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# Copyright 2017 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.
#==============================================================================
"""Data Flow Operations."""
# pylint: disable=g-bad-name
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import collections
import hashlib
import threading
import six
from tensorflow.python.eager import context
from tensorflow.python.framework import dtypes as _dtypes
from tensorflow.python.framework import ops
from tensorflow.python.framework import random_seed
from tensorflow.python.framework import tensor_shape
from tensorflow.python.framework import tensor_util
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import control_flow_ops
from tensorflow.python.ops import gen_data_flow_ops
from tensorflow.python.ops import math_ops
# go/tf-wildcard-import
# pylint: disable=wildcard-import
from tensorflow.python.ops.gen_data_flow_ops import *
# pylint: enable=wildcard-import
def _as_type_list(dtypes):
"""Convert dtypes to a list of types."""
assert dtypes is not None
if not (isinstance(dtypes, list) or isinstance(dtypes, tuple)):
# We have a single type.
return [dtypes]
else:
# We have a list or tuple of types.
return list(dtypes)
def _as_shape_list(shapes, dtypes, unknown_dim_allowed=False,
unknown_rank_allowed=False):
"""Convert shapes to a list of tuples of int (or None)."""
del dtypes
if unknown_dim_allowed:
if (not isinstance(shapes, collections.Sequence)
or not shapes
or any(shape is None or isinstance(shape, int) for shape in shapes)):
raise ValueError(
"When providing partial shapes, a list of shapes must be provided.")
if shapes is None: return None
if isinstance(shapes, tensor_shape.TensorShape):
shapes = [shapes]
if not isinstance(shapes, (tuple, list)):
raise TypeError(
"shapes must be a TensorShape or a list or tuple of TensorShapes.")
if all(shape is None or isinstance(shape, int) for shape in shapes):
# We have a single shape.
shapes = [shapes]
shapes = [tensor_shape.as_shape(shape) for shape in shapes]
if not unknown_dim_allowed:
if any([not shape.is_fully_defined() for shape in shapes]):
raise ValueError("All shapes must be fully defined: %s" % shapes)
if not unknown_rank_allowed:
if any([shape.dims is None for shape in shapes]):
raise ValueError("All shapes must have a defined rank: %s" % shapes)
return shapes
def _as_name_list(names, dtypes):
if names is None:
return None
if not isinstance(names, (list, tuple)):
names = [names]
if len(names) != len(dtypes):
raise ValueError("List of names must have the same length as the list "
"of dtypes")
return list(names)
def _shape_common(s1, s2):
"""The greatest lower bound (ordered by specificity) TensorShape."""
s1 = tensor_shape.TensorShape(s1)
s2 = tensor_shape.TensorShape(s2)
if s1.ndims is None or s2.ndims is None or s1.ndims != s2.ndims:
return tensor_shape.unknown_shape()
d = [
d1 if d1 is not None and d1 == d2 else None
for (d1, d2) in zip(s1.as_list(), s2.as_list())]
return tensor_shape.TensorShape(d)
# pylint: disable=protected-access
class QueueBase(object):
"""Base class for queue implementations.
A queue is a TensorFlow data structure that stores tensors across
multiple steps, and exposes operations that enqueue and dequeue
tensors.
Each queue element is a tuple of one or more tensors, where each
tuple component has a static dtype, and may have a static shape. The
queue implementations support versions of enqueue and dequeue that
handle single elements, versions that support enqueuing and
dequeuing a batch of elements at once.
See @{tf.FIFOQueue} and
@{tf.RandomShuffleQueue} for concrete
implementations of this class, and instructions on how to create
them.
@compatibility(eager)
Queues are not compatible with eager execution. Instead, please
use `tf.data` to get data into your model.
@end_compatibility
"""
def __init__(self, dtypes, shapes, names, queue_ref):
"""Constructs a queue object from a queue reference.
The two optional lists, `shapes` and `names`, must be of the same length
as `dtypes` if provided. The values at a given index `i` indicate the
shape and name to use for the corresponding queue component in `dtypes`.
Args:
dtypes: A list of types. The length of dtypes must equal the number
of tensors in each element.
shapes: Constraints on the shapes of tensors in an element:
A list of shape tuples or None. This list is the same length
as dtypes. If the shape of any tensors in the element are constrained,
all must be; shapes can be None if the shapes should not be constrained.
names: Optional list of names. If provided, the `enqueue()` and
`dequeue()` methods will use dictionaries with these names as keys.
Must be None or a list or tuple of the same length as `dtypes`.
queue_ref: The queue reference, i.e. the output of the queue op.
Raises:
ValueError: If one of the arguments is invalid.
RuntimeError: If eager execution is enabled.
"""
if context.in_eager_mode():
raise RuntimeError(
"Queues are not supported when eager execution is enabled. "
"Instead, please use tf.data to get data into your model.")
self._dtypes = dtypes
if shapes is not None:
if len(shapes) != len(dtypes):
raise ValueError("Queue shapes must have the same length as dtypes")
self._shapes = [tensor_shape.TensorShape(s) for s in shapes]
else:
self._shapes = [tensor_shape.unknown_shape() for _ in self._dtypes]
if names is not None:
if len(names) != len(dtypes):
raise ValueError("Queue names must have the same length as dtypes")
self._names = names
else:
self._names = None
self._queue_ref = queue_ref
if context.in_graph_mode():
self._name = self._queue_ref.op.name.split("/")[-1]
else:
self._name = context.context().scope_name
@staticmethod
def from_list(index, queues):
"""Create a queue using the queue reference from `queues[index]`.
Args:
index: An integer scalar tensor that determines the input that gets
selected.
queues: A list of `QueueBase` objects.
Returns:
A `QueueBase` object.
Raises:
TypeError: When `queues` is not a list of `QueueBase` objects,
or when the data types of `queues` are not all the same.
"""
if ((not queues) or
(not isinstance(queues, list)) or
(not all(isinstance(x, QueueBase) for x in queues))):
raise TypeError("A list of queues expected")
dtypes = queues[0].dtypes
if not all([dtypes == q.dtypes for q in queues[1:]]):
raise TypeError("Queues do not have matching component dtypes.")
names = queues[0].names
if not all([names == q.names for q in queues[1:]]):
raise TypeError("Queues do not have matching component names.")
queue_shapes = [q.shapes for q in queues]
reduced_shapes = [
six.moves.reduce(_shape_common, s) for s in zip(*queue_shapes)]
queue_refs = array_ops.stack([x.queue_ref for x in queues])
selected_queue = array_ops.gather(queue_refs, index)
return QueueBase(dtypes=dtypes, shapes=reduced_shapes, names=names,
queue_ref=selected_queue)
@property
def queue_ref(self):
"""The underlying queue reference."""
return self._queue_ref
@property
def name(self):
"""The name of the underlying queue."""
if context.in_graph_mode():
return self._queue_ref.op.name
return self._name
@property
def dtypes(self):
"""The list of dtypes for each component of a queue element."""
return self._dtypes
@property
def shapes(self):
"""The list of shapes for each component of a queue element."""
return self._shapes
@property
def names(self):
"""The list of names for each component of a queue element."""
return self._names
def _check_enqueue_dtypes(self, vals):
"""Validate and convert `vals` to a list of `Tensor`s.
The `vals` argument can be a Tensor, a list or tuple of tensors, or a
dictionary with tensor values.
If it is a dictionary, the queue must have been constructed with a
`names` attribute and the dictionary keys must match the queue names.
If the queue was constructed with a `names` attribute, `vals` must
be a dictionary.
Args:
vals: A tensor, a list or tuple of tensors, or a dictionary..
Returns:
A list of `Tensor` objects.
Raises:
ValueError: If `vals` is invalid.
"""
if isinstance(vals, dict):
if not self._names:
raise ValueError("Queue must have names to enqueue a dictionary")
if sorted(self._names, key=str) != sorted(vals.keys(), key=str):
raise ValueError("Keys in dictionary to enqueue do not match "
"names of Queue. Dictionary: (%s), Queue: (%s)" %
(sorted(vals.keys()), sorted(self._names)))
# The order of values in `self._names` indicates the order in which the
# tensors in the dictionary `vals` must be listed.
vals = [vals[k] for k in self._names]
else:
if self._names:
raise ValueError("You must enqueue a dictionary in a Queue with names")
if not isinstance(vals, (list, tuple)):
vals = [vals]
tensors = []
for i, (val, dtype) in enumerate(zip(vals, self._dtypes)):
tensors.append(ops.convert_to_tensor(val, dtype=dtype,
name="component_%d" % i))
return tensors
def _scope_vals(self, vals):
"""Return a list of values to pass to `name_scope()`.
Args:
vals: A tensor, a list or tuple of tensors, or a dictionary.
Returns:
The values in vals as a list.
