forked from tensorflow/models
-
Notifications
You must be signed in to change notification settings - Fork 0
/
standard_runner_test.py
152 lines (110 loc) · 4.94 KB
/
standard_runner_test.py
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
# Copyright 2021 The Orbit 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 orbit.standard_runner."""
from absl.testing import parameterized
from orbit import standard_runner
from orbit import utils
import tensorflow as tf
def dataset_fn(input_context=None):
del input_context
def dummy_data(_):
return tf.zeros((1, 1), dtype=tf.float32)
dataset = tf.data.Dataset.range(1)
dataset = dataset.repeat()
dataset = dataset.map(
dummy_data, num_parallel_calls=tf.data.experimental.AUTOTUNE)
return dataset
class TestTrainer(standard_runner.StandardTrainer):
"""A StandardTrainer subclass for tests."""
def __init__(self, options=None):
self.strategy = tf.distribute.get_strategy()
self.global_step = utils.create_global_step()
dataset = self.strategy.distribute_datasets_from_function(dataset_fn)
super().__init__(train_dataset=dataset, options=options)
def train_loop_begin(self):
self.global_step.assign(0)
def train_step(self, iterator):
def replica_step(_):
self.global_step.assign_add(1)
self.strategy.run(replica_step, args=(next(iterator),))
def train_loop_end(self):
return self.global_step.numpy()
class TestEvaluator(standard_runner.StandardEvaluator):
"""A StandardEvaluator subclass for tests."""
def __init__(self, options=None):
self.strategy = tf.distribute.get_strategy()
self.global_step = utils.create_global_step()
dataset = self.strategy.distribute_datasets_from_function(dataset_fn)
super().__init__(eval_dataset=dataset, options=options)
def eval_begin(self):
self.global_step.assign(0)
def eval_step(self, iterator):
def replica_step(_):
self.global_step.assign_add(1)
self.strategy.run(replica_step, args=(next(iterator),))
def eval_end(self):
return self.global_step.numpy()
class TestEvaluatorWithOutputsAggregation(standard_runner.StandardEvaluator):
"""A StandardEvaluator subclass for tests."""
def __init__(self, options=None):
self.strategy = tf.distribute.get_strategy()
dataset = self.strategy.distribute_datasets_from_function(
lambda _: tf.data.Dataset.range(10))
super().__init__(eval_dataset=dataset, options=options)
def eval_begin(self):
return tf.constant((0.0,))
def eval_reduce(self, state, step_outputs):
state = tf.concat([state, step_outputs], 0)
return state
def eval_step(self, iterator):
def replica_step(x):
x = tf.cast(x, tf.float32)
return tf.reduce_sum(x)
return self.strategy.experimental_local_results(
self.strategy.run(replica_step, args=(next(iterator),)))
def eval_end(self, outputs):
return tf.reduce_sum(outputs)
class StandardRunnerTest(parameterized.TestCase):
def test_default_trainer(self):
trainer = TestTrainer()
self.assertEqual(trainer.train(tf.constant(10)), 10)
def test_trainer_with_tpu_summary_optimization(self):
options = standard_runner.StandardTrainerOptions(
use_tpu_summary_optimization=True)
trainer = TestTrainer(options)
self.assertEqual(trainer.train(tf.constant(10)), 10)
@parameterized.named_parameters(("use_tf_while_loop", True), ("", False))
def test_default_evaluator(self, use_tf_while_loop):
options = standard_runner.StandardEvaluatorOptions(
use_tf_while_loop=use_tf_while_loop)
evaluator = TestEvaluator(options)
self.assertEqual(evaluator.evaluate(tf.constant(10)), 10)
@parameterized.named_parameters(("use_tf_while_loop", True), ("", False))
def test_evaluator_with_outputs_aggregation(self, use_tf_while_loop):
options = standard_runner.StandardEvaluatorOptions(
use_tf_while_loop=use_tf_while_loop)
evaluator = TestEvaluatorWithOutputsAggregation(options)
self.assertEqual(evaluator.evaluate(tf.constant(10)), 45)
@parameterized.named_parameters(
("recreate_iterator_for_each_eval", True, 10, 10),
("not_recreate_iterator_for_each_eval", False, 10, 35))
def test_evaluator_with_repeat_dataset(self, recreate_iterator_for_each_eval,
sum_for_1st_time, sum_for_2nd_time):
options = standard_runner.StandardEvaluatorOptions(
recreate_iterator_for_each_eval=recreate_iterator_for_each_eval)
evaluator = TestEvaluatorWithOutputsAggregation(options)
self.assertEqual(evaluator.evaluate(tf.constant(5)), sum_for_1st_time)
self.assertEqual(evaluator.evaluate(tf.constant(5)), sum_for_2nd_time)
if __name__ == "__main__":
tf.test.main()