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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.
# ==============================================================================
"""The TensorBoard Histograms plugin.
See `http_api.md` in this directory for specifications of the routes for
this plugin.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import collections
import random
import numpy as np
import six
import tensorflow as tf
from werkzeug import wrappers
from tensorboard import plugin_util
from tensorboard.backend import http_util
from tensorboard.plugins import base_plugin
from tensorboard.plugins.histogram import metadata
class HistogramsPlugin(base_plugin.TBPlugin):
"""Histograms Plugin for TensorBoard.
This supports both old-style summaries (created with TensorFlow ops
that output directly to the `histo` field of the proto) and new-style
summaries (as created by the `tensorboard.plugins.histogram.summary`
module).
"""
plugin_name = metadata.PLUGIN_NAME
# Use a round number + 1 since sampling includes both start and end steps,
# so N+1 samples corresponds to dividing the step sequence into N intervals.
SAMPLE_SIZE = 51
def __init__(self, context):
"""Instantiates HistogramsPlugin via TensorBoard core.
Args:
context: A base_plugin.TBContext instance.
"""
self._db_connection_provider = context.db_connection_provider
self._multiplexer = context.multiplexer
def get_plugin_apps(self):
return {
'/histograms': self.histograms_route,
'/tags': self.tags_route,
}
def is_active(self):
"""This plugin is active iff any run has at least one histograms tag."""
if self._db_connection_provider:
# The plugin is active if one relevant tag can be found in the database.
db = self._db_connection_provider()
cursor = db.execute('''
SELECT
1
FROM Tags
WHERE Tags.plugin_name = ?
LIMIT 1
''', (metadata.PLUGIN_NAME,))
return bool(list(cursor))
return bool(self._multiplexer) and any(self.index_impl().values())
def index_impl(self):
"""Return {runName: {tagName: {displayName: ..., description: ...}}}."""
if self._db_connection_provider:
# Read tags from the database.
db = self._db_connection_provider()
cursor = db.execute('''
SELECT
Tags.tag_name,
Tags.display_name,
Runs.run_name
FROM Tags
JOIN Runs
ON Tags.run_id = Runs.run_id
WHERE
Tags.plugin_name = ?
''', (metadata.PLUGIN_NAME,))
result = collections.defaultdict(dict)
for row in cursor:
tag_name, display_name, run_name = row
result[run_name][tag_name] = {
'displayName': display_name,
# TODO(chihuahua): Populate the description. Currently, the tags
# table does not link with the description table.
'description': '',
}
return result
runs = self._multiplexer.Runs()
result = {run: {} for run in runs}
mapping = self._multiplexer.PluginRunToTagToContent(metadata.PLUGIN_NAME)
for (run, tag_to_content) in six.iteritems(mapping):
for (tag, content) in six.iteritems(tag_to_content):
content = metadata.parse_plugin_metadata(content)
summary_metadata = self._multiplexer.SummaryMetadata(run, tag)
result[run][tag] = {'displayName': summary_metadata.display_name,
'description': plugin_util.markdown_to_safe_html(
summary_metadata.summary_description)}
return result
def histograms_impl(self, tag, run, downsample_to=None):
"""Result of the form `(body, mime_type)`, or `ValueError`.
At most `downsample_to` events will be returned. If this value is
`None`, then no downsampling will be performed.
"""
if self._db_connection_provider:
# Serve data from the database.
db = self._db_connection_provider()
cursor = db.cursor()
# Prefetch the tag ID matching this run and tag.
cursor.execute(
'''
SELECT
tag_id
FROM Tags
JOIN Runs USING (run_id)
WHERE
Runs.run_name = :run
AND Tags.tag_name = :tag
AND Tags.plugin_name = :plugin
''',
{'run': run, 'tag': tag, 'plugin': metadata.PLUGIN_NAME})
row = cursor.fetchone()
if not row:
raise ValueError('No histogram tag %r for run %r' % (tag, run))
(tag_id,) = row
# Fetch tensor values, optionally with linear-spaced sampling by step.
# For steps ranging from s_min to s_max and sample size k, this query
# divides the range into k - 1 equal-sized intervals and returns the
# lowest step at or above each of the k interval boundaries (which always
# includes s_min and s_max, and may be fewer than k results if there are
# intervals where no steps are present). For contiguous steps the results
# can be formally expressed as the following:
# [s_min + math.ceil(i / k * (s_max - s_min)) for i in range(0, k + 1)]
cursor.execute(
'''
SELECT
MIN(step) AS step,
computed_time,
data,
dtype,
shape
FROM Tensors
INNER JOIN (
SELECT
MIN(step) AS min_step,
MAX(step) AS max_step
FROM Tensors
/* Filter out NULL so we can use TensorSeriesStepIndex. */
WHERE series = :tag_id AND step IS NOT NULL
)
/* Ensure we omit reserved rows, which have NULL step values. */
WHERE series = :tag_id AND step IS NOT NULL
/* Bucket rows into sample_size linearly spaced buckets, or do
no sampling if sample_size is NULL. */
GROUP BY
IFNULL(:sample_size - 1, max_step - min_step)
* (step - min_step) / (max_step - min_step)
ORDER BY step
''',
{'tag_id': tag_id, 'sample_size': downsample_to})
events = [(computed_time, step, self._get_values(data, dtype, shape))
for step, computed_time, data, dtype, shape in cursor]
else:
# Serve data from events files.
try:
tensor_events = self._multiplexer.Tensors(run, tag)
except KeyError:
raise ValueError('No histogram tag %r for run %r' % (tag, run))
events = [[e.wall_time, e.step, tf.make_ndarray(e.tensor_proto).tolist()]
for e in tensor_events]
if downsample_to is not None and len(events) > downsample_to:
indices = sorted(random.Random(0).sample(list(range(len(events))),
downsample_to))
events = [events[i] for i in indices]
return (events, 'application/json')
def _get_values(self, data_blob, dtype_enum, shape_string):
"""Obtains values for histogram data given blob and dtype enum.
Args:
data_blob: The blob obtained from the database.
dtype_enum: The enum representing the dtype.
shape_string: A comma-separated string of numbers denoting shape.
Returns:
The histogram values as a list served to the frontend.
"""
buf = np.frombuffer(data_blob, dtype=tf.DType(dtype_enum).as_numpy_dtype)
return buf.reshape([int(i) for i in shape_string.split(',')]).tolist()
@wrappers.Request.application
def tags_route(self, request):
index = self.index_impl()
return http_util.Respond(request, index, 'application/json')
@wrappers.Request.application
def histograms_route(self, request):
"""Given a tag and single run, return array of histogram values."""
tag = request.args.get('tag')
run = request.args.get('run')
try:
(body, mime_type) = self.histograms_impl(
tag, run, downsample_to=self.SAMPLE_SIZE)
code = 200
except ValueError as e:
(body, mime_type) = (str(e), 'text/plain')
code = 400
return http_util.Respond(request, body, mime_type, code=code)
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