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
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
|
# 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.
# ==============================================================================
"""ModelFnOps tests."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import six
from tensorflow.contrib.learn.python.learn.estimators import constants
from tensorflow.contrib.learn.python.learn.estimators import model_fn
from tensorflow.python.client import session
from tensorflow.python.estimator.export import export_output as core_export_lib
from tensorflow.python.framework import constant_op
from tensorflow.python.ops import control_flow_ops
from tensorflow.python.platform import test
from tensorflow.python.saved_model import signature_constants
from tensorflow.python.training import basic_session_run_hooks
from tensorflow.python.training import monitored_session
class ModelFnopsTest(test.TestCase):
"""Multi-output tests."""
def create_predictions(self):
probabilities = constant_op.constant([1., 1., 1.])
scores = constant_op.constant([1., 2., 3.])
classes = constant_op.constant([b"0", b"1", b"2"])
return {
"probabilities": probabilities,
"scores": scores,
"classes": classes}
def create_model_fn_ops(self, predictions, output_alternatives,
mode=model_fn.ModeKeys.INFER):
return model_fn.ModelFnOps(
model_fn.ModeKeys.INFER,
predictions=predictions,
loss=constant_op.constant([1]),
train_op=control_flow_ops.no_op(),
eval_metric_ops={
"metric_key": (constant_op.constant(1.), control_flow_ops.no_op()),
"loss": (constant_op.constant(1.), control_flow_ops.no_op()),
},
training_chief_hooks=[basic_session_run_hooks.StepCounterHook()],
training_hooks=[basic_session_run_hooks.StepCounterHook()],
output_alternatives=output_alternatives,
scaffold=monitored_session.Scaffold())
def assertEquals_except_export_and_eval_loss(
self, model_fn_ops, estimator_spec):
expected_eval_metric_ops = {}
for key, value in six.iteritems(model_fn_ops.eval_metric_ops):
if key != "loss":
expected_eval_metric_ops[key] = value
self.assertEqual(model_fn_ops.predictions, estimator_spec.predictions)
self.assertEqual(model_fn_ops.loss, estimator_spec.loss)
self.assertEqual(model_fn_ops.train_op, estimator_spec.train_op)
self.assertEqual(expected_eval_metric_ops,
estimator_spec.eval_metric_ops)
self.assertAllEqual(model_fn_ops.training_chief_hooks,
estimator_spec.training_chief_hooks)
self.assertAllEqual(model_fn_ops.training_hooks,
estimator_spec.training_hooks)
self.assertEqual(model_fn_ops.scaffold, estimator_spec.scaffold)
def testEstimatorSpec_except_export(self):
predictions = self.create_predictions()
model_fn_ops = self.create_model_fn_ops(
predictions, None, mode=model_fn.ModeKeys.INFER)
estimator_spec = model_fn_ops.estimator_spec()
self.assertEquals_except_export_and_eval_loss(model_fn_ops, estimator_spec)
def testEstimatorSpec_export_regression_with_scores(self):
predictions = self.create_predictions()
output_alternatives = {"regression_head": (
constants.ProblemType.LINEAR_REGRESSION, predictions)}
model_fn_ops = self.create_model_fn_ops(
predictions, output_alternatives, mode=model_fn.ModeKeys.INFER)
estimator_spec = model_fn_ops.estimator_spec()
self.assertEquals_except_export_and_eval_loss(model_fn_ops, estimator_spec)
with session.Session():
regression_output = estimator_spec.export_outputs["regression_head"]
self.assertTrue(isinstance(
regression_output, core_export_lib.RegressionOutput))
self.assertAllEqual(predictions["scores"].eval(),
regression_output.value.eval())
def testEstimatorSpec_export_regression_with_probabilities(self):
predictions = self.create_predictions()
output_alternatives_predictions = predictions.copy()
del output_alternatives_predictions["scores"]