"""
if isinstance(vals, (list, tuple)):
return vals
elif isinstance(vals, dict):
return vals.values()
else:
return [vals]
def enqueue(self, vals, name=None):
"""Enqueues one element to this queue.
If the queue is full when this operation executes, it will block
until the element has been enqueued.
At runtime, this operation may raise an error if the queue is
@{tf.QueueBase.close} before or during its execution. If the
queue is closed before this operation runs,
`tf.errors.CancelledError` will be raised. If this operation is
blocked, and either (i) the queue is closed by a close operation
with `cancel_pending_enqueues=True`, or (ii) the session is
@{tf.Session.close},
`tf.errors.CancelledError` will be raised.
Args:
vals: A tensor, a list or tuple of tensors, or a dictionary containing
the values to enqueue.
name: A name for the operation (optional).
Returns:
The operation that enqueues a new tuple of tensors to the queue.
"""
with ops.name_scope(name, "%s_enqueue" % self._name,
self._scope_vals(vals)) as scope:
vals = self._check_enqueue_dtypes(vals)
# NOTE(mrry): Not using a shape function because we need access to
# the `QueueBase` object.
for val, shape in zip(vals, self._shapes):
val.get_shape().assert_is_compatible_with(shape)
if self._queue_ref.dtype == _dtypes.resource:
return gen_data_flow_ops._queue_enqueue_v2(
self._queue_ref, vals, name=scope)
else:
return gen_data_flow_ops._queue_enqueue(
self._queue_ref, vals, name=scope)
def enqueue_many(self, vals, name=None):
"""Enqueues zero or more elements to this queue.
This operation slices each component tensor along the 0th dimension to
make multiple queue elements. All of the tensors in `vals` must have the
same size in the 0th dimension.
If the queue is full when this operation executes, it will block
until all of the elements have been enqueued.
At runtime, this operation may raise an error if the queue is
@{tf.QueueBase.close} before or during its execution. If the
queue is closed before this operation runs,
`tf.errors.CancelledError` will be raised. If this operation is
blocked, and either (i) the queue is closed by a close operation
with `cancel_pending_enqueues=True`, or (ii) the session is
@{tf.Session.close},
`tf.errors.CancelledError` will be raised.
Args:
vals: A tensor, a list or tuple of tensors, or a dictionary
from which the queue elements are taken.
name: A name for the operation (optional).
Returns:
The operation that enqueues a batch of tuples of tensors to the queue.
"""
with ops.name_scope(name, "%s_EnqueueMany" % self._name,
self._scope_vals(vals)) as scope:
vals = self._check_enqueue_dtypes(vals)
# NOTE(mrry): Not using a shape function because we need access to
# the `QueueBase` object.
batch_dim = vals[0].get_shape().with_rank_at_least(1)[0]
for val, shape in zip(vals, self._shapes):
batch_dim = batch_dim.merge_with(
val.get_shape().with_rank_at_least(1)[0])
val.get_shape()[1:].assert_is_compatible_with(shape)
return gen_data_flow_ops._queue_enqueue_many_v2(
self._queue_ref, vals, name=scope)
def _dequeue_return_value(self, tensors):
"""Return the value to return from a dequeue op.
If the queue has names, return a dictionary with the
names as keys. Otherwise return either a single tensor
or a list of tensors depending on the length of `tensors`.
Args:
tensors: List of tensors from the dequeue op.
Returns:
A single tensor, a list of tensors, or a dictionary
of tensors.
"""
if self._names:
# The returned values in `tensors` are in the same order as
# the names in `self._names`.
return {n: tensors[i] for i, n in enumerate(self._names)}
elif len(tensors) == 1:
return tensors[0]
else:
return tensors
def dequeue(self, name=None):
"""Dequeues one element from this queue.
If the queue is empty when this operation executes, it will block
until there is an element to dequeue.
At runtime, this operation may raise an error if the queue is
@{tf.QueueBase.close} before or during its execution. If the
queue is closed, the queue is empty, and there are no pending
enqueue operations that can fulfill this request,
`tf.errors.OutOfRangeError` will be raised. If the session is
@{tf.Session.close},
`tf.errors.CancelledError` will be raised.
Args:
name: A name for the operation (optional).
Returns:
The tuple of tensors that was dequeued.
"""
if name is None:
name = "%s_Dequeue" % self._name
if self._queue_ref.dtype == _dtypes.resource:
ret = gen_data_flow_ops._queue_dequeue_v2(
self._queue_ref, self._dtypes, name=name)
else:
ret = gen_data_flow_ops._queue_dequeue(
self._queue_ref, self._dtypes, name=name)
# NOTE(mrry): Not using a shape function because we need access to
# the `QueueBase` object.
if context.in_graph_mode():
op = ret[0].op
for output, shape in zip(op.values(), self._shapes):
output.set_shape(shape)
return self._dequeue_return_value(ret)
def dequeue_many(self, n, name=None):
"""Dequeues and concatenates `n` elements from this queue.
This operation concatenates queue-element component tensors along
the 0th dimension to make a single component tensor. All of the
components in the dequeued tuple will have size `n` in the 0th dimension.
If the queue is closed and there are less than `n` elements left, then an
`OutOfRange` exception is raised.
At runtime, this operation may raise an error if the queue is
@{tf.QueueBase.close} before or during its execution. If the
queue is closed, the queue contains fewer than `n` elements, and
there are no pending enqueue operations that can fulfill this
request, `tf.errors.OutOfRangeError` will be raised. If the
session is @{tf.Session.close},
`tf.errors.CancelledError` will be raised.
Args:
n: A scalar `Tensor` containing the number of elements to dequeue.
name: A name for the operation (optional).
Returns:
The tuple of concatenated tensors that was dequeued.
"""
if name is None:
name = "%s_DequeueMany" % self._name
ret = gen_data_flow_ops._queue_dequeue_many_v2(
self._queue_ref, n=n, component_types=self._dtypes, name=name)
# NOTE(mrry): Not using a shape function because we need access to
# the Queue object.
if context.in_graph_mode():
op = ret[0].op
batch_dim = tensor_shape.Dimension(
tensor_util.constant_value(op.inputs[1]))
for output, shape in zip(op.values(), self._shapes):
output.set_shape(
tensor_shape.TensorShape([batch_dim]).concatenate(shape))
return self._dequeue_return_value(ret)
def dequeue_up_to(self, n, name=None):
"""Dequeues and concatenates `n` elements from this queue.
**Note** This operation is not supported by all queues. If a queue does not
support DequeueUpTo, then a `tf.errors.UnimplementedError` is raised.
This operation concatenates queue-element component tensors along
the 0th dimension to make a single component tensor. If the queue
has not been closed, all of the components in the dequeued tuple
will have size `n` in the 0th dimension.
If the queue is closed and there are more than `0` but fewer than
`n` elements remaining, then instead of raising a
`tf.errors.OutOfRangeError` like @{tf.QueueBase.dequeue_many},
less than `n` elements are returned immediately. If the queue is
closed and there are `0` elements left in the queue, then a
`tf.errors.OutOfRangeError` is raised just like in `dequeue_many`.
Otherwise the behavior is identical to `dequeue_many`.
Args:
n: A scalar `Tensor` containing the number of elements to dequeue.
name: A name for the operation (optional).
Returns:
The tuple of concatenated tensors that was dequeued.
"""
if name is None:
name = "%s_DequeueUpTo" % self._name
ret = gen_data_flow_ops._queue_dequeue_up_to_v2(
self._queue_ref, n=n, component_types=self._dtypes, name=name)
# NOTE(mrry): Not using a shape function because we need access to
# the Queue object.
if context.in_graph_mode():
op = ret[0].op
for output, shape in zip(op.values(), self._shapes):
output.set_shape(tensor_shape.TensorShape([None]).concatenate(shape))
return self._dequeue_return_value(ret)
def close(self, cancel_pending_enqueues=False, name=None):
"""Closes this queue.
This operation signals that no more elements will be enqueued in
the given queue. Subsequent `enqueue` and `enqueue_many`
operations will fail. Subsequent `dequeue` and `dequeue_many`
operations will continue to succeed if sufficient elements remain
in the queue. Subsequently dequeue and dequeue_many operations
that would otherwise block waiting for more elements (if close
hadn't been called) will now fail immediately.
If `cancel_pending_enqueues` is `True`, all pending requests will also
be canceled.
Args:
cancel_pending_enqueues: (Optional.) A boolean, defaulting to
`False` (described above).
name: A name for the operation (optional).
Returns:
The operation that closes the queue.
"""
if name is None:
name = "%s_Close" % self._name
if self._queue_ref.dtype == _dtypes.resource:
return gen_data_flow_ops._queue_close_v2(
self._queue_ref, cancel_pending_enqueues=cancel_pending_enqueues,
name=name)
else:
return gen_data_flow_ops._queue_close(
self._queue_ref, cancel_pending_enqueues=cancel_pending_enqueues,
name=name)
def is_closed(self, name=None):
""" Returns true if queue is closed.
