identity(node, name='control_dependency')
quantize.Quantize(graph, is_training, weight_bits=8, activation_bits=8)
quantization_node_name = 'FakeQuantWithMinMaxVars'
add_quant = graph.get_operation_by_name('test/add_quant/' +
quantization_node_name)
self.assertEqual(add_quant.type, quantization_node_name)
def testFinalLayerQuantized(self):
self._RunTestOverParameters(self._TestFinalLayerQuantized)
def _TestFinalLayerQuantized(self, is_training):
graph = ops.Graph()
with graph.as_default():
batch_size, height, width, depth = 5, 128, 128, 3
input1 = array_ops.zeros((batch_size, height, width, depth))
_ = conv2d(
input1,
32, [5, 5],
stride=2,
padding='SAME',
weights_initializer=self._WeightInit(0.09),
activation_fn=None,
scope='test')
# Ensure that the a FakeQuant operation is in the outputs of the BiasAdd.
bias_add_op = graph.get_operation_by_name('test/BiasAdd')
quantize.Quantize(graph, is_training, weight_bits=8, activation_bits=8)
self.assertTrue('FakeQuantWithMinMaxVars' in
[op.type for op in bias_add_op.outputs[0].consumers()])
def testPostActivationBypassQuantized(self):
self._RunTestOverParameters(self._TestPostActivationBypassQuantized)
def _TestPostActivationBypassQuantized(self, is_training):
graph = ops.Graph()
with graph.as_default():
batch_size, height, width, depth = 5, 128, 128, 3
input1 = array_ops.zeros((batch_size, height, width, depth))
input2 = array_ops.zeros((batch_size, height / 2, width / 2, 32))
conv = conv2d(
input1,
32, [5, 5],
stride=2,
padding='SAME',
weights_initializer=self._WeightInit(0.09),
activation_fn=array_ops.identity,
scope='test/test')
bypass_tensor = math_ops.add(conv, input2, name='test/add')
_ = array_ops.identity(bypass_tensor, name='test/output')
quantize.Quantize(graph, is_training, weight_bits=8, activation_bits=8)
# Ensure that the bypass node is preceded and followed by
# FakeQuantWithMinMaxVars operations.
self.assertTrue('FakeQuantWithMinMaxVars' in
[c.type for c in bypass_tensor.consumers()])
self.assertTrue('FakeQuantWithMinMaxVars' in
[i.op.type for i in bypass_tensor.op.inputs])
def testWithNameScope(self):
self._RunTestOverParameters(self._TestWithNameScope)
def _TestWithNameScope(self, is_training):
graph = ops.Graph()
with graph.as_default():
with graph.name_scope('name_scope'):
batch_size, height, width, depth = 5, 128, 128, 3
input1 = array_ops.zeros((batch_size, height, width, depth))
_ = conv2d(
input1,
32, [5, 5],
stride=2,
padding='SAME',
weights_initializer=self._WeightInit(0.09),
activation_fn=None,
scope='test')
quantize.Quantize(graph, is_training, weight_bits=8, activation_bits=8)
for op in graph.get_operations():
self.assertTrue(not op.name.startswith('name_scope/name_scope/'),
'Broken op: %s' % op.name)
def _WeightInit(self, stddev):
"""Returns 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 initialized that initialzes with a truncated normal variable.
"""
return init_ops.truncated_normal_initializer(stddev=stddev)
if __name__ == '__main__':
googletest.main()
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