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rl/Lib/site-packages/tensorboard/plugins/projector/__init__.py

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+# Copyright 2016 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.
+# ==============================================================================
+"""Public API for the Embedding Projector.
+
+@@ProjectorPluginAsset
+@@ProjectorConfig
+@@EmbeddingInfo
+@@EmbeddingMetadata
+@@SpriteMetadata
+"""
+
+
+import os
+
+from google.protobuf import text_format as _text_format
+from tensorboard.compat import tf
+from tensorboard.plugins.projector import metadata as _metadata
+from tensorboard.plugins.projector.projector_config_pb2 import (  # noqa: F401
+    EmbeddingInfo,
+)
+from tensorboard.plugins.projector.projector_config_pb2 import (  # noqa: F401
+    SpriteMetadata,
+)
+from tensorboard.plugins.projector.projector_config_pb2 import (  # noqa: F401
+    ProjectorConfig,
+)
+
+
+def visualize_embeddings(logdir, config):
+    """Stores a config file used by the embedding projector.
+
+    Args:
+      logdir: Directory into which to store the config file, as a `str`.
+        For compatibility, can also be a `tf.compat.v1.summary.FileWriter`
+        object open at the desired logdir.
+      config: `tf.contrib.tensorboard.plugins.projector.ProjectorConfig`
+        proto that holds the configuration for the projector such as paths to
+        checkpoint files and metadata files for the embeddings. If
+        `config.model_checkpoint_path` is none, it defaults to the
+        `logdir` used by the summary_writer.
+
+    Raises:
+      ValueError: If the summary writer does not have a `logdir`.
+    """
+    # Convert from `tf.compat.v1.summary.FileWriter` if necessary.
+    logdir = getattr(logdir, "get_logdir", lambda: logdir)()
+
+    # Sanity checks.
+    if logdir is None:
+        raise ValueError("Expected logdir to be a path, but got None")
+
+    # Saving the config file in the logdir.
+    config_pbtxt = _text_format.MessageToString(config)
+    path = os.path.join(logdir, _metadata.PROJECTOR_FILENAME)
+    with tf.io.gfile.GFile(path, "w") as f:
+        f.write(config_pbtxt)

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

@@ -0,0 +1,27 @@
+# Copyright 2020 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 projector plugin."""
+
+
+PLUGIN_NAME = "projector"
+
+PLUGINS_DIR = "plugins"  # must match plugin_asset_util.PLUGINS_DIR
+PLUGIN_ASSETS_NAME = "org_tensorflow_tensorboard_projector"
+
+# FYI - the PROJECTOR_FILENAME is hardcoded in the visualize_embeddings
+# method in tf.contrib.tensorboard.plugins.projector module.
+# TODO(@decentralion): Fix duplication when we find a permanent home for the
+# projector module.
+PROJECTOR_FILENAME = "projector_config.pbtxt"