This operation returns true if the queue is closed and false if the queue
is open.
Args:
name: A name for the operation (optional).
Returns:
True if the queue is closed and false if the queue is open.
"""
if name is None:
name = "%s_Is_Closed" % self._name
if self._queue_ref.dtype == _dtypes.resource:
return gen_data_flow_ops.queue_is_closed_v2(self._queue_ref,name=name)
else:
return gen_data_flow_ops.queue_is_closed_(self._queue_ref,name=name)
def size(self, name=None):
"""Compute the number of elements in this queue.
Args:
name: A name for the operation (optional).
Returns:
A scalar tensor containing the number of elements in this queue.
"""
if name is None:
name = "%s_Size" % self._name
if self._queue_ref.dtype == _dtypes.resource:
return gen_data_flow_ops._queue_size_v2(self._queue_ref, name=name)
else:
return gen_data_flow_ops._queue_size(self._queue_ref, name=name)
class RandomShuffleQueue(QueueBase):
"""A queue implementation that dequeues elements in a random order.
See @{tf.QueueBase} for a description of the methods on
this class.
@compatibility(eager)
Queues are not compatible with eager execution. Instead, please
use `tf.data` to get data into your model.
@end_compatibility
"""
def __init__(self, capacity, min_after_dequeue, dtypes, shapes=None,
names=None, seed=None, shared_name=None,
name="random_shuffle_queue"):
"""Create a queue that dequeues elements in a random order.
A `RandomShuffleQueue` has bounded capacity; supports multiple
concurrent producers and consumers; and provides exactly-once
delivery.
A `RandomShuffleQueue` holds a list of up to `capacity`
elements. Each element is a fixed-length tuple of tensors whose
dtypes are described by `dtypes`, and whose shapes are optionally
described by the `shapes` argument.
If the `shapes` argument is specified, each component of a queue
element must have the respective fixed shape. If it is
unspecified, different queue elements may have different shapes,
but the use of `dequeue_many` is disallowed.
The `min_after_dequeue` argument allows the caller to specify a
minimum number of elements that will remain in the queue after a
`dequeue` or `dequeue_many` operation completes, to ensure a
minimum level of mixing of elements. This invariant is maintained
by blocking those operations until sufficient elements have been
enqueued. The `min_after_dequeue` argument is ignored after the
queue has been closed.
Args:
capacity: An integer. The upper bound on the number of elements
that may be stored in this queue.
min_after_dequeue: An integer (described above).
dtypes: A list of `DType` objects. The length of `dtypes` must equal
the number of tensors in each queue element.
shapes: (Optional.) A list of fully-defined `TensorShape` objects
with the same length as `dtypes`, or `None`.
names: (Optional.) A list of string naming the components in the queue
with the same length as `dtypes`, or `None`. If specified the dequeue
methods return a dictionary with the names as keys.
seed: A Python integer. Used to create a random seed. See
@{tf.set_random_seed}
for behavior.
shared_name: (Optional.) If non-empty, this queue will be shared under
the given name across multiple sessions.
name: Optional name for the queue operation.
"""
dtypes = _as_type_list(dtypes)
shapes = _as_shape_list(shapes, dtypes)
names = _as_name_list(names, dtypes)
seed1, seed2 = random_seed.get_seed(seed)
if seed1 is None and seed2 is None:
seed1, seed2 = 0, 0
elif seed is None and shared_name is not None:
# This means that graph seed is provided but op seed is not provided.
# If shared_name is also provided, make seed2 depend only on the graph
# seed and shared_name. (seed2 from get_seed() is generally dependent on
# the id of the last op created.)
string = (str(seed1) + shared_name).encode("utf-8")
seed2 = int(hashlib.md5(string).hexdigest()[:8], 16) & 0x7FFFFFFF
queue_ref = gen_data_flow_ops._random_shuffle_queue_v2(
component_types=dtypes, shapes=shapes, capacity=capacity,
min_after_dequeue=min_after_dequeue, seed=seed1, seed2=seed2,
shared_name=shared_name, name=name)
super(RandomShuffleQueue, self).__init__(dtypes, shapes, names, queue_ref)
class FIFOQueue(QueueBase):
"""A queue implementation that dequeues elements in first-in first-out order.
See @{tf.QueueBase} for a description of the methods on
this class.
@compatibility(eager)
Queues are not compatible with eager execution. Instead, please
use `tf.data` to get data into your model.
@end_compatibility
"""
def __init__(self, capacity, dtypes, shapes=None, names=None,
shared_name=None, name="fifo_queue"):
"""Creates a queue that dequeues elements in a first-in first-out order.
A `FIFOQueue` has bounded capacity; supports multiple concurrent
producers and consumers; and provides exactly-once delivery.
A `FIFOQueue` holds a list of up to `capacity` elements. Each
element is a fixed-length tuple of tensors whose dtypes are
described by `dtypes`, and whose shapes are optionally described
by the `shapes` argument.
If the `shapes` argument is specified, each component of a queue
element must have the respective fixed shape. If it is
unspecified, different queue elements may have different shapes,
but the use of `dequeue_many` is disallowed.
Args:
capacity: An integer. The upper bound on the number of elements
that may be stored in this queue.
dtypes: A list of `DType` objects. The length of `dtypes` must equal
the number of tensors in each queue element.
shapes: (Optional.) A list of fully-defined `TensorShape` objects
with the same length as `dtypes`, or `None`.
names: (Optional.) A list of string naming the components in the queue
with the same length as `dtypes`, or `None`. If specified the dequeue
methods return a dictionary with the names as keys.
shared_name: (Optional.) If non-empty, this queue will be shared under
the given name across multiple sessions.
name: Optional name for the queue operation.
"""
dtypes = _as_type_list(dtypes)
shapes = _as_shape_list(shapes, dtypes)
names = _as_name_list(names, dtypes)
queue_ref = gen_data_flow_ops._fifo_queue_v2(
component_types=dtypes, shapes=shapes, capacity=capacity,
shared_name=shared_name, name=name)
super(FIFOQueue, self).__init__(dtypes, shapes, names, queue_ref)
class PaddingFIFOQueue(QueueBase):
"""A FIFOQueue that supports batching variable-sized tensors by padding.
A `PaddingFIFOQueue` may contain components with dynamic shape, while also
supporting `dequeue_many`. See the constructor for more details.
See @{tf.QueueBase} for a description of the methods on
this class.
@compatibility(eager)
Queues are not compatible with eager execution. Instead, please
use `tf.data` to get data into your model.
@end_compatibility
"""
def __init__(self, capacity, dtypes, shapes, names=None, shared_name=None,
name="padding_fifo_queue"):
"""Creates a queue that dequeues elements in a first-in first-out order.
A `PaddingFIFOQueue` has bounded capacity; supports multiple concurrent
producers and consumers; and provides exactly-once delivery.
A `PaddingFIFOQueue` holds a list of up to `capacity` elements. Each
element is a fixed-length tuple of tensors whose dtypes are
described by `dtypes`, and whose shapes are described by the `shapes`
argument.
The `shapes` argument must be specified; each component of a queue
element must have the respective shape. Shapes of fixed
rank but variable size are allowed by setting any shape dimension to None.
In this case, the inputs' shape may vary along the given dimension, and
`dequeue_many` will pad the given dimension with zeros up to the maximum
shape of all elements in the given batch.
Args:
capacity: An integer. The upper bound on the number of elements
that may be stored in this queue.
dtypes: A list of `DType` objects. The length of `dtypes` must equal
the number of tensors in each queue element.
shapes: A list of `TensorShape` objects, with the same length as
`dtypes`. Any dimension in the `TensorShape` containing value
`None` is dynamic and allows values to be enqueued with
variable size in that dimension.
names: (Optional.) A list of string naming the components in the queue
with the same length as `dtypes`, or `None`. If specified the dequeue
methods return a dictionary with the names as keys.
shared_name: (Optional.) If non-empty, this queue will be shared under
the given name across multiple sessions.
name: Optional name for the queue operation.
Raises:
ValueError: If shapes is not a list of shapes, or the lengths of dtypes
and shapes do not match, or if names is specified and the lengths of
dtypes and names do not match.
"""
dtypes = _as_type_list(dtypes)
shapes = _as_shape_list(shapes, dtypes, unknown_dim_allowed=True)
names = _as_name_list(names, dtypes)
if len(dtypes) != len(shapes):
raise ValueError("Shapes must be provided for all components, "
"but received %d dtypes and %d shapes."
% (len(dtypes), len(shapes)))
queue_ref = gen_data_flow_ops._padding_fifo_queue_v2(
component_types=dtypes, shapes=shapes, capacity=capacity,
shared_name=shared_name, name=name)
super(PaddingFIFOQueue, self).__init__(dtypes, shapes, names, queue_ref)
class PriorityQueue(QueueBase):
"""A queue implementation that dequeues elements in prioritized order.