output_alternatives = {"regression_head": (
constants.ProblemType.LINEAR_REGRESSION,
output_alternatives_predictions)}
model_fn_ops = self.create_model_fn_ops(
predictions, output_alternatives, mode=model_fn.ModeKeys.INFER)
estimator_spec = model_fn_ops.estimator_spec()
self.assertEquals_except_export_and_eval_loss(model_fn_ops, estimator_spec)
with session.Session():
regression_output = estimator_spec.export_outputs["regression_head"]
self.assertTrue(isinstance(
regression_output, core_export_lib.RegressionOutput))
self.assertAllEqual(predictions["probabilities"].eval(),
regression_output.value.eval())
def testEstimatorSpec_export_classification(self):
predictions = self.create_predictions()
output_alternatives = {"classification_head": (
constants.ProblemType.CLASSIFICATION, predictions)}
model_fn_ops = self.create_model_fn_ops(
predictions, output_alternatives, mode=model_fn.ModeKeys.INFER)
estimator_spec = model_fn_ops.estimator_spec()
self.assertEquals_except_export_and_eval_loss(model_fn_ops, estimator_spec)
with session.Session():
classification_output = estimator_spec.export_outputs[
"classification_head"]
self.assertTrue(isinstance(classification_output,
core_export_lib.ClassificationOutput))
self.assertAllEqual(predictions["scores"].eval(),
classification_output.scores.eval())
self.assertAllEqual(predictions["classes"].eval(),
classification_output.classes.eval())
def testEstimatorSpec_export_classification_with_missing_scores(self):
predictions = self.create_predictions()
output_alternatives_predictions = predictions.copy()
del output_alternatives_predictions["scores"]
output_alternatives = {"classification_head": (
constants.ProblemType.CLASSIFICATION, output_alternatives_predictions)}
model_fn_ops = self.create_model_fn_ops(
predictions, output_alternatives, mode=model_fn.ModeKeys.INFER)
estimator_spec = model_fn_ops.estimator_spec()
self.assertEquals_except_export_and_eval_loss(model_fn_ops, estimator_spec)
with session.Session():
classification_output = estimator_spec.export_outputs[
"classification_head"]
self.assertTrue(isinstance(classification_output,
core_export_lib.ClassificationOutput))
self.assertAllEqual(predictions["probabilities"].eval(),
classification_output.scores.eval())
self.assertAllEqual(predictions["classes"].eval(),
classification_output.classes.eval())
def testEstimatorSpec_export_classification_with_missing_scores_proba(self):
predictions = self.create_predictions()
output_alternatives_predictions = predictions.copy()
del output_alternatives_predictions["scores"]
del output_alternatives_predictions["probabilities"]
output_alternatives = {"classification_head": (
constants.ProblemType.CLASSIFICATION, output_alternatives_predictions)}
model_fn_ops = self.create_model_fn_ops(
predictions, output_alternatives, mode=model_fn.ModeKeys.INFER)
estimator_spec = model_fn_ops.estimator_spec()
self.assertEquals_except_export_and_eval_loss(model_fn_ops, estimator_spec)
with session.Session():
classification_output = estimator_spec.export_outputs[
"classification_head"]
self.assertTrue(isinstance(classification_output,
core_export_lib.ClassificationOutput))
self.assertIsNone(classification_output.scores)
self.assertAllEqual(predictions["classes"].eval(),
classification_output.classes.eval())
def testEstimatorSpec_export_classification_with_missing_classes(self):
predictions = self.create_predictions()
output_alternatives_predictions = predictions.copy()
del output_alternatives_predictions["classes"]
output_alternatives = {"classification_head": (
constants.ProblemType.CLASSIFICATION, output_alternatives_predictions)}
model_fn_ops = self.create_model_fn_ops(
predictions, output_alternatives, mode=model_fn.ModeKeys.INFER)
estimator_spec = model_fn_ops.estimator_spec()
