f40a875355
Back-ticks are now converted to links in the api_docs generator. With the new docs repo we're moving to simplify the docs pipeline, and make everything more readable.
By doing this we no longer get test failures for symbols that don't exist (`tf.does_not_exist` will not get a link).
There is also no way, not to set custom link text. That's okay.
This is the result of the following regex replacement (+ a couple of manual edits.):
re: @\{([^$].*?)(\$.+?)?}
sub: `\1`
Which does the following replacements:
"@{tf.symbol}" --> "`tf.symbol`"
"@{tf.symbol$link_text}" --> "`tf.symbol`"
PiperOrigin-RevId: 208042358
230 lines
7.5 KiB
Python
230 lines
7.5 KiB
Python
# 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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"""Experimental API for gathering statistics from `tf.data` pipelines."""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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from tensorflow.python.data.ops import dataset_ops
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from tensorflow.python.framework import dtypes
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from tensorflow.python.framework import ops
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from tensorflow.python.ops import gen_dataset_ops
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# TODO(b/38416882): Properly export in the `tf.contrib.data` API when stable
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# or make private / remove.
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class StatsAggregator(object):
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"""A stateful resource that aggregates statistics from one or more iterators.
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To record statistics, use one of the custom transformation functions defined
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in this module when defining your `tf.data.Dataset`. All statistics will be
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aggregated by the `StatsAggregator` that is associated with a particular
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iterator (see below). For example, to record the total number of bytes
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produced by iterating over a dataset:
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```python
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dataset = ...
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dataset = dataset.apply(stats_ops.bytes_produced_stats("total_bytes"))
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```
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To associate a `StatsAggregator` with a `tf.data.Iterator` object, use
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the following pattern:
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```python
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dataset = ...
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iterator = dataset.make_one_shot_iterator()
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stats_aggregator = stats_ops.StatsAggregator()
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set_op = stats_aggregator.subscribe(iterator)
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with tf.Session() as sess:
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# Running `set_op` will associate `iterator` with `stats_aggregator`.
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sess.run(set_op)
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```
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To get a protocol buffer summary of the currently aggregated statistics,
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use the `StatsAggregator.get_summary()` tensor. The easiest way to do this
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is to add the returned tensor to the `tf.GraphKeys.SUMMARIES` collection,
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so that the summaries will be included with any existing summaries.
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```python
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stats_aggregator = stats_ops.StatsAggregator()
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stats_summary = stats_aggregator.get_summary()
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tf.add_to_collection(tf.GraphKeys.SUMMARIES, stats_summary)
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```
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Note: This interface is experimental and expected to change. In particular,
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we expect to add other implementations of `StatsAggregator` that provide
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different ways of exporting statistics, and add more types of statistics.
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"""
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def __init__(self):
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"""Creates a `StatsAggregator`."""
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self._resource = gen_dataset_ops.stats_aggregator_handle()
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def get_summary(self):
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"""Returns a string `tf.Tensor` that summarizes the aggregated statistics.
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The returned tensor will contain a serialized `tf.summary.Summary` protocol
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buffer, which can be used with the standard TensorBoard logging facilities.
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Returns:
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A scalar string `tf.Tensor` that summarizes the aggregated statistics.
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"""
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return gen_dataset_ops.stats_aggregator_summary(self._resource)
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class _SetStatsAggregatorDataset(dataset_ops.Dataset):
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"""A `Dataset` that acts as an identity, and sets given stats_aggregator."""
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def __init__(self, input_dataset, stats_aggregator):
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super(_SetStatsAggregatorDataset, self).__init__()
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self._input_dataset = input_dataset
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self._stats_aggregator = stats_aggregator
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def _as_variant_tensor(self):
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return gen_dataset_ops.set_stats_aggregator_dataset(
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self._input_dataset._as_variant_tensor(), # pylint: disable=protected-access
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self._stats_aggregator._resource, # pylint: disable=protected-access
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**dataset_ops.flat_structure(self))
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@property
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def output_shapes(self):
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return self._input_dataset.output_shapes
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@property
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def output_types(self):
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return self._input_dataset.output_types
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@property
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def output_classes(self):
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return self._input_dataset.output_classes
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# TODO(b/38416882): Properly export in the `tf.contrib.data` API when stable
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# or make private / remove.
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def set_stats_aggregator(stats_aggregator):
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"""Set the given stats_aggregator for aggregating the input dataset stats.
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Args:
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stats_aggregator: A `StatsAggregator` object.
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Returns:
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A `Dataset` transformation function, which can be passed to
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`tf.data.Dataset.apply`.
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"""
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def _apply_fn(dataset):
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return _SetStatsAggregatorDataset(dataset, stats_aggregator)
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return _apply_fn
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# TODO(b/38416882): Properly export in the `tf.contrib.data` API when stable
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# or make private / remove.
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def bytes_produced_stats(tag):
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"""Records the number of bytes produced by each element of the input dataset.
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To consume the statistics, associate a `StatsAggregator` with the output
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dataset.
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Args:
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tag: String. All statistics recorded by the returned transformation will
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be associated with the given `tag`.
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Returns:
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A `Dataset` transformation function, which can be passed to
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`tf.data.Dataset.apply`.
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"""
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def _apply_fn(dataset):
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return _StatsDataset(dataset, gen_dataset_ops.bytes_produced_stats_dataset,
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tag)
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return _apply_fn
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# TODO(b/38416882): Properly export in the `tf.contrib.data` API when stable
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# or make private / remove.
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def latency_stats(tag):
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"""Records the latency of producing each element of the input dataset.
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To consume the statistics, associate a `StatsAggregator` with the output
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dataset.
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Args:
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tag: String. All statistics recorded by the returned transformation will
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be associated with the given `tag`.
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Returns:
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A `Dataset` transformation function, which can be passed to
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`tf.data.Dataset.apply`.
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"""
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def _apply_fn(dataset):
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return _StatsDataset(dataset, gen_dataset_ops.latency_stats_dataset, tag)
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return _apply_fn
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# TODO(b/38416882): Properly export in the `tf.contrib.data` API when stable
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# or make private / remove.
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def feature_stats(tag):
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"""Records the features stats from `Example` records of the input dataset.
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To consume the statistics, associate a `StatsAggregator` with the output
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dataset.
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Args:
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tag: String. All statistics recorded by the returned transformation will be
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associated with the given `tag`.
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Returns:
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A `Dataset` transformation function, which can be passed to
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`tf.data.Dataset.apply`.
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"""
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def _apply_fn(dataset):
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return _StatsDataset(dataset, gen_dataset_ops.feature_stats_dataset, tag)
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return _apply_fn
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class _StatsDataset(dataset_ops.Dataset):
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"""A `Dataset` that acts as an identity, and also records statistics."""
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def __init__(self, input_dataset, op_function, tag):
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super(_StatsDataset, self).__init__()
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self._input_dataset = input_dataset
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self._op_function = op_function
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self._tag = ops.convert_to_tensor(tag, dtype=dtypes.string)
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def _as_variant_tensor(self):
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return self._op_function(
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self._input_dataset._as_variant_tensor(), # pylint: disable=protected-access
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self._tag,
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**dataset_ops.flat_structure(self))
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@property
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def output_shapes(self):
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return self._input_dataset.output_shapes
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@property
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def output_types(self):
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return self._input_dataset.output_types
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@property
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def output_classes(self):
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return self._input_dataset.output_classes
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