See @{tf.QueueBase} for a description of the methods on
this class.
@compatibility(eager)
Queues are not compatible with eager execution. Instead, please
use `tf.data` to get data into your model.
@end_compatibility
"""
def __init__(self, capacity, types, shapes=None, names=None, shared_name=None,
name="priority_queue"):
"""Creates a queue that dequeues elements in a first-in first-out order.
A `PriorityQueue` has bounded capacity; supports multiple concurrent
producers and consumers; and provides exactly-once delivery.
A `PriorityQueue` holds a list of up to `capacity` elements. Each
element is a fixed-length tuple of tensors whose dtypes are
described by `types`, and whose shapes are optionally described
by the `shapes` argument.
If the `shapes` argument is specified, each component of a queue
element must have the respective fixed shape. If it is
unspecified, different queue elements may have different shapes,
but the use of `dequeue_many` is disallowed.
Enqueues and Dequeues to the `PriorityQueue` must include an additional
tuple entry at the beginning: the `priority`. The priority must be
an int64 scalar (for `enqueue`) or an int64 vector (for `enqueue_many`).
Args:
capacity: An integer. The upper bound on the number of elements
that may be stored in this queue.
types: A list of `DType` objects. The length of `types` must equal
the number of tensors in each queue element, except the first priority
element. The first tensor in each element is the priority,
which must be type int64.
shapes: (Optional.) A list of fully-defined `TensorShape` objects,
with the same length as `types`, or `None`.
names: (Optional.) A list of strings naming the components in the queue
with the same length as `dtypes`, or `None`. If specified, the dequeue
methods return a dictionary with the names as keys.
shared_name: (Optional.) If non-empty, this queue will be shared under
the given name across multiple sessions.
name: Optional name for the queue operation.
"""
types = _as_type_list(types)
shapes = _as_shape_list(shapes, types)
queue_ref = gen_data_flow_ops._priority_queue_v2(
component_types=types, shapes=shapes, capacity=capacity,
shared_name=shared_name, name=name)
priority_dtypes = [_dtypes.int64] + types
priority_shapes = [()] + shapes if shapes else shapes
super(PriorityQueue, self).__init__(
priority_dtypes, priority_shapes, names, queue_ref)
# TODO(josh11b): class BatchQueue(QueueBase):
class Barrier(object):
"""Represents a key-value map that persists across graph executions."""
def __init__(self, types, shapes=None, shared_name=None, name="barrier"):
"""Creates a barrier that persists across different graph executions.
A barrier represents a key-value map, where each key is a string, and
each value is a tuple of tensors.
At runtime, the barrier contains 'complete' and 'incomplete'
elements. A complete element has defined tensors for all
components of its value tuple, and may be accessed using
take_many. An incomplete element has some undefined components in
its value tuple, and may be updated using insert_many.
The barrier call `take_many` outputs values in a particular order.
First, it only outputs completed values. Second, the order in which
completed values are returned matches the order in which their very
first component was inserted into the barrier. So, for example, for this
sequence of insertions and removals:
barrier = Barrier((tf.string, tf.int32), shapes=((), ()))
barrier.insert_many(0, keys=["k1", "k2"], values=["a", "b"]).run()
barrier.insert_many(1, keys=["k1"], values=[1]).run()
barrier.insert_many(0, keys=["k3"], values=["c"]).run()
barrier.insert_many(1, keys=["k3"], values=[3]).run()
barrier.insert_many(1, keys=["k2"], values=[2]).run()
(indices, keys, values) = barrier.take_many(2)
(indices_val, keys_val, values0_val, values1_val) =
session.run([indices, keys, values[0], values[1]])
The output will be (up to permutation of "k1" and "k2"):
indices_val == (-2**63, -2**63)
keys_val == ("k1", "k2")
values0_val == ("a", "b")
values1_val == (1, 2)
Note the key "k2" was inserted into the barrier before "k3". Even though
"k3" was completed first, both are complete by the time
take_many is called. As a result, "k2" is prioritized and "k1" and "k2"
are returned first. "k3" remains in the barrier until the next execution
of `take_many`. Since "k1" and "k2" had their first insertions into
the barrier together, their indices are the same (-2**63). The index
of "k3" will be -2**63 + 1, because it was the next new inserted key.
Args:
types: A single dtype or a tuple of dtypes, corresponding to the
dtypes of the tensor elements that comprise a value in this barrier.
shapes: Optional. Constraints on the shapes of tensors in the values:
a single tensor shape tuple; a tuple of tensor shape tuples
for each barrier-element tuple component; or None if the shape should
not be constrained.
shared_name: Optional. If non-empty, this barrier will be shared under
the given name across multiple sessions.
name: Optional name for the barrier op.
Raises:
ValueError: If one of the `shapes` indicate no elements.
"""
self._types = _as_type_list(types)
if shapes is not None:
shapes = _as_shape_list(shapes, self._types)
self._shapes = [tensor_shape.TensorShape(s) for s in shapes]
for i, shape in enumerate(self._shapes):
if shape.num_elements() == 0:
raise ValueError("Empty tensors are not supported, but received "
"shape '%s' at index %d" % (shape, i))
else:
self._shapes = [tensor_shape.unknown_shape() for _ in self._types]
self._barrier_ref = gen_data_flow_ops._barrier(
component_types=self._types, shapes=self._shapes,
shared_name=shared_name, name=name)
if context.in_graph_mode():
self._name = self._barrier_ref.op.name.split("/")[-1]
else:
self._name = context.context().scope_name
@property
def barrier_ref(self):
"""Get the underlying barrier reference."""
return self._barrier_ref
@property
def name(self):
"""The name of the underlying barrier."""
if context.in_graph_mode():
return self._barrier_ref.op.name
return self._name
def insert_many(self, component_index, keys, values, name=None):
"""For each key, assigns the respective value to the specified component.
This operation updates each element at component_index.
Args:
component_index: The component of the value that is being assigned.
keys: A vector of keys, with length n.
values: An any-dimensional tensor of values, which are associated with the
respective keys. The first dimension must have length n.
name: Optional name for the op.
Returns:
The operation that performs the insertion.
Raises:
InvalidArgumentsError: If inserting keys and values without elements.
"""
if name is None:
name = "%s_BarrierInsertMany" % self._name
return gen_data_flow_ops._barrier_insert_many(
self._barrier_ref, keys, values, component_index, name=name)
def take_many(self,
num_elements,
allow_small_batch=False,
timeout=None,
name=None):
"""Takes the given number of completed elements from this barrier.
This operation concatenates completed-element component tensors along
the 0th dimension to make a single component tensor.
If barrier has no completed elements, this operation will block
until there are 'num_elements' elements to take.
TODO(b/25743580): the semantics of `allow_small_batch` are experimental
and may be extended to other cases in the future.
TODO(ebrevdo): If a take_many(allow_small_batch=True) is blocking
already when the barrier is closed, it will block for ever. Fix this
by using asynchronous operations.
Args:
num_elements: The number of elements to take.
allow_small_batch: If the barrier is closed, don't block if there are less
completed elements than requested, but instead return all available
completed elements.
timeout: This specifies the number of milliseconds to block
before returning with DEADLINE_EXCEEDED. (This option is not
supported yet.)
name: A name for the operation (optional).
Returns:
A tuple of (index, key, value_list).
"index" is a int64 tensor of length num_elements containing the
index of the insert_many call for which the very first component of
the given element was inserted into the Barrier, starting with
the value -2**63. Note, this value is different from the
index of the insert_many call for which the element was completed.
"key" is a string tensor of length num_elements containing the keys.
"value_list" is a tuple of tensors, each one with size num_elements
in the 0th dimension for each component in the barrier's values.
"""
if name is None:
name = "%s_BarrierTakeMany" % self._name
ret = gen_data_flow_ops._barrier_take_many(self._barrier_ref,
num_elements,
self._types,
allow_small_batch,
timeout,
name=name)
# NOTE(mrry): Not using a shape function because we need access to
# the Barrier object.
if context.in_graph_mode():
op = ret[0].op
if allow_small_batch:
batch_dim = None
else:
batch_dim = tensor_shape.Dimension(
tensor_util.constant_value(op.inputs[1]))
op.outputs[0].set_shape(tensor_shape.vector(batch_dim)) # indices
op.outputs[1].set_shape(tensor_shape.vector(batch_dim)) # keys
for output, shape in zip(op.outputs[2:], self._shapes): # value_list
output.set_shape(
tensor_shape.TensorShape([batch_dim]).concatenate(
shape))
return ret
def close(self, cancel_pending_enqueues=False, name=None):
"""Closes this barrier.
This operation signals that no more new key values will be inserted in the
given barrier. Subsequent InsertMany operations with new keys will fail.
