1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
|
# Copyright 2016 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 the SWIG-wrapped quantize training rewriting."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
from tensorflow.python import pywrap_tensorflow
from tensorflow.python.client import session
from tensorflow.python.framework import constant_op
from tensorflow.python.framework import importer
from tensorflow.python.framework import ops
from tensorflow.python.ops import math_ops
from tensorflow.python.ops import variables
from tensorflow.python.platform import test
from tensorflow.python.training import saver as saver_module
class PywrapQuantizeTrainingTest(test.TestCase):
# Mainly to verify the python interface is working.
# More tests for this function can be found in the related c++ tests.
def testQuantizeTraining(self):
with session.Session() as sess:
a = constant_op.constant(6.0, shape=[1, 1])
b = constant_op.constant(7.0, shape=[1, 1])
c = math_ops.matmul(a, b, name='matmul')
self.assertEquals(c.eval(), 42.0)
self.assertEquals(len(sess.graph_def.node), 3)
result = pywrap_tensorflow.do_quantize_training_on_graphdef(
sess.graph_def, 8)
# We just want to guarantee that some rewrite happened.
self.assertGreater(len(result.node), 3)
# Test that save/restoring works for EMA variables generated in the
# quantized training rewrite.
def testQuantizedSaveRestore(self):
save_path = os.path.join(self.get_temp_dir(), 'quantized_save_restore')
g = ops.Graph()
with session.Session(graph=g) as sess:
a = constant_op.constant(6.0, shape=[1, 1], name='a')
b = variables.VariableV1(
constant_op.constant(7.0, shape=[1, 1]), name='b')
c = math_ops.matmul(a, b, name='matmul')
init_op = variables.global_variables_initializer()
saver = saver_module.Saver({'b': b})
result = pywrap_tensorflow.do_quantize_training_on_graphdef(
sess.graph_def, 8)
with ops.Graph().as_default() as g, session.Session(graph=g) as sess:
_ = importer.import_graph_def(result, name='')
# Initialize the variable.
sess.run(g.get_operation_by_name(init_op.name))
# Run the graph for one step to assign values to the quantization min/max
# variables.
sess.run(g.get_tensor_by_name(c.name))
saver.save(sess, save_path)
with ops.Graph().as_default() as g, session.Session(graph=g) as sess:
_ = importer.import_graph_def(result, name='')
# When we restore the saved variabled, the quantization variables should
# be restored as well.
saver.restore(sess, save_path)
self.assertEquals(7.0, sess.run(g.get_tensor_by_name('b:0')))
self.assertEquals(6.0, sess.run(g.get_tensor_by_name('a/Min/Variable:0')))
self.assertEquals(6.0, sess.run(g.get_tensor_by_name('a/Max/Variable:0')))
self.assertEquals(7.0,
sess.run(g.get_tensor_by_name('b/read/Min/Variable:0')))
self.assertEquals(7.0,
sess.run(g.get_tensor_by_name('b/read/Max/Variable:0')))
if __name__ == '__main__':
test.main()
|
