def testManagedMainErrorTwoQueues(self):
# Tests that the supervisor correctly raises a main loop
# error even when using multiple queues for input.
logdir = self._test_dir("managed_main_error_two_queues")
os.makedirs(logdir)
data_path = self._csv_data(logdir)
with self.assertRaisesRegexp(RuntimeError, "fail at step 3"):
with ops.Graph().as_default():
# Create an input pipeline that reads the file 3 times.
filename_queue = input_lib.string_input_producer([data_path],
num_epochs=3)
reader = io_ops.TextLineReader()
_, csv = reader.read(filename_queue)
rec = parsing_ops.decode_csv(csv,
record_defaults=[[1], [1], [1]])
shuff_rec = input_lib.shuffle_batch(rec, 1, 6, 4)
sv = supervisor.Supervisor(logdir=logdir)
with sv.managed_session("") as sess:
for step in range(9):
if sv.should_stop():
break
elif step == 3:
raise RuntimeError("fail at step 3")
else:
sess.run(shuff_rec)
def fn(tensors, scope):
return input.shuffle_batch(
tensors,
batch_size=batch_size,
num_threads=num_threads,
capacity=capacity,
enqueue_many=enqueue_many,
min_after_dequeue=min_after_dequeue,
seed=seed,
allow_smaller_final_batch=allow_smaller_final_batch,
name=scope)
def fn(tensors, scope):
return input.shuffle_batch(
tensors,
batch_size=batch_size,
num_threads=num_threads,
capacity=capacity,
enqueue_many=enqueue_many,
min_after_dequeue=min_after_dequeue,
seed=seed,
allow_smaller_final_batch=allow_smaller_final_batch,
name=scope)
def _apply_transform(self, transform_input, **kwargs):
batched = input_ops.shuffle_batch(transform_input,
batch_size=self.batch_size,
capacity=self.queue_capacity,
min_after_dequeue=self.min_after_dequeue,
num_threads=self.num_threads,
seed=self.seed,
enqueue_many=True)
# TODO(jamieas): batch will soon return a list regardless of the number of
# enqueued tensors. Remove the following once that change is in place.
if not isinstance(batched, (tuple, list)):
batched = (batched,)
# pylint: disable=not-callable
return self.return_type(*batched)
def _apply_transform(self, transform_input):
batched = input_ops.shuffle_batch(transform_input,
batch_size=self.batch_size,
capacity=self.queue_capacity,
min_after_dequeue=self.min_after_dequeue,
num_threads=self.num_threads,
seed=self.seed,
enqueue_many=True)
# TODO(jamieas): batch will soon return a list regardless of the number of
# enqueued tensors. Remove the following once that change is in place.
if not isinstance(batched, (tuple, list)):
batched = (batched,)
# pylint: disable=not-callable
return self.return_type(*batched)
def testManagedEndOfInputTwoQueues(self):
# Tests that the supervisor finishes without an error when using
# a fixed number of epochs, reading from two queues, the second
# one producing a batch from the first one.
logdir = self._test_dir("managed_end_of_input_two_queues")
os.makedirs(logdir)
data_path = self._csv_data(logdir)
with ops.Graph().as_default():
# Create an input pipeline that reads the file 3 times.
filename_queue = input_lib.string_input_producer(
[data_path], num_epochs=3)
reader = io_ops.TextLineReader()
_, csv = reader.read(filename_queue)
rec = parsing_ops.decode_csv(csv, record_defaults=[[1], [1], [1]])
shuff_rec = input_lib.shuffle_batch(rec, 1, 6, 4)
sv = supervisor.Supervisor(logdir=logdir)
with sv.managed_session("") as sess:
while not sv.should_stop():
sess.run(shuff_rec)
def testManagedEndOfInputTwoQueues(self):