InsertMany operations that just complement already existing keys with other
components, will continue to succeed. Subsequent TakeMany operations will
continue to succeed if sufficient elements remain in the barrier. Subsequent
TakeMany operations that would block will fail immediately.
If `cancel_pending_enqueues` is `True`, all pending requests to the
underlying queue will also be canceled, and completing of already
started values is also not acceptable anymore.
Args:
cancel_pending_enqueues: (Optional.) A boolean, defaulting to
`False` (described above).
name: Optional name for the op.
Returns:
The operation that closes the barrier.
"""
if name is None:
name = "%s_BarrierClose" % self._name
return gen_data_flow_ops._barrier_close(
self._barrier_ref,
cancel_pending_enqueues=cancel_pending_enqueues,
name=name)
def ready_size(self, name=None):
"""Compute the number of complete elements in the given barrier.
Args:
name: A name for the operation (optional).
Returns:
A single-element tensor containing the number of complete elements in the
given barrier.
"""
if name is None:
name = "%s_BarrierReadySize" % self._name
return gen_data_flow_ops._barrier_ready_size(self._barrier_ref, name=name)
def incomplete_size(self, name=None):
"""Compute the number of incomplete elements in the given barrier.
Args:
name: A name for the operation (optional).
Returns:
A single-element tensor containing the number of incomplete elements in
the given barrier.
"""
if name is None:
name = "%s_BarrierIncompleteSize" % self._name
return gen_data_flow_ops._barrier_incomplete_size(
self._barrier_ref, name=name)
class ConditionalAccumulatorBase(object):
"""A conditional accumulator for aggregating gradients.
Up-to-date gradients (i.e., time step at which gradient was computed is
equal to the accumulator's time step) are added to the accumulator.
Extraction of the average gradient is blocked until the required number of
gradients has been accumulated.
"""
def __init__(self, dtype, shape, accumulator_ref):
"""Creates a new ConditionalAccumulator.
Args:
dtype: Datatype of the accumulated gradients.
shape: Shape of the accumulated gradients.
accumulator_ref: A handle to the conditional accumulator, created by sub-
classes
"""
self._dtype = dtype
if shape is not None:
self._shape = tensor_shape.TensorShape(shape)
else:
self._shape = tensor_shape.unknown_shape()
self._accumulator_ref = accumulator_ref
if context.in_graph_mode():
self._name = self._accumulator_ref.op.name.split("/")[-1]
else:
self._name = context.context().scope_name
@property
def accumulator_ref(self):
"""The underlying accumulator reference."""
return self._accumulator_ref
@property
def name(self):
"""The name of the underlying accumulator."""
return self._name
@property
def dtype(self):
"""The datatype of the gradients accumulated by this accumulator."""
return self._dtype
def num_accumulated(self, name=None):
"""Number of gradients that have currently been aggregated in accumulator.
Args:
name: Optional name for the operation.
Returns:
Number of accumulated gradients currently in accumulator.
"""
if name is None:
name = "%s_NumAccumulated" % self._name
return gen_data_flow_ops.accumulator_num_accumulated(
self._accumulator_ref, name=name)
def set_global_step(self, new_global_step, name=None):
"""Sets the global time step of the accumulator.
The operation logs a warning if we attempt to set to a time step that is
lower than the accumulator's own time step.
Args:
new_global_step: Value of new time step. Can be a variable or a constant
name: Optional name for the operation.
Returns:
Operation that sets the accumulator's time step.
"""
return gen_data_flow_ops.accumulator_set_global_step(
self._accumulator_ref,
math_ops.to_int64(ops.convert_to_tensor(new_global_step)),
name=name)
class ConditionalAccumulator(ConditionalAccumulatorBase):
"""A conditional accumulator for aggregating gradients.
Up-to-date gradients (i.e., time step at which gradient was computed is
equal to the accumulator's time step) are added to the accumulator.
Extraction of the average gradient is blocked until the required number of
gradients has been accumulated.
"""
def __init__(self,
dtype,
shape=None,
shared_name=None,
name="conditional_accumulator"):
"""Creates a new ConditionalAccumulator.
Args:
dtype: Datatype of the accumulated gradients.
shape: Shape of the accumulated gradients.
shared_name: Optional. If non-empty, this accumulator will be shared under
the given name across multiple sessions.
name: Optional name for the accumulator.
"""
accumulator_ref = gen_data_flow_ops.conditional_accumulator(
dtype=dtype, shape=shape, shared_name=shared_name, name=name)
super(ConditionalAccumulator, self).__init__(dtype, shape, accumulator_ref)
def apply_grad(self, grad, local_step=0, name=None):
"""Attempts to apply a gradient to the accumulator.
The attempt is silently dropped if the gradient is stale, i.e., local_step
is less than the accumulator's global time step.
Args:
grad: The gradient tensor to be applied.
local_step: Time step at which the gradient was computed.
name: Optional name for the operation.
Returns:
The operation that (conditionally) applies a gradient to the accumulator.
Raises:
ValueError: If grad is of the wrong shape
"""
grad = ops.convert_to_tensor(grad, self._dtype)
grad.get_shape().assert_is_compatible_with(self._shape)
local_step = math_ops.to_int64(ops.convert_to_tensor(local_step))
return gen_data_flow_ops.accumulator_apply_gradient(
self._accumulator_ref, local_step=local_step, gradient=grad, name=name)
def take_grad(self, num_required, name=None):
"""Attempts to extract the average gradient from the accumulator.
The operation blocks until sufficient number of gradients have been
successfully applied to the accumulator.
Once successful, the following actions are also triggered:
- Counter of accumulated gradients is reset to 0.
- Aggregated gradient is reset to 0 tensor.
- Accumulator's internal time step is incremented by 1.
Args:
num_required: Number of gradients that needs to have been aggregated
name: Optional name for the operation
Returns:
A tensor holding the value of the average gradient.
Raises:
InvalidArgumentError: If num_required < 1
"""
out = gen_data_flow_ops.accumulator_take_gradient(
self._accumulator_ref, num_required, dtype=self._dtype, name=name)
out.set_shape(self._shape)
return out
class SparseConditionalAccumulator(ConditionalAccumulatorBase):
"""A conditional accumulator for aggregating sparse gradients.
Sparse gradients are represented by IndexedSlices.
Up-to-date gradients (i.e., time step at which gradient was computed is
equal to the accumulator's time step) are added to the accumulator.
Extraction of the average gradient is blocked until the required number of
gradients has been accumulated.
Args:
dtype: Datatype of the accumulated gradients.
shape: Shape of the accumulated gradients.
shared_name: Optional. If non-empty, this accumulator will be shared under
the given name across multiple sessions.
name: Optional name for the accumulator.
"""
def __init__(self,
dtype,
shape=None,
shared_name=None,
name="sparse_conditional_accumulator"):
accumulator_ref = gen_data_flow_ops.sparse_conditional_accumulator(
dtype=dtype, shape=shape, shared_name=shared_name, name=name)
super(SparseConditionalAccumulator,
self).__init__(dtype, shape, accumulator_ref)
def apply_indexed_slices_grad(self, grad, local_step=0, name=None):
"""Attempts to apply a gradient to the accumulator.
The attempt is silently dropped if the gradient is stale, i.e., local_step
is less than the accumulator's global time step.
Args:
grad: The gradient IndexedSlices to be applied.
local_step: Time step at which the gradient was computed.
name: Optional name for the operation.
Returns:
The operation that (conditionally) applies a gradient to the accumulator.
Raises:
InvalidArgumentError: If grad is of the wrong shape
"""
return self.apply_grad(
grad_indices=grad.indices,
grad_values=grad.values,
grad_shape=grad.dense_shape,
local_step=local_step,
name=name)
def apply_grad(self,
grad_indices,
grad_values,
grad_shape=None,
local_step=0,
name=None):
"""Attempts to apply a sparse gradient to the accumulator.
The attempt is silently dropped if the gradient is stale, i.e., local_step
is less than the accumulator's global time step.
A sparse gradient is represented by its indices, values and possibly empty
or None shape. Indices must be a vector representing the locations of
non-zero entries in the tensor. Values are the non-zero slices of the
gradient, and must have the same first dimension as indices, i.e., the nnz
represented by indices and values must be consistent. Shape, if not empty or
None, must be consistent with the accumulator's shape (if also provided).
Example:
A tensor [[0, 0], [0. 1], [2, 3]] can be represented
indices: [1,2]
values: [[0,1],[2,3]]
shape: [3, 2]
Args:
grad_indices: Indices of the sparse gradient to be applied.
grad_values: Values of the sparse gradient to be applied.
grad_shape: Shape of the sparse gradient to be applied.
local_step: Time step at which the gradient was computed.
name: Optional name for the operation.
Returns:
The operation that (conditionally) applies a gradient to the accumulator.
