I am done

rl/Lib/site-packages/tensorboard/plugins/scalar/metadata.py

@@ -0,0 +1,61 @@
+# 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.
+# ==============================================================================
+"""Internal information about the scalar plugin."""
+
+
+from tensorboard.compat.proto import summary_pb2
+from tensorboard.plugins.scalar import plugin_data_pb2
+
+PLUGIN_NAME = "scalars"
+
+# The most recent value for the `version` field of the
+# `ScalarPluginData` proto.
+PROTO_VERSION = 0
+
+
+def create_summary_metadata(display_name, description):
+    """Create a `summary_pb2.SummaryMetadata` proto for scalar plugin data.
+
+    Returns:
+      A `summary_pb2.SummaryMetadata` protobuf object.
+    """
+    content = plugin_data_pb2.ScalarPluginData(version=PROTO_VERSION)
+    metadata = summary_pb2.SummaryMetadata(
+        display_name=display_name,
+        summary_description=description,
+        plugin_data=summary_pb2.SummaryMetadata.PluginData(
+            plugin_name=PLUGIN_NAME, content=content.SerializeToString()
+        ),
+    )
+    return metadata
+
+
+def parse_plugin_metadata(content):
+    """Parse summary metadata to a Python object.
+
+    Arguments:
+      content: The `content` field of a `SummaryMetadata` proto
+        corresponding to the scalar plugin.
+
+    Returns:
+      A `ScalarPluginData` protobuf object.
+    """
+    if not isinstance(content, bytes):
+        raise TypeError("Content type must be bytes")
+    result = plugin_data_pb2.ScalarPluginData.FromString(content)
+    if result.version == 0:
+        return result
+    # No other versions known at this time, so no migrations to do.
+    return result

rl/Lib/site-packages/tensorboard/plugins/scalar/plugin_data_pb2.py

@@ -0,0 +1,34 @@
+# -*- coding: utf-8 -*-
+# Generated by the protocol buffer compiler.  DO NOT EDIT!
+# source: tensorboard/plugins/scalar/plugin_data.proto
+"""Generated protocol buffer code."""
+from google.protobuf import descriptor as _descriptor
+from google.protobuf import descriptor_pool as _descriptor_pool
+from google.protobuf import message as _message
+from google.protobuf import reflection as _reflection
+from google.protobuf import symbol_database as _symbol_database
+# @@protoc_insertion_point(imports)
+
+_sym_db = _symbol_database.Default()
+
+
+
+
+DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n,tensorboard/plugins/scalar/plugin_data.proto\x12\x0btensorboard\"#\n\x10ScalarPluginData\x12\x0f\n\x07version\x18\x01 \x01(\x05\x62\x06proto3')
+
+
+
+_SCALARPLUGINDATA = DESCRIPTOR.message_types_by_name['ScalarPluginData']
+ScalarPluginData = _reflection.GeneratedProtocolMessageType('ScalarPluginData', (_message.Message,), {
+  'DESCRIPTOR' : _SCALARPLUGINDATA,
+  '__module__' : 'tensorboard.plugins.scalar.plugin_data_pb2'
+  # @@protoc_insertion_point(class_scope:tensorboard.ScalarPluginData)
+  })
+_sym_db.RegisterMessage(ScalarPluginData)
+
+if _descriptor._USE_C_DESCRIPTORS == False:
+
+  DESCRIPTOR._options = None
+  _SCALARPLUGINDATA._serialized_start=61
+  _SCALARPLUGINDATA._serialized_end=96
+# @@protoc_insertion_point(module_scope)

rl/Lib/site-packages/tensorboard/plugins/scalar/scalars_plugin.py

@@ -0,0 +1,184 @@
+# 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)

