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
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
|
# 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 export tools."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import random
import tempfile
import numpy as np
import six
from tensorflow.contrib import learn
from tensorflow.contrib.layers.python.layers import feature_column
from tensorflow.contrib.learn.python.learn.utils import export
from tensorflow.contrib.session_bundle import exporter
from tensorflow.contrib.session_bundle import manifest_pb2
from tensorflow.python.client import session
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import errors
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import random_ops
from tensorflow.python.platform import gfile
from tensorflow.python.platform import test
from tensorflow.python.training import saver
_X_KEY = 'my_x_key'
_X_COLUMN = feature_column.real_valued_column(_X_KEY, dimension=1)
def _training_input_fn():
x = random_ops.random_uniform(shape=(1,), minval=0.0, maxval=1000.0)
y = 2 * x + 3
return {_X_KEY: x}, y
class ExportTest(test.TestCase):
def _get_default_signature(self, export_meta_filename):
""" Gets the default signature from the export.meta file. """
with session.Session():
save = saver.import_meta_graph(export_meta_filename)
meta_graph_def = save.export_meta_graph()
collection_def = meta_graph_def.collection_def
signatures_any = collection_def['serving_signatures'].any_list.value
self.assertEquals(len(signatures_any), 1)
signatures = manifest_pb2.Signatures()
signatures_any[0].Unpack(signatures)
default_signature = signatures.default_signature
return default_signature
def _assert_export(self, export_monitor, export_dir, expected_signature):
self.assertTrue(gfile.Exists(export_dir))
# Only the written checkpoints are exported.
self.assertTrue(
saver.checkpoint_exists(os.path.join(export_dir, '00000001', 'export')),
'Exported checkpoint expected but not found: %s' %
os.path.join(export_dir, '00000001', 'export'))
self.assertTrue(
saver.checkpoint_exists(os.path.join(export_dir, '00000010', 'export')),
'Exported checkpoint expected but not found: %s' %
os.path.join(export_dir, '00000010', 'export'))
self.assertEquals(
six.b(os.path.join(export_dir, '00000010')),
export_monitor.last_export_dir)
# Validate the signature
signature = self._get_default_signature(
os.path.join(export_dir, '00000010', 'export.meta'))
self.assertTrue(signature.HasField(expected_signature))
def testExportMonitor_EstimatorProvidesSignature(self):
random.seed(42)
x = np.random.rand(1000)
y = 2 * x + 3
cont_features = [feature_column.real_valued_column('', dimension=1)]
regressor = learn.LinearRegressor(feature_columns=cont_features)
export_dir = os.path.join(tempfile.mkdtemp(), 'export')
export_monitor = learn.monitors.ExportMonitor(
every_n_steps=1, export_dir=export_dir, exports_to_keep=2)
regressor.fit(x, y, steps=10, monitors=[export_monitor])
self._assert_export(export_monitor, export_dir, 'regression_signature')
def testExportMonitor(self):
random.seed(42)
x = np.random.rand(1000)
y = 2 * x + 3
cont_features = [feature_column.real_valued_column('', dimension=1)]
export_dir = os.path.join(tempfile.mkdtemp(), 'export')
export_monitor = learn.monitors.ExportMonitor(
every_n_steps=1,
export_dir=export_dir,
exports_to_keep=2,
signature_fn=export.generic_signature_fn)
regressor = learn.LinearRegressor(feature_columns=cont_features)
regressor.fit(x, y, steps=10, monitors=[export_monitor])
self._assert_export(export_monitor, export_dir, 'generic_signature')
def testExportMonitorInputFeatureKeyMissing(self):
random.seed(42)
def _serving_input_fn():
return {
_X_KEY:
random_ops.random_uniform(
shape=(1,), minval=0.0, maxval=1000.0)
}, None
input_feature_key = 'my_example_key'
monitor = learn.monitors.ExportMonitor(
every_n_steps=1,
export_dir=os.path.join(tempfile.mkdtemp(), 'export'),
input_fn=_serving_input_fn,
input_feature_key=input_feature_key,
exports_to_keep=2,
signature_fn=export.generic_signature_fn)
