ai-content-maker/.venv/Lib/site-packages/tensorboard/plugins/scalar/scalars_plugin.py

185 lines
6.6 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 Scalars plugin.
See `http_api.md` in this directory for specifications of the routes for
this plugin.
"""
import csv
import io
import werkzeug.exceptions
from werkzeug import wrappers
from tensorboard import errors
from tensorboard import plugin_util
from tensorboard.backend import http_util
from tensorboard.data import provider
from tensorboard.plugins import base_plugin
from tensorboard.plugins.scalar import metadata
_DEFAULT_DOWNSAMPLING = 1000 # scalars per time series
class OutputFormat:
"""An enum used to list the valid output formats for API calls."""
JSON = "json"
CSV = "csv"
class ScalarsPlugin(base_plugin.TBPlugin):
"""Scalars Plugin for TensorBoard."""
plugin_name = metadata.PLUGIN_NAME
def __init__(self, context):
"""Instantiates ScalarsPlugin via TensorBoard core.
Args:
context: A base_plugin.TBContext instance.
"""
self._downsample_to = (context.sampling_hints or {}).get(
self.plugin_name, _DEFAULT_DOWNSAMPLING
)
self._data_provider = context.data_provider
self._version_checker = plugin_util._MetadataVersionChecker(
data_kind="scalar",
latest_known_version=0,
)
def get_plugin_apps(self):
return {
"/scalars": self.scalars_route,
"/scalars_multirun": self.scalars_multirun_route,
"/tags": self.tags_route,
}
def is_active(self):
return False # `list_plugins` as called by TB core suffices
def frontend_metadata(self):
return base_plugin.FrontendMetadata(element_name="tf-scalar-dashboard")
def index_impl(self, ctx, experiment=None):
"""Return {runName: {tagName: {displayName: ..., description:
...}}}."""
mapping = self._data_provider.list_scalars(
ctx,
experiment_id=experiment,
plugin_name=metadata.PLUGIN_NAME,
)
result = {run: {} for run in mapping}
for (run, tag_to_content) in mapping.items():
for (tag, metadatum) in tag_to_content.items():
md = metadata.parse_plugin_metadata(metadatum.plugin_content)
if not self._version_checker.ok(md.version, run, tag):
continue
description = plugin_util.markdown_to_safe_html(
metadatum.description
)
result[run][tag] = {
"displayName": metadatum.display_name,
"description": description,
}
return result
def scalars_impl(self, ctx, tag, run, experiment, output_format):
"""Result of the form `(body, mime_type)`."""
all_scalars = self._data_provider.read_scalars(
ctx,
experiment_id=experiment,
plugin_name=metadata.PLUGIN_NAME,
downsample=self._downsample_to,
run_tag_filter=provider.RunTagFilter(runs=[run], tags=[tag]),
)
scalars = all_scalars.get(run, {}).get(tag, None)
if scalars is None:
raise errors.NotFoundError(
"No scalar data for run=%r, tag=%r" % (run, tag)
)
values = [(x.wall_time, x.step, x.value) for x in scalars]
if output_format == OutputFormat.CSV:
string_io = io.StringIO()
writer = csv.writer(string_io)
writer.writerow(["Wall time", "Step", "Value"])
writer.writerows(values)
return (string_io.getvalue(), "text/csv")
else:
return (values, "application/json")
def scalars_multirun_impl(self, ctx, tag, runs, experiment):
"""Result of the form `(body, mime_type)`."""
all_scalars = self._data_provider.read_scalars(
ctx,
experiment_id=experiment,
plugin_name=metadata.PLUGIN_NAME,
downsample=self._downsample_to,
run_tag_filter=provider.RunTagFilter(runs=runs, tags=[tag]),
)
body = {
run: [(x.wall_time, x.step, x.value) for x in run_data[tag]]
for (run, run_data) in all_scalars.items()
}
return (body, "application/json")
@wrappers.Request.application
def tags_route(self, request):
ctx = plugin_util.context(request.environ)
experiment = plugin_util.experiment_id(request.environ)
index = self.index_impl(ctx, experiment=experiment)
return http_util.Respond(request, index, "application/json")
@wrappers.Request.application
def scalars_route(self, request):
"""Given a tag and single run, return array of ScalarEvents."""
tag = request.args.get("tag")
run = request.args.get("run")
if tag is None or run is None:
raise errors.InvalidArgumentError(
"Both run and tag must be specified: tag=%r, run=%r"
% (tag, run)
)
ctx = plugin_util.context(request.environ)
experiment = plugin_util.experiment_id(request.environ)
output_format = request.args.get("format")
(body, mime_type) = self.scalars_impl(
ctx, tag, run, experiment, output_format
)
return http_util.Respond(request, body, mime_type)
@wrappers.Request.application
def scalars_multirun_route(self, request):
"""Given a tag and list of runs, return dict of ScalarEvent arrays."""
if request.method != "POST":
raise werkzeug.exceptions.MethodNotAllowed(["POST"])
tags = request.form.getlist("tag")
runs = request.form.getlist("runs")
if len(tags) != 1:
raise errors.InvalidArgumentError(
"tag must be specified exactly once"
)
tag = tags[0]
ctx = plugin_util.context(request.environ)
experiment = plugin_util.experiment_id(request.environ)
(body, mime_type) = self.scalars_multirun_impl(
ctx, tag, runs, experiment
)
return http_util.Respond(request, body, mime_type)