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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.
# ==============================================================================
"""Implements a simple prefetch_queue."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from tensorflow.python.framework import ops
from tensorflow.python.ops import data_flow_ops
from tensorflow.python.ops import math_ops
from tensorflow.python.summary import summary
from tensorflow.python.training import queue_runner
def _which_queue(dynamic_pad):
return (data_flow_ops.PaddingFIFOQueue if dynamic_pad
else data_flow_ops.FIFOQueue)
def prefetch_queue(tensors,
capacity=8,
num_threads=1,
dynamic_pad=False,
shared_name=None,
name=None):
"""Creates a queue to prefetech tensors from `tensors`.
A queue runner for enqueing tensors into the prefetch_queue is automatically
added to the TF QueueRunners collection.
Example:
This is for example useful to pre-assemble input batches read with
`tf.train.batch()` and enqueue the pre-assembled batches. Ops that dequeue
from the pre-assembled queue will not pay the cost of assembling the batch.
images, labels = tf.train.batch([image, label], batch_size=32, num_threads=4)
batch_queue = prefetch_queue([images, labels])
images, labels = batch_queue.dequeue()
logits = Net(images)
loss = Loss(logits, labels)
Args:
tensors: A list or dictionary of `Tensors` to enqueue in the buffer.
capacity: An integer. The maximum number of elements in the queue.
num_threads: An integer. Number of threads running the enqueue op.
dynamic_pad: Boolean. Whether to allow variable dimensions in input shapes.
shared_name: (optional). If set, this queue will be shared under the given
name across multiple sessions.
name: (Optional) A name for the operations.
Returns:
A queue from which you can dequeue tensors with the same type and shape
as `tensors`.
"""
if isinstance(tensors, dict):
# Need to wrap the keys and values in list() since Python3 returns views.
# We sort the keys so the order is consistent across runs.
names = list(sorted(tensors.keys()))
tensor_list = list([tensors[n] for n in names])
else:
names = None
tensor_list = tensors
with ops.name_scope(name, "prefetch_queue", tensor_list) as name:
dtypes = [t.dtype for t in tensor_list]
shapes = [t.get_shape() for t in tensor_list]
queue = _which_queue(dynamic_pad)(
capacity=capacity,
dtypes=dtypes,
shapes=shapes,
names=names,
shared_name=shared_name)
enqueue_op = queue.enqueue(tensors)
queue_runner.add_queue_runner(
queue_runner.QueueRunner(queue, [enqueue_op] * num_threads))
summary.scalar("fraction_of_%d_full" % capacity,
math_ops.to_float(queue.size()) * (1. / capacity))
return queue
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