regressor = learn.LinearRegressor(feature_columns=[_X_COLUMN])
with self.assertRaisesRegexp(KeyError, input_feature_key):
regressor.fit(input_fn=_training_input_fn, steps=10, monitors=[monitor])
def testExportMonitorInputFeatureKeyNoneNoFeatures(self):
random.seed(42)
input_feature_key = 'my_example_key'
def _serving_input_fn():
return {input_feature_key: None}, None
monitor = learn.monitors.ExportMonitor(
every_n_steps=1,
export_dir=os.path.join(tempfile.mkdtemp(), 'export'),
input_fn=_serving_input_fn,
input_feature_key=input_feature_key,
exports_to_keep=2,
signature_fn=export.generic_signature_fn)
regressor = learn.LinearRegressor(feature_columns=[_X_COLUMN])
with self.assertRaisesRegexp(ValueError,
'features or examples must be defined'):
regressor.fit(input_fn=_training_input_fn, steps=10, monitors=[monitor])
def testExportMonitorInputFeatureKeyNone(self):
random.seed(42)
input_feature_key = 'my_example_key'
def _serving_input_fn():
return {
input_feature_key:
None,
_X_KEY:
random_ops.random_uniform(
shape=(1,), minval=0.0, maxval=1000.0)
}, None
monitor = learn.monitors.ExportMonitor(
every_n_steps=1,
export_dir=os.path.join(tempfile.mkdtemp(), 'export'),
input_fn=_serving_input_fn,
input_feature_key=input_feature_key,
exports_to_keep=2,
signature_fn=export.generic_signature_fn)
regressor = learn.LinearRegressor(feature_columns=[_X_COLUMN])
with self.assertRaisesRegexp(ValueError, 'examples cannot be None'):
regressor.fit(input_fn=_training_input_fn, steps=10, monitors=[monitor])
def testExportMonitorInputFeatureKeyNoFeatures(self):
random.seed(42)
input_feature_key = 'my_example_key'
def _serving_input_fn():
return {
input_feature_key:
array_ops.placeholder(
dtype=dtypes.string, shape=(1,))
}, None
monitor = learn.monitors.ExportMonitor(
every_n_steps=1,
export_dir=os.path.join(tempfile.mkdtemp(), 'export'),
input_fn=_serving_input_fn,
input_feature_key=input_feature_key,
exports_to_keep=2,
signature_fn=export.generic_signature_fn)
regressor = learn.LinearRegressor(feature_columns=[_X_COLUMN])
with self.assertRaisesRegexp(KeyError, _X_KEY):
regressor.fit(input_fn=_training_input_fn, steps=10, monitors=[monitor])
def testExportMonitorInputFeature(self):
random.seed(42)
input_feature_key = 'my_example_key'
def _serving_input_fn():
return {
input_feature_key:
array_ops.placeholder(
dtype=dtypes.string, shape=(1,)),
_X_KEY:
random_ops.random_uniform(
shape=(1,), minval=0.0, maxval=1000.0)
}, None
export_dir = os.path.join(tempfile.mkdtemp(), 'export')
monitor = learn.monitors.ExportMonitor(
every_n_steps=1,
export_dir=export_dir,
input_fn=_serving_input_fn,
input_feature_key=input_feature_key,
exports_to_keep=2,
signature_fn=export.generic_signature_fn)
regressor = learn.LinearRegressor(feature_columns=[_X_COLUMN])
regressor.fit(input_fn=_training_input_fn, steps=10, monitors=[monitor])
self._assert_export(monitor, export_dir, 'generic_signature')
def testExportMonitorRegressionSignature(self):
def _regression_signature(examples, unused_features, predictions):
signatures = {}
signatures['regression'] = (exporter.regression_signature(examples,
predictions))
return signatures['regression'], signatures
random.seed(42)
x = np.random.rand(1000)
y = 2 * x + 3
cont_features = [feature_column.real_valued_column('', dimension=1)]
regressor = learn.LinearRegressor(feature_columns=cont_features)
export_dir = os.path.join(tempfile.mkdtemp(), 'export')
export_monitor = learn.monitors.ExportMonitor(
every_n_steps=1,
export_dir=export_dir,
exports_to_keep=1,
signature_fn=_regression_signature)
regressor.fit(x, y, steps=10, monitors=[export_monitor])
self.assertTrue(gfile.Exists(export_dir))
with self.assertRaises(errors.NotFoundError):
saver.checkpoint_exists(os.path.join(export_dir, '00000000', 'export'))
self.assertTrue(
saver.checkpoint_exists(os.path.join(export_dir, '00000010', 'export')))
# Validate the signature
signature = self._get_default_signature(
os.path.join(export_dir, '00000010', 'export.meta'))
self.assertTrue(signature.HasField('regression_signature'))
if __name__ == '__main__':
test.main()
|