# Tests that the supervisor finishes without an error when using
# a fixed number of epochs, reading from two queues, the second
# one producing a batch from the first one.
logdir = self._test_dir("managed_end_of_input_two_queues")
os.makedirs(logdir)
data_path = self._csv_data(logdir)
with ops.Graph().as_default():
# Create an input pipeline that reads the file 3 times.
filename_queue = input_lib.string_input_producer([data_path],
num_epochs=3)
reader = io_ops.TextLineReader()
_, csv = reader.read(filename_queue)
rec = parsing_ops.decode_csv(csv, record_defaults=[[1], [1], [1]])
shuff_rec = input_lib.shuffle_batch(rec, 1, 6, 4)
sv = supervisor.Supervisor(logdir=logdir)
with sv.managed_session("") as sess:
while not sv.should_stop():
sess.run(shuff_rec)
def testManagedMainErrorTwoQueues(self):
# Tests that the supervisor correctly raises a main loop
# error even when using multiple queues for input.
logdir = self._test_dir("managed_main_error_two_queues")
os.makedirs(logdir)
data_path = self._csv_data(logdir)
with self.assertRaisesRegexp(RuntimeError, "fail at step 3"):
with ops.Graph().as_default():
# Create an input pipeline that reads the file 3 times.
filename_queue = input_lib.string_input_producer(
[data_path], num_epochs=3)
reader = io_ops.TextLineReader()
_, csv = reader.read(filename_queue)
rec = parsing_ops.decode_csv(csv, record_defaults=[[1], [1], [1]])
shuff_rec = input_lib.shuffle_batch(rec, 1, 6, 4)
sv = supervisor.Supervisor(logdir=logdir)
with sv.managed_session("") as sess:
for step in range(9):
if sv.should_stop():
break
elif step == 3:
raise RuntimeError("fail at step 3")
else:
sess.run(shuff_rec)
def read_batch_examples(file_pattern,
batch_size,
reader,
randomize_input=True,
queue_capacity=10000,
num_threads=1,
name='dequeue_examples'):
"""Adds operations to read, queue, batch `Example` protos.
Given file pattern (or list of files), will setup a queue for file names,
read `Example` proto using provided `reader`, use batch queue to create
batches of examples of size `batch_size`.
All queue runners are added to the queue runners collection, and may be
started via `start_queue_runners`.
All ops are added to the default graph.
Args:
file_pattern: List of files or pattern of file paths containing
`Example` records. See `tf.gfile.Glob` for pattern rules.
batch_size: An int or scalar `Tensor` specifying the batch size to use.
reader: A function or class that returns an object with
`read` method, (filename tensor) -> (example tensor).
randomize_input: Whether the input should be randomized.
queue_capacity: Capacity for input queue.
num_threads: The number of threads enqueuing examples.
name: Name of resulting op.
Returns:
String `Tensor` of batched `Example` proto.
Raises:
ValueError: for invalid inputs.
"""
# Retrive files to read.
if isinstance(file_pattern, list):
file_names = file_pattern
if not file_names:
raise ValueError('No files given to dequeue_examples.')
else:
file_names = list(gfile.Glob(file_pattern))
if not file_names:
raise ValueError('No files match %s.' % file_pattern)
# Sort files so it will be deterministic for unit tests. They'll be shuffled
# in `string_input_producer` if `randomize_input` is enabled.
if not randomize_input:
file_names = sorted(file_names)
# Check input parameters are given and reasonable.
if (not queue_capacity) or (queue_capacity <= 0):
raise ValueError('Invalid queue_capacity %s.' % queue_capacity)
if (batch_size is None) or (
(not isinstance(batch_size, ops.Tensor)) and
(batch_size <= 0 or batch_size > queue_capacity)):
raise ValueError('Invalid batch_size %s, with queue_capacity %s.' %
(batch_size, queue_capacity))
if (not num_threads) or (num_threads <= 0):
raise ValueError('Invalid num_threads %s.' % num_threads)
with ops.name_scope(name) as scope:
# Setup filename queue with shuffling.
with ops.name_scope('file_name_queue') as file_name_queue_scope:
file_name_queue = input_ops.string_input_producer(
constant_op.constant(file_names, name='input'),
shuffle=randomize_input,
name=file_name_queue_scope)
# Create reader and set it to read from filename queue.
with ops.name_scope('read'):
_, example_proto = reader().read(file_name_queue)
# Setup batching queue.
if randomize_input:
if isinstance(batch_size, ops.Tensor):
min_after_dequeue = int(queue_capacity * 0.4)
else:
min_after_dequeue = max(queue_capacity - (3 * batch_size),
batch_size)
examples = input_ops.shuffle_batch(
[example_proto],
batch_size,
capacity=queue_capacity,
num_threads=num_threads,
min_after_dequeue=min_after_dequeue,
name=scope)
else:
examples = input_ops.batch([example_proto],
batch_size,
capacity=queue_capacity,
num_threads=num_threads,
name=scope)
return examples
def read_batch_examples(file_pattern, batch_size, reader,
randomize_input=True, queue_capacity=10000,
num_threads=1, name='dequeue_examples'):
"""Adds operations to read, queue, batch `Example` protos.
Given file pattern (or list of files), will setup a queue for file names,
read `Example` proto using provided `reader`, use batch queue to create
batches of examples of size `batch_size`.
All queue runners are added to the queue runners collection, and may be
started via `start_queue_runners`.
All ops are added to the default graph.
Args:
file_pattern: List of files or pattern of file paths containing
`Example` records. See `tf.gfile.Glob` for pattern rules.
batch_size: An int or scalar `Tensor` specifying the batch size to use.
reader: A function or class that returns an object with
`read` method, (filename tensor) -> (example tensor).
randomize_input: Whether the input should be randomized.
queue_capacity: Capacity for input queue.
num_threads: The number of threads enqueuing examples.
name: Name of resulting op.
Returns:
String `Tensor` of batched `Example` proto.
Raises:
ValueError: for invalid inputs.
"""
# Retrive files to read.
if isinstance(file_pattern, list):
file_names = file_pattern
if not file_names:
raise ValueError('No files given to dequeue_examples.')
else:
file_names = list(gfile.Glob(file_pattern))
if not file_names:
raise ValueError('No files match %s.' % file_pattern)
# Sort files so it will be deterministic for unit tests. They'll be shuffled
# in `string_input_producer` if `randomize_input` is enabled.
if not randomize_input:
file_names = sorted(file_names)
# Check input parameters are given and reasonable.
if (not queue_capacity) or (queue_capacity <= 0):
raise ValueError('Invalid queue_capacity %s.' % queue_capacity)
if (batch_size is None) or (
(not isinstance(batch_size, ops.Tensor)) and
(batch_size <= 0 or batch_size > queue_capacity)):
raise ValueError(
'Invalid batch_size %s, with queue_capacity %s.' %
(batch_size, queue_capacity))
if (not num_threads) or (num_threads <= 0):
raise ValueError('Invalid num_threads %s.' % num_threads)
with ops.name_scope(name) as scope:
# Setup filename queue with shuffling.
with ops.name_scope('file_name_queue') as file_name_queue_scope:
file_name_queue = input_ops.string_input_producer(
constant_op.constant(file_names, name='input'),
shuffle=randomize_input, name=file_name_queue_scope)
# Create reader and set it to read from filename queue.
with ops.name_scope('read'):
_, example_proto = reader().read(file_name_queue)
# Setup batching queue.
if randomize_input:
if isinstance(batch_size, ops.Tensor):
min_after_dequeue = int(queue_capacity * 0.4)
else:
min_after_dequeue = max(queue_capacity - (3 * batch_size), batch_size)
examples = input_ops.shuffle_batch(
[example_proto], batch_size, capacity=queue_capacity,
num_threads=num_threads, min_after_dequeue=min_after_dequeue,
name=scope)
else:
examples = input_ops.batch(
[example_proto], batch_size, capacity=queue_capacity,
num_threads=num_threads, name=scope)
return examples