Raises:
InvalidArgumentError: If grad is of the wrong shape
"""
local_step = math_ops.to_int64(ops.convert_to_tensor(local_step))
return gen_data_flow_ops.sparse_accumulator_apply_gradient(
self._accumulator_ref,
local_step=local_step,
gradient_indices=math_ops.to_int64(grad_indices),
gradient_values=grad_values,
gradient_shape=math_ops.to_int64([] if grad_shape is None else
grad_shape),
has_known_shape=(grad_shape is not None),
name=name)
def take_grad(self, num_required, name=None):
"""Attempts to extract the average gradient from the accumulator.
The operation blocks until sufficient number of gradients have been
successfully applied to the accumulator.
Once successful, the following actions are also triggered:
- Counter of accumulated gradients is reset to 0.
- Aggregated gradient is reset to 0 tensor.
- Accumulator's internal time step is incremented by 1.
Args:
num_required: Number of gradients that needs to have been aggregated
name: Optional name for the operation
Returns:
A tuple of indices, values, and shape representing the average gradient.
Raises:
InvalidArgumentError: If num_required < 1
"""
return gen_data_flow_ops.sparse_accumulator_take_gradient(
self._accumulator_ref, num_required, dtype=self._dtype, name=name)
def take_indexed_slices_grad(self, num_required, name=None):
"""Attempts to extract the average gradient from the accumulator.
The operation blocks until sufficient number of gradients have been
successfully applied to the accumulator.
Once successful, the following actions are also triggered:
- Counter of accumulated gradients is reset to 0.
- Aggregated gradient is reset to 0 tensor.
- Accumulator's internal time step is incremented by 1.
Args:
num_required: Number of gradients that needs to have been aggregated
name: Optional name for the operation
Returns:
An IndexedSlices holding the value of the average gradient.
Raises:
InvalidArgumentError: If num_required < 1
"""
return_val = gen_data_flow_ops.sparse_accumulator_take_gradient(
self._accumulator_ref, num_required, dtype=self._dtype, name=name)
return ops.IndexedSlices(
indices=return_val.indices,
values=return_val.values,
dense_shape=return_val.shape)
class BaseStagingArea(object):
"""Base class for Staging Areas."""
_identifier = 0
_lock = threading.Lock()
def __init__(self, dtypes, shapes=None, names=None, shared_name=None,
capacity=0, memory_limit=0):
if shared_name is None:
self._name = (ops.get_default_graph()
.unique_name(self.__class__.__name__))
elif isinstance(shared_name, six.string_types):
self._name = shared_name
else:
raise ValueError("shared_name must be a string")
self._dtypes = dtypes
if shapes is not None:
if len(shapes) != len(dtypes):
raise ValueError("StagingArea shapes must be the same length as dtypes")
self._shapes = [tensor_shape.TensorShape(s) for s in shapes]
else:
self._shapes = [tensor_shape.unknown_shape() for _ in self._dtypes]
if names is not None:
if len(names) != len(dtypes):
raise ValueError("StagingArea names must be the same length as dtypes")
self._names = names
else:
self._names = None
self._capacity = capacity
self._memory_limit = memory_limit
# all get and put ops must colocate with this op
with ops.name_scope("%s_root" % self._name):
self._coloc_op = control_flow_ops.no_op()
@property
def name(self):
"""The name of the staging area."""
return self._name
@property
def dtypes(self):
"""The list of dtypes for each component of a staging area element."""
return self._dtypes
@property
def shapes(self):
"""The list of shapes for each component of a staging area element."""
return self._shapes
@property
def names(self):
"""The list of names for each component of a staging area element."""
return self._names
@property
def capacity(self):
"""The maximum number of elements of this staging area."""
return self._capacity
@property
def memory_limit(self):
"""The maximum number of bytes of this staging area."""
return self._memory_limit
def _check_put_dtypes(self, vals, indices=None):
"""Validate and convert `vals` to a list of `Tensor`s.
The `vals` argument can be a Tensor, a list or tuple of tensors, or a
dictionary with tensor values.
If `vals` is a list, then the appropriate indices associated with the
values must be provided.
If it is a dictionary, the staging area must have been constructed with a
`names` attribute and the dictionary keys must match the staging area names.
`indices` will be inferred from the dictionary keys.
If the staging area was constructed with a `names` attribute, `vals` must
be a dictionary.
Checks that the dtype and shape of each value matches that
of the staging area.
Args:
vals: A tensor, a list or tuple of tensors, or a dictionary..
Returns:
A (tensors, indices) tuple where `tensors` is a list of `Tensor` objects
and `indices` is a list of indices associed with the tensors.
Raises:
ValueError: If `vals` or `indices` is invalid.
"""
if isinstance(vals, dict):
if not self._names:
raise ValueError(
"Staging areas must have names to enqueue a dictionary")
if not set(vals.keys()).issubset(self._names):
raise ValueError("Keys in dictionary to put do not match names "
"of staging area. Dictionary: (%s), Queue: (%s)" %
(sorted(vals.keys()), sorted(self._names)))
# The order of values in `self._names` indicates the order in which the
# tensors in the dictionary `vals` must be listed.
vals, indices, n = zip(*[(vals[k], i, k) for i, k in enumerate(self._names)
if k in vals])
else:
if self._names:
raise ValueError("You must enqueue a dictionary in a staging area "
"with names")
if indices is None:
raise ValueError("Indices must be supplied when inserting a list "
"of tensors")
if len(indices) != len(vals):
raise ValueError("Number of indices '%s' doesn't match "
"number of values '%s'")
if not isinstance(vals, (list, tuple)):
vals = [vals]
indices = [0]
# Sanity check number of values
if not len(vals) <= len(self._dtypes):
raise ValueError("Unexpected number of inputs '%s' vs '%s'" % (
len(vals), len(self._dtypes)))
tensors = []
for val, i in zip(vals, indices):
dtype, shape = self._dtypes[i], self._shapes[i]
# Check dtype
if not val.dtype == dtype:
raise ValueError("Datatypes do not match. '%s' != '%s'" %(
str(val.dtype), str(dtype)))
# Check shape
val.get_shape().assert_is_compatible_with(shape)
tensors.append(ops.convert_to_tensor(val, dtype=dtype,
name="component_%d" % i))
return tensors, indices
def _create_device_transfers(self, tensors):
"""Encode inter-device transfers if the current device
is not the same as the Staging Area's device
"""
if not isinstance(tensors, (tuple, list)):
tensors = [tensors]
curr_device_scope = control_flow_ops.no_op().device
if curr_device_scope != self._coloc_op.device:
tensors = [array_ops.identity(t) for t in tensors]
return tensors
def _get_return_value(self, tensors, indices):
"""Return the value to return from a get op.
If the staging area has names, return a dictionary with the
names as keys. Otherwise return either a single tensor
or a list of tensors depending on the length of `tensors`.
Args:
tensors: List of tensors from the get op.
indices: Indices of associated names and shapes
Returns:
A single tensor, a list of tensors, or a dictionary
of tensors.
"""
tensors = self._create_device_transfers(tensors)
# Sets shape
for output, i in zip(tensors, indices):
output.set_shape(self._shapes[i])
if self._names:
# The returned values in `tensors` are in the same order as
# the names in `self._names`.
return {self._names[i]: t for t, i in zip(tensors, indices)}
return tensors
def _scope_vals(self, vals):
"""Return a list of values to pass to `name_scope()`.
Args:
vals: A tensor, a list or tuple of tensors, or a dictionary.
Returns:
The values in vals as a list.
"""
if isinstance(vals, (list, tuple)):
return vals
elif isinstance(vals, dict):
return vals.values()
else:
return [vals]
class StagingArea(BaseStagingArea):
"""Class for staging inputs. No ordering guarantees.
A `StagingArea` is a TensorFlow data structure that stores tensors across
multiple steps, and exposes operations that can put and get tensors.
Each `StagingArea` element is a tuple of one or more tensors, where each
tuple component has a static dtype, and may have a static shape.
The capacity of a `StagingArea` may be bounded or unbounded.
It supports multiple concurrent producers and consumers; and
provides exactly-once delivery.
Each element of a `StagingArea` is a fixed-length tuple of tensors whose
dtypes are described by `dtypes`, and whose shapes are optionally described
by the `shapes` argument.
If the `shapes` argument is specified, each component of a staging area
element must have the respective fixed shape. If it is
unspecified, different elements may have different shapes,
It can be configured with a capacity in which case
put(values) will block until space becomes available.
Similarly, it can be configured with a memory limit which
will block put(values) until space is available.
This is mostly useful for limiting the number of tensors on
devices such as GPUs.
All get() and peek() commands block if the requested data
is not present in the Staging Area.
"""
def __init__(self, dtypes, shapes=None, names=None, shared_name=None,
capacity=0, memory_limit=0):
"""Constructs a staging area object.
