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# 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.
# ==============================================================================
"""Tests for the experimental input pipeline ops."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import time
import numpy as np
from tensorflow.python.data.experimental.ops import batching
from tensorflow.python.data.experimental.ops import optimization
from tensorflow.python.data.kernel_tests import test_base
from tensorflow.python.data.ops import dataset_ops
from tensorflow.python.ops import math_ops
from tensorflow.python.platform import test
class ModelDatasetTest(test_base.DatasetTestBase):
def testModelMap(self):
k = 1024 * 1024
dataset = dataset_ops.Dataset.from_tensors((np.random.rand(1, 4 * k),
np.random.rand(4 * k,
1))).repeat()
dataset = dataset.map(math_ops.matmul)
options = dataset_ops.Options()
options.experimental_autotune = True
iterator = dataset.with_options(options).make_one_shot_iterator()
get_next = iterator.get_next()
deltas = []
with self.cached_session() as sess:
for _ in range(5):
sess.run(get_next.op)
for _ in range(100):
start = time.time()
sess.run(get_next.op)
end = time.time()
deltas.append(end - start)
print("%f (median), %f (mean), %f (stddev), %f (min), %f (max)\n" %
(np.median(deltas), np.mean(deltas), np.std(deltas), np.min(deltas),
np.max(deltas)))
def testModelParallelMap(self):
k = 1024 * 1024
dataset = dataset_ops.Dataset.from_tensors((np.random.rand(1, 4 * k),
np.random.rand(4 * k,
1))).repeat()
dataset = dataset.map(
math_ops.matmul, num_parallel_calls=optimization.AUTOTUNE)
options = dataset_ops.Options()
options.experimental_autotune = True
iterator = dataset.with_options(options).make_one_shot_iterator()
get_next = iterator.get_next()
deltas = []
with self.cached_session() as sess:
for _ in range(5):
sess.run(get_next.op)
for _ in range(1000):
start = time.time()
sess.run(get_next.op)
end = time.time()
deltas.append(end - start)
print("%f (median), %f (mean), %f (stddev), %f (min), %f (max)\n" %
(np.median(deltas), np.mean(deltas), np.std(deltas), np.min(deltas),
np.max(deltas)))
def testModelMapAndBatch(self):
batch_size = 16
k = 1024 * 1024
dataset = dataset_ops.Dataset.from_tensors((np.random.rand(1, 4 * k),
np.random.rand(4 * k,
1))).repeat()
dataset = dataset.apply(
batching.map_and_batch(
math_ops.matmul,
num_parallel_calls=optimization.AUTOTUNE,
batch_size=batch_size))
options = dataset_ops.Options()
options.experimental_autotune = True
iterator = dataset.with_options(options).make_one_shot_iterator()
get_next = iterator.get_next()
deltas = []
with self.cached_session() as sess:
for _ in range(5):
sess.run(get_next.op)
for _ in range(10):
start = time.time()
sess.run(get_next.op)
end = time.time()
deltas.append(end - start)
print("%f (median), %f (mean), %f (stddev), %f (min), %f (max)\n" %
(np.median(deltas), np.mean(deltas), np.std(deltas), np.min(deltas),
np.max(deltas)))
def testModelParallelInterleave(self):
k = 1024 * 1024
dataset = dataset_ops.Dataset.from_tensors((np.random.rand(1, 4 * k),
np.random.rand(4 * k,
1))).repeat()
dataset = dataset.map(math_ops.matmul)
dataset = dataset_ops.Dataset.range(1).repeat().interleave(
lambda _: dataset,
cycle_length=10,
num_parallel_calls=optimization.AUTOTUNE)
options = dataset_ops.Options()
options.experimental_autotune = True
iterator = dataset.with_options(options).make_one_shot_iterator()
get_next = iterator.get_next()
deltas = []
with self.cached_session() as sess:
for _ in range(5):
sess.run(get_next.op)
for _ in range(1000):
start = time.time()
sess.run(get_next.op)
end = time.time()
deltas.append(end - start)
print("%f (median), %f (mean), %f (stddev), %f (min), %f (max)\n" %
(np.median(deltas), np.mean(deltas), np.std(deltas), np.min(deltas),
np.max(deltas)))
def testModelNested(self):
k = 1024 * 1024
a = (np.random.rand(1, 8 * k), np.random.rand(8 * k, 1))
b = (np.random.rand(1, 4 * k), np.random.rand(4 * k, 1))
c = (np.random.rand(1, 2 * k), np.random.rand(2 * k, 1))
dataset = dataset_ops.Dataset.from_tensors((a, b, c)).repeat()
def f1(a, b, c):
x, y = a
return math_ops.matmul(x, y), b, c
def f2(a, b, c):
x, y = b
return a, math_ops.matmul(x, y), c
def f3(a, b, c):
x, y = c
return a, b, math_ops.matmul(x, y)
dataset = dataset.map(f1, num_parallel_calls=optimization.AUTOTUNE)
dataset = dataset_ops.Dataset.range(1).repeat().interleave(
lambda _: dataset, cycle_length=2)
dataset = dataset.map(f2, num_parallel_calls=optimization.AUTOTUNE)
dataset = dataset_ops.Dataset.range(1).repeat().interleave(
lambda _: dataset, cycle_length=2)
dataset = dataset.map(f3, num_parallel_calls=optimization.AUTOTUNE)
options = dataset_ops.Options()
options.experimental_autotune = True
iterator = dataset.with_options(options).make_one_shot_iterator()
get_next = iterator.get_next()
deltas = []
with self.cached_session() as sess:
for _ in range(5):
sess.run(get_next)
for _ in range(100):
start = time.time()
sess.run(get_next)
end = time.time()
deltas.append(end - start)
print("%f (median), %f (mean), %f (stddev), %f (min), %f (max)\n" %
(np.median(deltas), np.mean(deltas), np.std(deltas), np.min(deltas),
np.max(deltas)))
if __name__ == "__main__":
test.main()
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