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# 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 slim.data.prefetch_queue."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
from tensorflow.contrib.slim.python.slim.data import prefetch_queue
from tensorflow.python.framework import constant_op
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import errors_impl
from tensorflow.python.framework import ops
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import data_flow_ops
from tensorflow.python.ops import random_ops
from tensorflow.python.ops import variables
from tensorflow.python.platform import test
from tensorflow.python.training import input as input_lib
from tensorflow.python.training import queue_runner_impl
class PrefetchQueueTest(test.TestCase):
def testOneThread(self):
with self.cached_session() as sess:
batch_size = 10
image_size = 32
num_batches = 5
zero64 = constant_op.constant(0, dtype=dtypes.int64)
examples = variables.Variable(zero64)
counter = examples.count_up_to(num_batches * batch_size)
image = random_ops.random_normal(
[image_size, image_size, 3], dtype=dtypes.float32, name='images')
label = random_ops.random_uniform(
[1], 0, 10, dtype=dtypes.int32, name='labels')
batches = input_lib.batch(
[counter, image, label], batch_size=batch_size, num_threads=1)
batches = prefetch_queue.prefetch_queue(batches).dequeue()
variables.global_variables_initializer().run()
threads = queue_runner_impl.start_queue_runners()
for i in range(num_batches):
results = sess.run(batches)
self.assertAllEqual(results[0],
np.arange(i * batch_size, (i + 1) * batch_size))
self.assertEquals(results[1].shape,
(batch_size, image_size, image_size, 3))
self.assertEquals(results[2].shape, (batch_size, 1))
# Reached the limit.
with self.assertRaises(errors_impl.OutOfRangeError):
sess.run(batches)
for thread in threads:
thread.join()
def testMultiThread(self):
with self.cached_session() as sess:
batch_size = 10
image_size = 32
num_batches = 5
zero64 = constant_op.constant(0, dtype=dtypes.int64)
examples = variables.Variable(zero64)
counter = examples.count_up_to(num_batches * batch_size)
image = random_ops.random_normal(
[image_size, image_size, 3], dtype=dtypes.float32, name='images')
label = random_ops.random_uniform(
[1], 0, 10, dtype=dtypes.int32, name='labels')
batches = input_lib.batch(
[counter, image, label], batch_size=batch_size, num_threads=4)
batches = prefetch_queue.prefetch_queue(batches).dequeue()
variables.global_variables_initializer().run()
threads = queue_runner_impl.start_queue_runners()
value_counter = []
for _ in range(num_batches):
results = sess.run(batches)
value_counter.append(results[0])
self.assertEqual(results[1].shape,
(batch_size, image_size, image_size, 3))
self.assertEqual(results[2].shape, (batch_size, 1))
self.assertAllEqual(
np.sort(np.concatenate(value_counter)),
np.arange(0, num_batches * batch_size))
# Reached the limit.
with self.assertRaises(errors_impl.OutOfRangeError):
sess.run(batches)
for thread in threads:
thread.join()
def testMultipleDequeue(self):
with self.cached_session() as sess:
batch_size = 10
image_size = 32
num_batches = 4
zero64 = constant_op.constant(0, dtype=dtypes.int64)
examples = variables.Variable(zero64)
counter = examples.count_up_to(num_batches * batch_size)
image = random_ops.random_normal(
[image_size, image_size, 3], dtype=dtypes.float32, name='images')
label = random_ops.random_uniform(
[1], 0, 10, dtype=dtypes.int32, name='labels')
batches = input_lib.batch(
[counter, image, label], batch_size=batch_size, num_threads=4)
batcher = prefetch_queue.prefetch_queue(batches)
batches_list = [batcher.dequeue() for _ in range(2)]
variables.global_variables_initializer().run()
threads = queue_runner_impl.start_queue_runners()
value_counter = []
for _ in range(int(num_batches / 2)):
for batches in batches_list:
results = sess.run(batches)
value_counter.append(results[0])
self.assertEquals(results[1].shape,
(batch_size, image_size, image_size, 3))
self.assertEquals(results[2].shape, (batch_size, 1))
self.assertAllEqual(
np.sort(np.concatenate(value_counter)),
np.arange(0, num_batches * batch_size))
# Reached the limit.
with self.assertRaises(errors_impl.OutOfRangeError):
sess.run(batches)
for thread in threads:
thread.join()
def testDynamicPad_failure(self):
with ops.Graph().as_default():
variable_tensor = array_ops.placeholder(dtypes.int32, shape=[None, 3])
with self.assertRaisesRegexp(ValueError, 'shapes must be fully defined'):
prefetch_queue.prefetch_queue([variable_tensor])
def testDynamicPad(self):
with self.cached_session() as sess:
# Create 3 tensors of variable but compatible shapes.
var_shape = [None, 2]
p1 = constant_op.constant([[1, 2], [3, 4]])
p1.set_shape(var_shape)
p2 = constant_op.constant([[5, 6], [7, 8], [9, 10]])
p2.set_shape(var_shape)
p3 = constant_op.constant([[11, 12]])
p3.set_shape(var_shape)
batch = [p1, p2, p3]
batch_size = len(batch)
zero64 = constant_op.constant(0, dtype=dtypes.int64)
examples = variables.Variable(zero64)
counter = examples.count_up_to(batch_size)
# Create a PaddingFIFOQueue to enqueue these tensors.
q = data_flow_ops.PaddingFIFOQueue(
capacity=10, dtypes=[dtypes.int32], shapes=[var_shape])
for tensor in [p1, p2, p3]:
q.enqueue([tensor]).run()
# Dequeue from the queue and batch them using batch().
batches = input_lib.batch([q.dequeue(), counter], batch_size=batch_size,
num_threads=1, dynamic_pad=True)
self.assertEqual([batch_size, None, 2], batches[0].shape.as_list())
# Finally, assemble them into prefetch_queue with dynamic_pad.
batcher = prefetch_queue.prefetch_queue(batches, dynamic_pad=True)
batches = batcher.dequeue()
self.assertEqual([batch_size, None, 2], batches[0].shape.as_list())
variables.global_variables_initializer().run()
threads = queue_runner_impl.start_queue_runners()
values, _ = sess.run(batches)
# We enqueued 3 tensors of [None, 2] shapes, so using dynamic_pad
# they should be padded to the fixed size [3, 3, 2], where 3
# is the maximum length of the batch.
self.assertTrue(np.array_equal(
np.array([[[1, 2], [3, 4], [0, 0]],
[[5, 6], [7, 8], [9, 10]],
[[11, 12], [0, 0], [0, 0]]]),
values))
with self.assertRaises(errors_impl.OutOfRangeError):
sess.run(batches)
for thread in threads:
thread.join()
def testDictConstruction(self):
with ops.Graph().as_default():
batches = {
'first': constant_op.constant([1]),
'second': constant_op.constant([2.0, 2.1])
}
prefetcher = prefetch_queue.prefetch_queue(batches)
dequeued = prefetcher.dequeue()
self.assertTrue(isinstance(dequeued, dict))
self.assertEqual(2, len(dequeued))
self.assertEqual(dtypes.int32, dequeued['first'].dtype)
self.assertEqual(dtypes.float32, dequeued['second'].dtype)
if __name__ == '__main__':
test.main()
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