The two optional lists, `shapes` and `names`, must be of the same length
as `dtypes` if provided. The values at a given index `i` indicate the
shape and name to use for the corresponding queue component in `dtypes`.
The device scope at the time of object creation determines where the
storage for the `StagingArea` will reside. Calls to `put` will incur a copy
to this memory space, if necessary. Tensors returned by `get` will be
placed according to the device scope when `get` is called.
Args:
dtypes: A list of types. The length of dtypes must equal the number
of tensors in each element.
capacity: (Optional.) Maximum number of elements.
An integer. If zero, the Staging Area is unbounded
memory_limit: (Optional.) Maximum number of bytes of all tensors
in the Staging Area.
An integer. If zero, the Staging Area is unbounded
shapes: (Optional.) Constraints on the shapes of tensors in an element.
A list of shape tuples or None. This list is the same length
as dtypes. If the shape of any tensors in the element are constrained,
all must be; shapes can be None if the shapes should not be constrained.
names: (Optional.) If provided, the `get()` and
`put()` methods will use dictionaries with these names as keys.
Must be None or a list or tuple of the same length as `dtypes`.
shared_name: (Optional.) A name to be used for the shared object. By
passing the same name to two different python objects they will share
the underlying staging area. Must be a string.
Raises:
ValueError: If one of the arguments is invalid.
"""
super(StagingArea, self).__init__(dtypes, shapes,
names, shared_name,
capacity, memory_limit)
def put(self, values, name=None):
"""Create an op that places a value into the staging area.
This operation will block if the `StagingArea` has reached
its capacity.
Args:
values: Tensor (or a tuple of Tensors) to place into the staging area.
name: A name for the operation (optional).
Returns:
The created op.
Raises:
ValueError: If the number or type of inputs don't match the staging area.
"""
with ops.name_scope(name, "%s_put" % self._name,
self._scope_vals(values)) as scope:
# Hard-code indices for this staging area
indices = (list(six.moves.range(len(values)))
if isinstance(values, (list, tuple)) else None)
vals, _ = self._check_put_dtypes(values, indices)
with ops.colocate_with(self._coloc_op):
op = gen_data_flow_ops.stage(values=vals, shared_name=self._name,
name=scope, capacity=self._capacity,
memory_limit=self._memory_limit)
return op
def __internal_get(self, get_fn, name):
with ops.colocate_with(self._coloc_op):
ret = get_fn()
indices = list(six.moves.range(len(self._dtypes))) # Hard coded
return self._get_return_value(ret, indices)
def get(self, name=None):
"""Gets one element from this staging area.
If the staging area is empty when this operation executes, it will block
until there is an element to dequeue.
Note that unlike others ops that can block, like the queue Dequeue
operations, this can stop other work from happening. To avoid this, the
intended use is for this to be called only when there will be an element
already available. One method for doing this in a training loop would be to
run a `put()` call during a warmup session.run call, and then call both
`get()` and `put()` in each subsequent step.
The placement of the returned tensor will be determined by the current
device scope when this function is called.
Args:
name: A name for the operation (optional).
Returns:
The tuple of tensors that was gotten.
"""
if name is None:
name = "%s_get" % self._name
fn = lambda: gen_data_flow_ops.unstage(dtypes=self._dtypes,
shared_name=self._name, name=name,
capacity=self._capacity,
memory_limit=self._memory_limit)
return self.__internal_get(fn, name)
def peek(self, index, name=None):
"""Peeks at an element in the staging area.
If the staging area is too small to contain the element at
the specified index, it will block until enough elements
are inserted to complete the operation.
The placement of the returned tensor will be determined by
the current device scope when this function is called.
Args:
index: The index of the tensor within the staging area
to look up.
name: A name for the operation (optional).
Returns:
The tuple of tensors that was gotten.
"""
if name is None:
name = "%s_peek" % self._name
fn = lambda: gen_data_flow_ops.stage_peek(index,
dtypes=self._dtypes, shared_name=self._name,
name=name, capacity=self._capacity,
memory_limit=self._memory_limit)
return self.__internal_get(fn, name)
def size(self, name=None):
"""Returns the number of elements in the staging area.
Args:
name: A name for the operation (optional)
Returns:
The created op
"""
if name is None:
name = "%s_size" % self._name
return gen_data_flow_ops.stage_size(name=name, shared_name=self._name,
dtypes=self._dtypes, capacity=self._capacity,
memory_limit=self._memory_limit)
def clear(self, name=None):
"""Clears the staging area.
Args:
name: A name for the operation (optional)
Returns:
The created op
"""
if name is None:
name = "%s_clear" % self._name
return gen_data_flow_ops.stage_clear(name=name, shared_name=self._name,
dtypes=self._dtypes, capacity=self._capacity,
memory_limit=self._memory_limit)
class MapStagingArea(BaseStagingArea):
"""
A `MapStagingArea` is a TensorFlow data structure that stores tensors across
multiple steps, and exposes operations that can put and get tensors.
Each `MapStagingArea` element is a (key, value) pair.
Only int64 keys are supported, other types should be
hashed to produce a key.
Values are a tuple of one or more tensors.
Each tuple component has a static dtype,
and may have a static shape.
The capacity of a `MapStagingArea` may be bounded or unbounded.
It supports multiple concurrent producers and consumers; and
provides exactly-once delivery.
Each value tuple of a `MapStagingArea` is a fixed-length tuple of tensors whose
dtypes are described by `dtypes`, and whose shapes are optionally described
by the `shapes` argument.
If the `shapes` argument is specified, each component of a staging area
element must have the respective fixed shape. If it is
unspecified, different elements may have different shapes,
It behaves like an associative container with support for:
- put(key, values)
- peek(key) like dict.get(key)
- get(key) like dict.pop(key)
- get(key=None) like dict.popitem()
- size()
- clear()
If ordered a tree structure ordered by key will be used and
get(key=None) will remove (key, value) pairs in increasing key order.
Otherwise a hashtable
It can be configured with a capacity in which case
put(key, values) will block until space becomes available.
Similarly, it can be configured with a memory limit which
will block put(key, values) until space is available.
This is mostly useful for limiting the number of tensors on
devices such as GPUs.
All get() and peek() commands block if the requested
(key, value) pair is not present in the staging area.
Partial puts are supported and will be placed in an incomplete
map until such time as all values associated with the key have
been inserted. Once completed, this (key, value) pair will be
inserted into the map. Data in the incomplete map
counts towards the memory limit, but not towards capacity limit.
Partial gets from the map are also supported.
This removes the partially requested tensors from the entry,
but the entry is only removed from the map once all tensors
associated with it are removed.
"""
def __init__(self, dtypes, shapes=None, names=None, shared_name=None,
ordered=False, capacity=0, memory_limit=0):
"""
Args:
dtypes: A list of types. The length of dtypes must equal the number
of tensors in each element.
capacity: (Optional.) Maximum number of elements.
An integer. If zero, the Staging Area is unbounded
memory_limit: (Optional.) Maximum number of bytes of all tensors
in the Staging Area (excluding keys).
An integer. If zero, the Staging Area is unbounded
ordered: (Optional.) If True the underlying data structure
is a tree ordered on key. Otherwise assume a hashtable.
shapes: (Optional.) Constraints on the shapes of tensors in an element.
A list of shape tuples or None. This list is the same length
as dtypes. If the shape of any tensors in the element are constrained,
all must be; shapes can be None if the shapes should not be constrained.
names: (Optional.) If provided, the `get()` and
`put()` methods will use dictionaries with these names as keys.
Must be None or a list or tuple of the same length as `dtypes`.
shared_name: (Optional.) A name to be used for the shared object. By
passing the same name to two different python objects they will share
the underlying staging area. Must be a string.
Raises:
ValueError: If one of the arguments is invalid.
"""
super(MapStagingArea, self).__init__(dtypes, shapes,
names, shared_name,
capacity, memory_limit)
# Defer to different methods depending if the map is ordered
self._ordered = ordered
if ordered:
self._put_fn = gen_data_flow_ops.ordered_map_stage
self._pop_fn = gen_data_flow_ops.ordered_map_unstage
self._popitem_fn = gen_data_flow_ops.ordered_map_unstage_no_key
self._peek_fn = gen_data_flow_ops.ordered_map_peek
self._size_fn = gen_data_flow_ops.ordered_map_size
self._incomplete_size_fn = gen_data_flow_ops.ordered_map_incomplete_size
self._clear_fn = gen_data_flow_ops.ordered_map_clear
else:
self._put_fn = gen_data_flow_ops.map_stage
self._pop_fn = gen_data_flow_ops.map_unstage
self._popitem_fn = gen_data_flow_ops.map_unstage_no_key
self._peek_fn = gen_data_flow_ops.map_peek
self._size_fn = gen_data_flow_ops.map_size
self._incomplete_size_fn = gen_data_flow_ops.map_incomplete_size
self._clear_fn = gen_data_flow_ops.map_clear
def put(self, key, vals, indices=None, name=None):
"""
Create an op that stores the (key, vals) pair in the staging area.
