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
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
|
# =============================================================================
# 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.
# =============================================================================
"""Table-driven test for encode_proto op.
This test is run once with each of the *.TestCase.pbtxt files
in the test directory.
It tests that encode_proto is a lossless inverse of decode_proto
(for the specified fields).
"""
# Python3 readiness boilerplate
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from absl.testing import parameterized
import numpy as np
from google.protobuf import text_format
from tensorflow.contrib.proto.python.kernel_tests import proto_op_test_base as test_base
from tensorflow.contrib.proto.python.kernel_tests import test_example_pb2
from tensorflow.python.framework import dtypes
from tensorflow.python.ops import array_ops
class EncodeProtoOpTestBase(test_base.ProtoOpTestBase, parameterized.TestCase):
"""Base class for testing proto encoding ops."""
def __init__(self, decode_module, encode_module, methodName='runTest'): # pylint: disable=invalid-name
"""EncodeProtoOpTestBase initializer.
Args:
decode_module: a module containing the `decode_proto_op` method
encode_module: a module containing the `encode_proto_op` method
methodName: the name of the test method (same as for test.TestCase)
"""
super(EncodeProtoOpTestBase, self).__init__(methodName)
self._decode_module = decode_module
self._encode_module = encode_module
def testBadInputs(self):
# Invalid field name
with self.test_session():
with self.assertRaisesOpError('Unknown field: non_existent_field'):
self._encode_module.encode_proto(
sizes=[[1]],
values=[np.array([[0.0]], dtype=np.int32)],
message_type='tensorflow.contrib.proto.TestValue',
field_names=['non_existent_field']).eval()
# Incorrect types.
with self.test_session():
with self.assertRaisesOpError(
'Incompatible type for field double_value.'):
self._encode_module.encode_proto(
sizes=[[1]],
values=[np.array([[0.0]], dtype=np.int32)],
message_type='tensorflow.contrib.proto.TestValue',
field_names=['double_value']).eval()
# Incorrect shapes of sizes.
with self.test_session():
with self.assertRaisesOpError(
r'sizes should be batch_size \+ \[len\(field_names\)\]'):
sizes = array_ops.placeholder(dtypes.int32)
values = array_ops.placeholder(dtypes.float64)
self._encode_module.encode_proto(
sizes=sizes,
values=[values],
message_type='tensorflow.contrib.proto.TestValue',
field_names=['double_value']).eval(feed_dict={
sizes: [[[0, 0]]],
values: [[0.0]]
})
# Inconsistent shapes of values.
with self.test_session():
with self.assertRaisesOpError(
'Values must match up to the last dimension'):
sizes = array_ops.placeholder(dtypes.int32)
values1 = array_ops.placeholder(dtypes.float64)
values2 = array_ops.placeholder(dtypes.int32)
(self._encode_module.encode_proto(
sizes=[[1, 1]],
values=[values1, values2],
message_type='tensorflow.contrib.proto.TestValue',
field_names=['double_value', 'int32_value']).eval(feed_dict={
values1: [[0.0]],
values2: [[0], [0]]
}))
def _testRoundtrip(self, in_bufs, message_type, fields):
field_names = [f.name for f in fields]
out_types = [f.dtype for f in fields]
with self.test_session() as sess:
sizes, field_tensors = self._decode_module.decode_proto(
in_bufs,
message_type=message_type,
field_names=field_names,
output_types=out_types)
out_tensors = self._encode_module.encode_proto(
sizes,
field_tensors,
message_type=message_type,
field_names=field_names)
out_bufs, = sess.run([out_tensors])
# Check that the re-encoded tensor has the same shape.
self.assertEqual(in_bufs.shape, out_bufs.shape)
# Compare the input and output.
for in_buf, out_buf in zip(in_bufs.flat, out_bufs.flat):
in_obj = test_example_pb2.TestValue()
in_obj.ParseFromString(in_buf)
out_obj = test_example_pb2.TestValue()
out_obj.ParseFromString(out_buf)
# Check that the deserialized objects are identical.
self.assertEqual(in_obj, out_obj)
# Check that the input and output serialized messages are identical.
# If we fail here, there is a difference in the serialized
# representation but the new serialization still parses. This could
# be harmless (a change in map ordering?) or it could be bad (e.g.
# loss of packing in the encoding).
self.assertEqual(in_buf, out_buf)
@parameterized.named_parameters(*test_base.ProtoOpTestBase.named_parameters())
def testRoundtrip(self, case):
in_bufs = [value.SerializeToString() for value in case.values]
# np.array silently truncates strings if you don't specify dtype=object.
in_bufs = np.reshape(np.array(in_bufs, dtype=object), list(case.shapes))
return self._testRoundtrip(
in_bufs, 'tensorflow.contrib.proto.TestValue', case.fields)
@parameterized.named_parameters(*test_base.ProtoOpTestBase.named_parameters())
def testRoundtripPacked(self, case):
# Now try with the packed serialization.
# We test the packed representations by loading the same test cases using
# PackedTestValue instead of TestValue. To do this we rely on the text
# format being the same for packed and unpacked fields, and reparse the test
# message using the packed version of the proto.
in_bufs = [
# Note: float_format='.17g' is necessary to ensure preservation of
# doubles and floats in text format.
text_format.Parse(
text_format.MessageToString(
value, float_format='.17g'),
test_example_pb2.PackedTestValue()).SerializeToString()
for value in case.values
]
# np.array silently truncates strings if you don't specify dtype=object.
in_bufs = np.reshape(np.array(in_bufs, dtype=object), list(case.shapes))
return self._testRoundtrip(
in_bufs, 'tensorflow.contrib.proto.PackedTestValue', case.fields)
|