self.assertEquals_except_export_and_eval_loss(model_fn_ops, estimator_spec)
with session.Session():
classification_output = estimator_spec.export_outputs[
"classification_head"]
self.assertTrue(isinstance(classification_output,
core_export_lib.ClassificationOutput))
self.assertAllEqual(predictions["scores"].eval(),
classification_output.scores.eval())
self.assertIsNone(classification_output.classes)
def testEstimatorSpec_export_classification_with_nonstring_classes(self):
predictions = self.create_predictions()
output_alternatives_predictions = predictions.copy()
output_alternatives_predictions["classes"] = constant_op.constant(
[1, 2, 3])
output_alternatives = {"classification_head": (
constants.ProblemType.CLASSIFICATION, output_alternatives_predictions)}
model_fn_ops = self.create_model_fn_ops(
predictions, output_alternatives, mode=model_fn.ModeKeys.INFER)
estimator_spec = model_fn_ops.estimator_spec()
self.assertEquals_except_export_and_eval_loss(model_fn_ops, estimator_spec)
with session.Session():
classification_output = estimator_spec.export_outputs[
"classification_head"]
self.assertTrue(isinstance(classification_output,
core_export_lib.ClassificationOutput))
self.assertAllEqual(predictions["scores"].eval(),
classification_output.scores.eval())
self.assertIsNone(classification_output.classes)
def testEstimatorSpec_export_logistic(self):
predictions = self.create_predictions()
output_alternatives = {"logistic_head": (
constants.ProblemType.LOGISTIC_REGRESSION, predictions)}
model_fn_ops = self.create_model_fn_ops(
predictions, output_alternatives, mode=model_fn.ModeKeys.INFER)
estimator_spec = model_fn_ops.estimator_spec()
self.assertEquals_except_export_and_eval_loss(model_fn_ops, estimator_spec)
with session.Session():
logistic_output = estimator_spec.export_outputs["logistic_head"]
self.assertTrue(isinstance(logistic_output,
core_export_lib.ClassificationOutput))
self.assertAllEqual(predictions["scores"].eval(),
logistic_output.scores.eval())
self.assertAllEqual(predictions["classes"].eval(),
logistic_output.classes.eval())
def testEstimatorSpec_export_unspecified(self):
predictions = self.create_predictions()
output_alternatives = {"unspecified_head": (
constants.ProblemType.UNSPECIFIED, predictions)}
model_fn_ops = self.create_model_fn_ops(
predictions, output_alternatives, mode=model_fn.ModeKeys.INFER)
estimator_spec = model_fn_ops.estimator_spec()
self.assertEquals_except_export_and_eval_loss(model_fn_ops, estimator_spec)
with session.Session():
unspecified_output = estimator_spec.export_outputs["unspecified_head"]
self.assertTrue(isinstance(unspecified_output,
core_export_lib.PredictOutput))
self.assertEqual(predictions, unspecified_output.outputs)
def testEstimatorSpec_export_multihead(self):
predictions = self.create_predictions()
output_alternatives = {
"regression_head": (
constants.ProblemType.LINEAR_REGRESSION, predictions),
"classification_head": (
constants.ProblemType.CLASSIFICATION, predictions)}
model_fn_ops = self.create_model_fn_ops(
predictions, output_alternatives, mode=model_fn.ModeKeys.INFER)
estimator_spec = model_fn_ops.estimator_spec("regression_head")
self.assertEquals_except_export_and_eval_loss(model_fn_ops, estimator_spec)
with session.Session():
regression_output = estimator_spec.export_outputs["regression_head"]
self.assertTrue(isinstance(
regression_output, core_export_lib.RegressionOutput))
self.assertAllEqual(predictions["scores"].eval(),
regression_output.value.eval())
default_output = estimator_spec.export_outputs[
signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY]
self.assertTrue(isinstance(default_output,
core_export_lib.RegressionOutput))
self.assertAllEqual(predictions["scores"].eval(),
default_output.value.eval())
if __name__ == "__main__":
test.main()
|