Incomplete puts are possible, preferably using a dictionary for vals
as the appropriate dtypes and shapes can be inferred from the value names
dictionary key values. If vals is a list or tuple, indices must
also be specified so that the op knows at which element position
to perform the insert.
This operation will block if the capacity or memory limit of this
container is reached.
Args:
key: Key associated with the data
vals: Tensor (or a dict/tuple of Tensors) to place
into the staging area.
indices: (Optional) if vals is a tuple/list, this is required.
name: A name for the operation (optional)
Returns:
The created op
Raises:
ValueError: If the number or type of inputs don't match the staging area.
"""
with ops.name_scope(name, "%s_put" % self._name,
self._scope_vals(vals)) as scope:
vals, indices = self._check_put_dtypes(vals, indices)
with ops.colocate_with(self._coloc_op):
op = self._put_fn(key, indices, vals, dtypes=self._dtypes,
shared_name=self._name, name=scope,
capacity=self._capacity,
memory_limit=self._memory_limit)
return op
def _get_indices_and_dtypes(self, indices=None):
if indices is None:
indices = list(six.moves.range(len(self._dtypes)))
if not isinstance(indices, (tuple, list)):
raise TypeError("Invalid indices type '%s'" % type(indices))
if len(indices) == 0:
raise ValueError("Empty indices")
if all(isinstance(i, str) for i in indices):
if self._names is None:
raise ValueError("String indices provided '%s', but this Staging Area "
"was not created with names." % indices)
try:
indices = [self._names.index(n) for n in indices]
except ValueError:
raise ValueError("Named index '%s' not in "
"Staging Area names '%s'" % (n, self._names))
elif all(isinstance(i, int) for i in indices):
pass
else:
raise TypeError("Mixed types in indices '%s'. "
"May only be str or int" % indices)
dtypes = [self._dtypes[i] for i in indices]
return indices, dtypes
def peek(self, key, indices=None, name=None):
"""
Peeks at staging area data associated with the key.
If the key is not in the staging area, it will block
until the associated (key, value) is inserted.
Args:
key: Key associated with the required data
indices: Partial list of tensors to retrieve (optional).
A list of integer or string indices.
String indices are only valid if the Staging Area
has names associated with it.
name: A name for the operation (optional)
Returns:
The created op
"""
if name is None:
name = "%s_pop" % self._name
indices, dtypes = self._get_indices_and_dtypes(indices)
with ops.colocate_with(self._coloc_op):
result = self._peek_fn(key, shared_name=self._name,
indices=indices,
dtypes=dtypes,
name=name,
capacity=self._capacity,
memory_limit=self._memory_limit)
return self._get_return_value(result, indices)
def get(self, key=None, indices=None, name=None):
"""
If the key is provided, the associated (key, value)
is returned from the staging area. If the key is not
in the staging area, this method will block until
the associated (key, value) is inserted.
If no key is provided and the staging area is ordered,
the (key, value) with the smallest key will be returned.
Otherwise, a random (key, value) will be returned.
If the staging area is empty when this operation executes,
it will block until there is an element to dequeue.
Args:
key: Key associated with the required data (Optional)
indices: Partial list of tensors to retrieve (optional).
A list of integer or string indices.
String indices are only valid if the Staging Area
has names associated with it.
name: A name for the operation (optional)
Returns:
The created op
"""
if key is None:
return self._popitem(indices=indices, name=name)
else:
return self._pop(key, indices=indices, name=name)
def _pop(self, key, indices=None, name=None):
"""
Remove and return the associated (key, value)
is returned from the staging area. If the key is not
in the staging area, this method will block until
the associated (key, value) is inserted.
Args:
key: Key associated with the required data
indices: Partial list of tensors to retrieve (optional).
A list of integer or string indices.
String indices are only valid if the Staging Area
has names associated with it.
name: A name for the operation (optional)
Returns:
The created op
"""
if name is None:
name = "%s_get" % self._name
indices, dtypes = self._get_indices_and_dtypes(indices)
with ops.colocate_with(self._coloc_op):
result = self._pop_fn(key, shared_name=self._name,
indices=indices,
dtypes=dtypes,
name=name,
capacity=self._capacity,
memory_limit=self._memory_limit)
return key, self._get_return_value(result, indices)
def _popitem(self, indices=None, name=None):
"""
If the staging area is ordered,
the (key, value) with the smallest key will be returned.
Otherwise, a random (key, value) will be returned.
If the staging area is empty when this operation executes,
it will block until there is an element to dequeue.
Args:
key: Key associated with the required data
indices: Partial list of tensors to retrieve (optional).
A list of integer or string indices.
String indices are only valid if the Staging Area
has names associated with it.
name: A name for the operation (optional)
Returns:
The created op
"""
if name is None:
name = "%s_get_nokey" % self._name
indices, dtypes = self._get_indices_and_dtypes(indices)
with ops.colocate_with(self._coloc_op):
key, result = self._popitem_fn(shared_name=self._name,
indices=indices,
dtypes=dtypes,
name=name,
capacity=self._capacity,
memory_limit=self._memory_limit)
# Separate keys and results out from
# underlying namedtuple
key = self._create_device_transfers(key)[0]
result = self._get_return_value(result, indices)
return key, result
def size(self, name=None):
"""
Returns the number of elements in the staging area.
Args:
name: A name for the operation (optional)
Returns:
The created op
"""
if name is None:
name = "%s_size" % self._name
return self._size_fn(shared_name=self._name,
name=name, dtypes=self._dtypes,
capacity=self._capacity,
memory_limit=self._memory_limit)
def incomplete_size(self, name=None):
"""
Returns the number of incomplete elements in the staging area.
Args:
name: A name for the operation (optional)
Returns:
The created op
"""
if name is None:
name = "%s_incomplete_size" % self._name
return self._incomplete_size_fn(shared_name=self._name,
name=name, dtypes=self._dtypes,
capacity=self._capacity,
memory_limit=self._memory_limit)
def clear(self, name=None):
"""
Clears the staging area.
Args:
name: A name for the operation (optional)
Returns:
The created op
"""
if name is None:
name = "%s_clear" % self._name
return self._clear_fn(shared_name=self._name,
name=name, dtypes=self._dtypes,
capacity=self._capacity,
memory_limit=self._memory_limit)
class RecordInput(object):
"""RecordInput asynchronously reads and randomly yields TFRecords.
A RecordInput Op will continuously read a batch of records asynchronously
into a buffer of some fixed capacity. It can also asynchronously yield
random records from this buffer.
It will not start yielding until at least `buffer_size / 2` elements have been
placed into the buffer so that sufficient randomization can take place.
The order the files are read will be shifted each epoch by `shift_amount` so
that the data is presented in a different order every epoch.
"""
def __init__(self,
file_pattern,
batch_size=1,
buffer_size=1,
parallelism=1,
shift_ratio=0,
seed=0,
name=None,
batches=None):
"""Constructs a RecordInput Op.
Args:
file_pattern: File path to the dataset, possibly containing wildcards.
All matching files will be iterated over each epoch.
batch_size: How many records to return at a time.
buffer_size: The maximum number of records the buffer will contain.
parallelism: How many reader threads to use for reading from files.
shift_ratio: What percentage of the total number files to move the start
file forward by each epoch.
seed: Specify the random number seed used by generator that randomizes
records.
name: Optional name for the operation.
batches: None by default, creating a single batch op. Otherwise specifies
how many batches to create, which are returned as a list when
`get_yield_op()` is called. An example use case is to split processing
between devices on one computer.
Raises:
ValueError: If one of the arguments is invalid.
"""
self._batch_size = batch_size
if batches is not None:
self._batch_size *= batches
self._batches = batches
self._file_pattern = file_pattern
self._buffer_size = buffer_size
self._parallelism = parallelism
self._shift_ratio = shift_ratio
self._seed = seed
self._name = name
def get_yield_op(self):
"""Adds a node that yields a group of records every time it is executed.
If RecordInput `batches` parameter is not None, it yields a list of
record batches with the specified `batch_size`.
"""
records = gen_data_flow_ops.record_input(
file_pattern=self._file_pattern,
file_buffer_size=self._buffer_size,
file_parallelism=self._parallelism,
file_shuffle_shift_ratio=self._shift_ratio,
batch_size=self._batch_size,
file_random_seed=self._seed,
name=self._name)
if self._batches is None:
return records
else:
with ops.name_scope(self._name):
batch_list = [[] for i in six.moves.range(self._batches)]
records = array_ops.split(records, self._batch_size, 0)
records = [array_ops.reshape(record, []) for record in records]
for index, protobuf in zip(six.moves.range(len(records)), records):
batch_index = index % self._batches
batch_list[batch_index].append(protobuf)
return batch_list