rl/Lib/site-packages/tensorboard/plugins/scalar/summary.py

@@ -0,0 +1,111 @@
+# 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.
+# ==============================================================================
+"""Scalar summaries and TensorFlow operations to create them.
+
+A scalar summary stores a single floating-point value, as a rank-0
+tensor.
+"""
+
+
+import numpy as np
+
+from tensorboard.plugins.scalar import metadata
+from tensorboard.plugins.scalar import summary_v2
+
+
+# Export V2 versions.
+scalar = summary_v2.scalar
+scalar_pb = summary_v2.scalar_pb
+
+
+def op(name, data, display_name=None, description=None, collections=None):
+    """Create a legacy scalar summary op.
+
+    Arguments:
+      name: A unique name for the generated summary node.
+      data: A real numeric rank-0 `Tensor`. Must have `dtype` castable
+        to `float32`.
+      display_name: Optional name for this summary in TensorBoard, as a
+        constant `str`. Defaults to `name`.
+      description: Optional long-form description for this summary, as a
+        constant `str`. Markdown is supported. Defaults to empty.
+      collections: Optional list of graph collections keys. The new
+        summary op is added to these collections. Defaults to
+        `[Graph Keys.SUMMARIES]`.
+
+    Returns:
+      A TensorFlow summary op.
+    """
+    # TODO(nickfelt): remove on-demand imports once dep situation is fixed.
+    import tensorflow.compat.v1 as tf
+
+    if display_name is None:
+        display_name = name
+    summary_metadata = metadata.create_summary_metadata(
+        display_name=display_name, description=description
+    )
+    with tf.name_scope(name):
+        with tf.control_dependencies([tf.assert_scalar(data)]):
+            return tf.summary.tensor_summary(
+                name="scalar_summary",
+                tensor=tf.cast(data, tf.float32),
+                collections=collections,
+                summary_metadata=summary_metadata,
+            )
+
+
+def pb(name, data, display_name=None, description=None):
+    """Create a legacy scalar summary protobuf.
+
+    Arguments:
+      name: A unique name for the generated summary, including any desired
+        name scopes.
+      data: A rank-0 `np.array` or array-like form (so raw `int`s and
+        `float`s are fine, too).
+      display_name: Optional name for this summary in TensorBoard, as a
+        `str`. Defaults to `name`.
+      description: Optional long-form description for this summary, as a
+        `str`. Markdown is supported. Defaults to empty.
+
+    Returns:
+      A `tf.Summary` protobuf object.
+    """
+    # TODO(nickfelt): remove on-demand imports once dep situation is fixed.
+    import tensorflow.compat.v1 as tf
+
+    data = np.array(data)
+    if data.shape != ():
+        raise ValueError(
+            "Expected scalar shape for data, saw shape: %s." % data.shape
+        )
+    if data.dtype.kind not in ("b", "i", "u", "f"):  # bool, int, uint, float
+        raise ValueError("Cast %s to float is not supported" % data.dtype.name)
+    tensor = tf.make_tensor_proto(data.astype(np.float32))
+
+    if display_name is None:
+        display_name = name
+    summary_metadata = metadata.create_summary_metadata(
+        display_name=display_name, description=description
+    )
+    tf_summary_metadata = tf.SummaryMetadata.FromString(
+        summary_metadata.SerializeToString()
+    )
+    summary = tf.Summary()
+    summary.value.add(
+        tag="%s/scalar_summary" % name,
+        metadata=tf_summary_metadata,
+        tensor=tensor,
+    )
+    return summary

