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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.
# ==============================================================================
"""Experimental API for gathering statistics from `tf.data` pipelines."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from tensorflow.python.data.ops import dataset_ops
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import ops
from tensorflow.python.ops import gen_dataset_ops
# TODO(b/38416882): Properly export in the `tf.contrib.data` API when stable
# or make private / remove.
class StatsAggregator(object):
"""A stateful resource that aggregates statistics from one or more iterators.
To record statistics, use one of the custom transformation functions defined
in this module when defining your `tf.data.Dataset`. All statistics will be
aggregated by the `StatsAggregator` that is associated with a particular
iterator (see below). For example, to record the total number of bytes
produced by iterating over a dataset:
```python
dataset = ...
dataset = dataset.apply(stats_ops.bytes_produced_stats("total_bytes"))
```
To associate a `StatsAggregator` with a `tf.data.Iterator` object, use
the following pattern:
```python
dataset = ...
iterator = dataset.make_one_shot_iterator()
stats_aggregator = stats_ops.StatsAggregator()
set_op = stats_aggregator.subscribe(iterator)
with tf.Session() as sess:
# Running `set_op` will associate `iterator` with `stats_aggregator`.
sess.run(set_op)
```
To get a protocol buffer summary of the currently aggregated statistics,
use the `StatsAggregator.get_summary()` tensor. The easiest way to do this
is to add the returned tensor to the `tf.GraphKeys.SUMMARIES` collection,
so that the summaries will be included with any existing summaries.
```python
stats_aggregator = stats_ops.StatsAggregator()
stats_summary = stats_aggregator.get_summary()
tf.add_to_collection(tf.GraphKeys.SUMMARIES, stats_summary)
```
Note: This interface is experimental and expected to change. In particular,
we expect to add other implementations of `StatsAggregator` that provide
different ways of exporting statistics, and add more types of statistics.
"""
def __init__(self):
"""Creates a `StatsAggregator`."""
self._resource = gen_dataset_ops.stats_aggregator_handle()
def get_summary(self):
"""Returns a string `tf.Tensor` that summarizes the aggregated statistics.
The returned tensor will contain a serialized `tf.summary.Summary` protocol
buffer, which can be used with the standard TensorBoard logging facilities.
Returns:
A scalar string `tf.Tensor` that summarizes the aggregated statistics.
"""
return gen_dataset_ops.stats_aggregator_summary(self._resource)
class _SetStatsAggregatorDataset(dataset_ops.Dataset):
"""A `Dataset` that acts as an identity, and sets given stats_aggregator."""
def __init__(self, input_dataset, stats_aggregator):
super(_SetStatsAggregatorDataset, self).__init__()
self._input_dataset = input_dataset
self._stats_aggregator = stats_aggregator
def _as_variant_tensor(self):
return gen_dataset_ops.set_stats_aggregator_dataset(
self._input_dataset._as_variant_tensor(), # pylint: disable=protected-access
self._stats_aggregator._resource, # pylint: disable=protected-access
**dataset_ops.flat_structure(self))
@property
def output_shapes(self):
return self._input_dataset.output_shapes
@property
def output_types(self):
return self._input_dataset.output_types
@property
def output_classes(self):
return self._input_dataset.output_classes
# TODO(b/38416882): Properly export in the `tf.contrib.data` API when stable
# or make private / remove.
def set_stats_aggregator(stats_aggregator):
"""Set the given stats_aggregator for aggregating the input dataset stats.
Args:
stats_aggregator: A `StatsAggregator` object.
Returns:
A `Dataset` transformation function, which can be passed to
`tf.data.Dataset.apply`.
"""
def _apply_fn(dataset):
return _SetStatsAggregatorDataset(dataset, stats_aggregator)
return _apply_fn
# TODO(b/38416882): Properly export in the `tf.contrib.data` API when stable
# or make private / remove.
def bytes_produced_stats(tag):
"""Records the number of bytes produced by each element of the input dataset.
To consume the statistics, associate a `StatsAggregator` with the output
dataset.
Args:
tag: String. All statistics recorded by the returned transformation will
be associated with the given `tag`.
Returns:
A `Dataset` transformation function, which can be passed to
`tf.data.Dataset.apply`.
"""
def _apply_fn(dataset):
return _StatsDataset(dataset, gen_dataset_ops.bytes_produced_stats_dataset,
tag)
return _apply_fn
# TODO(b/38416882): Properly export in the `tf.contrib.data` API when stable
# or make private / remove.
def latency_stats(tag):
"""Records the latency of producing each element of the input dataset.
To consume the statistics, associate a `StatsAggregator` with the output
dataset.
Args:
tag: String. All statistics recorded by the returned transformation will
be associated with the given `tag`.
Returns:
A `Dataset` transformation function, which can be passed to
`tf.data.Dataset.apply`.
"""
def _apply_fn(dataset):
return _StatsDataset(dataset, gen_dataset_ops.latency_stats_dataset, tag)
return _apply_fn
class _StatsDataset(dataset_ops.Dataset):
"""A `Dataset` that acts as an identity, and also records statistics."""
def __init__(self, input_dataset, op_function, tag):
super(_StatsDataset, self).__init__()
self._input_dataset = input_dataset
self._op_function = op_function
self._tag = ops.convert_to_tensor(tag, dtype=dtypes.string)
def _as_variant_tensor(self):
return self._op_function(
self._input_dataset._as_variant_tensor(), # pylint: disable=protected-access
self._tag,
**dataset_ops.flat_structure(self))
@property
def output_shapes(self):
return self._input_dataset.output_shapes
@property
def output_types(self):
return self._input_dataset.output_types
@property
def output_classes(self):
return self._input_dataset.output_classes
|
