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61 lines
2.5 KiB
61 lines
2.5 KiB
# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""Provides functions to prefetch tensors to feed into models."""
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import tensorflow as tf
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def prefetch(tensor_dict, capacity):
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"""Creates a prefetch queue for tensors.
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Creates a FIFO queue to asynchronously enqueue tensor_dicts and returns a
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dequeue op that evaluates to a tensor_dict. This function is useful in
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prefetching preprocessed tensors so that the data is readily available for
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consumers.
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Example input pipeline when you don't need batching:
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----------------------------------------------------
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key, string_tensor = slim.parallel_reader.parallel_read(...)
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tensor_dict = decoder.decode(string_tensor)
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tensor_dict = preprocessor.preprocess(tensor_dict, ...)
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prefetch_queue = prefetcher.prefetch(tensor_dict, capacity=20)
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tensor_dict = prefetch_queue.dequeue()
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outputs = Model(tensor_dict)
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...
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----------------------------------------------------
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For input pipelines with batching, refer to core/batcher.py
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Args:
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tensor_dict: a dictionary of tensors to prefetch.
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capacity: the size of the prefetch queue.
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Returns:
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a FIFO prefetcher queue
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"""
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names = list(tensor_dict.keys())
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dtypes = [t.dtype for t in tensor_dict.values()]
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shapes = [t.get_shape() for t in tensor_dict.values()]
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prefetch_queue = tf.PaddingFIFOQueue(capacity, dtypes=dtypes,
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shapes=shapes,
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names=names,
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name='prefetch_queue')
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enqueue_op = prefetch_queue.enqueue(tensor_dict)
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tf.train.queue_runner.add_queue_runner(tf.train.queue_runner.QueueRunner(
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prefetch_queue, [enqueue_op]))
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tf.summary.scalar('queue/%s/fraction_of_%d_full' % (prefetch_queue.name,
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capacity),
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tf.to_float(prefetch_queue.size()) * (1. / capacity))
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return prefetch_queue
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