rl/Lib/site-packages/tensorboard/plugins/scalar/summary_v2.py

@@ -0,0 +1,125 @@
+# Copyright 2018 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.
+# ==============================================================================
+"""Scalar summaries and TensorFlow operations to create them, V2 versions.
+
+A scalar summary stores a single floating-point value, as a rank-0
+tensor.
+"""
+
+
+import numpy as np
+
+from tensorboard.compat import tf2 as tf
+from tensorboard.compat.proto import summary_pb2
+from tensorboard.plugins.scalar import metadata
+from tensorboard.util import tensor_util
+
+
+def scalar(name, data, step=None, description=None):
+    """Write a scalar summary.
+
+    See also `tf.summary.image`, `tf.summary.histogram`, `tf.summary.SummaryWriter`.
+
+    Writes simple numeric values for later analysis in TensorBoard.  Writes go to
+    the current default summary writer. Each summary point is associated with an
+    integral `step` value. This enables the incremental logging of time series
+    data.  A common usage of this API is to log loss during training to produce
+    a loss curve.
+
+    For example:
+
+    ```python
+    test_summary_writer = tf.summary.create_file_writer('test/logdir')
+    with test_summary_writer.as_default():
+        tf.summary.scalar('loss', 0.345, step=1)
+        tf.summary.scalar('loss', 0.234, step=2)
+        tf.summary.scalar('loss', 0.123, step=3)
+    ```
+
+    Multiple independent time series may be logged by giving each series a unique
+    `name` value.
+
+    See [Get started with TensorBoard](https://www.tensorflow.org/tensorboard/get_started)
+    for more examples of effective usage of `tf.summary.scalar`.
+
+    In general, this API expects that data points are logged with a monotonically
+    increasing step value. Duplicate points for a single step or points logged out
+    of order by step are not guaranteed to display as desired in TensorBoard.
+
+    Arguments:
+      name: A name for this summary. The summary tag used for TensorBoard will
+        be this name prefixed by any active name scopes.
+      data: A real numeric scalar value, convertible to a `float32` Tensor.
+      step: Explicit `int64`-castable monotonic step value for this summary. If
+        omitted, this defaults to `tf.summary.experimental.get_step()`, which must
+        not be None.
+      description: Optional long-form description for this summary, as a
+        constant `str`. Markdown is supported. Defaults to empty.
+
+    Returns:
+      True on success, or false if no summary was written because no default
+      summary writer was available.
+
+    Raises:
+      ValueError: if a default writer exists, but no step was provided and
+        `tf.summary.experimental.get_step()` is None.
+    """
+    summary_metadata = metadata.create_summary_metadata(
+        display_name=None, description=description
+    )
+    # TODO(https://github.com/tensorflow/tensorboard/issues/2109): remove fallback
+    summary_scope = (
+        getattr(tf.summary.experimental, "summary_scope", None)
+        or tf.summary.summary_scope
+    )
+    with summary_scope(name, "scalar_summary", values=[data, step]) as (tag, _):
+        tf.debugging.assert_scalar(data)
+        return tf.summary.write(
+            tag=tag,
+            tensor=tf.cast(data, tf.float32),
+            step=step,
+            metadata=summary_metadata,
+        )
+
+
+def scalar_pb(tag, data, description=None):
+    """Create a scalar summary_pb2.Summary protobuf.
+
+    Arguments:
+      tag: String tag for the summary.
+      data: A 0-dimensional `np.array` or a compatible python number type.
+      description: Optional long-form description for this summary, as a
+        `str`. Markdown is supported. Defaults to empty.
+
+    Raises:
+      ValueError: If the type or shape of the data is unsupported.
+
+    Returns:
+      A `summary_pb2.Summary` protobuf object.
+    """
+    arr = np.array(data)
+    if arr.shape != ():
+        raise ValueError(
+            "Expected scalar shape for tensor, got shape: %s." % arr.shape
+        )
+    if arr.dtype.kind not in ("b", "i", "u", "f"):  # bool, int, uint, float
+        raise ValueError("Cast %s to float is not supported" % arr.dtype.name)
+    tensor_proto = tensor_util.make_tensor_proto(arr.astype(np.float32))
+    summary_metadata = metadata.create_summary_metadata(
+        display_name=None, description=description
+    )
+    summary = summary_pb2.Summary()
+    summary.value.add(tag=tag, metadata=summary_metadata, tensor=tensor_proto)
+    return summary