234 lines
8.5 KiB
Python
234 lines
8.5 KiB
Python
|
# 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)
|