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
|
# 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.
# ==============================================================================
"""Benchmarks FilterDataset input pipeline op."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import time
import numpy as np
from tensorflow.python.client import session
from tensorflow.python.data.experimental.ops import optimization
from tensorflow.python.data.ops import dataset_ops
from tensorflow.python.framework import ops
from tensorflow.python.ops import math_ops
from tensorflow.python.platform import test
class FilterBenchmark(test.Benchmark):
# This benchmark compares the performance of pipeline with multiple chained
# filter with and without filter fusion.
def benchmarkFilters(self):
chain_lengths = [0, 1, 2, 5, 10, 20, 50]
for chain_length in chain_lengths:
self._benchmarkFilters(chain_length, False)
self._benchmarkFilters(chain_length, True)
def _benchmarkFilters(self, chain_length, optimize_dataset):
with ops.Graph().as_default():
dataset = dataset_ops.Dataset.from_tensors(5).repeat(None)
for _ in range(chain_length):
dataset = dataset.filter(lambda x: math_ops.greater_equal(x - 5, 0))
if optimize_dataset:
dataset = dataset.apply(optimization.optimize(["filter_fusion"]))
iterator = dataset.make_one_shot_iterator()
next_element = iterator.get_next()
with session.Session() as sess:
for _ in range(10):
sess.run(next_element.op)
deltas = []
for _ in range(100):
start = time.time()
for _ in range(100):
sess.run(next_element.op)
end = time.time()
deltas.append(end - start)
median_wall_time = np.median(deltas) / 100
opt_mark = "opt" if optimize_dataset else "no-opt"
print("Filter dataset {} chain length: {} Median wall time: {}".format(
opt_mark, chain_length, median_wall_time))
self.report_benchmark(
iters=1000,
wall_time=median_wall_time,
name="benchmark_filter_dataset_chain_latency_{}_{}".format(
opt_mark, chain_length))
if __name__ == "__main__":
test.main()
|