rl/Lib/site-packages/tensorboard/plugins/projector/projector_config_pb2.py

@@ -0,0 +1,54 @@
+# -*- coding: utf-8 -*-
+# Generated by the protocol buffer compiler.  DO NOT EDIT!
+# source: tensorboard/plugins/projector/projector_config.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'\n4tensorboard/plugins/projector/projector_config.proto\x12\x15tensorboard.projector\">\n\x0eSpriteMetadata\x12\x12\n\nimage_path\x18\x01 \x01(\t\x12\x18\n\x10single_image_dim\x18\x02 \x03(\r\"\xb5\x01\n\rEmbeddingInfo\x12\x13\n\x0btensor_name\x18\x01 \x01(\t\x12\x15\n\rmetadata_path\x18\x02 \x01(\t\x12\x16\n\x0e\x62ookmarks_path\x18\x03 \x01(\t\x12\x14\n\x0ctensor_shape\x18\x04 \x03(\r\x12\x35\n\x06sprite\x18\x05 \x01(\x0b\x32%.tensorboard.projector.SpriteMetadata\x12\x13\n\x0btensor_path\x18\x06 \x01(\t\"\x88\x01\n\x0fProjectorConfig\x12\x1d\n\x15model_checkpoint_path\x18\x01 \x01(\t\x12\x38\n\nembeddings\x18\x02 \x03(\x0b\x32$.tensorboard.projector.EmbeddingInfo\x12\x1c\n\x14model_checkpoint_dir\x18\x03 \x01(\tb\x06proto3')
+
+
+
+_SPRITEMETADATA = DESCRIPTOR.message_types_by_name['SpriteMetadata']
+_EMBEDDINGINFO = DESCRIPTOR.message_types_by_name['EmbeddingInfo']
+_PROJECTORCONFIG = DESCRIPTOR.message_types_by_name['ProjectorConfig']
+SpriteMetadata = _reflection.GeneratedProtocolMessageType('SpriteMetadata', (_message.Message,), {
+  'DESCRIPTOR' : _SPRITEMETADATA,
+  '__module__' : 'tensorboard.plugins.projector.projector_config_pb2'
+  # @@protoc_insertion_point(class_scope:tensorboard.projector.SpriteMetadata)
+  })
+_sym_db.RegisterMessage(SpriteMetadata)
+
+EmbeddingInfo = _reflection.GeneratedProtocolMessageType('EmbeddingInfo', (_message.Message,), {
+  'DESCRIPTOR' : _EMBEDDINGINFO,
+  '__module__' : 'tensorboard.plugins.projector.projector_config_pb2'
+  # @@protoc_insertion_point(class_scope:tensorboard.projector.EmbeddingInfo)
+  })
+_sym_db.RegisterMessage(EmbeddingInfo)
+
+ProjectorConfig = _reflection.GeneratedProtocolMessageType('ProjectorConfig', (_message.Message,), {
+  'DESCRIPTOR' : _PROJECTORCONFIG,
+  '__module__' : 'tensorboard.plugins.projector.projector_config_pb2'
+  # @@protoc_insertion_point(class_scope:tensorboard.projector.ProjectorConfig)
+  })
+_sym_db.RegisterMessage(ProjectorConfig)
+
+if _descriptor._USE_C_DESCRIPTORS == False:
+
+  DESCRIPTOR._options = None
+  _SPRITEMETADATA._serialized_start=79
+  _SPRITEMETADATA._serialized_end=141
+  _EMBEDDINGINFO._serialized_start=144
+  _EMBEDDINGINFO._serialized_end=325
+  _PROJECTORCONFIG._serialized_start=328
+  _PROJECTORCONFIG._serialized_end=464
+# @@protoc_insertion_point(module_scope)

rl/Lib/site-packages/tensorboard/plugins/projector/projector_plugin.py

@@ -0,0 +1,806 @@
+# Copyright 2016 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 Embedding Projector plugin."""
+
+
+import collections
+import functools
+import imghdr
+import mimetypes
+import os
+import threading
+
+import numpy as np
+from werkzeug import wrappers
+
+from google.protobuf import json_format
+from google.protobuf import text_format
+
+from tensorboard import context
+from tensorboard.backend.event_processing import plugin_asset_util
+from tensorboard.backend.http_util import Respond
+from tensorboard.compat import tf
+from tensorboard.plugins import base_plugin
+from tensorboard.plugins.projector import metadata
+from tensorboard.plugins.projector.projector_config_pb2 import ProjectorConfig
+from tensorboard.util import tb_logging
+
+logger = tb_logging.get_logger()
+
+# Number of tensors in the LRU cache.
+_TENSOR_CACHE_CAPACITY = 1
+
+# HTTP routes.
+CONFIG_ROUTE = "/info"
+TENSOR_ROUTE = "/tensor"
+METADATA_ROUTE = "/metadata"
+RUNS_ROUTE = "/runs"
+BOOKMARKS_ROUTE = "/bookmarks"
+SPRITE_IMAGE_ROUTE = "/sprite_image"
+
+_IMGHDR_TO_MIMETYPE = {
+    "bmp": "image/bmp",
+    "gif": "image/gif",
+    "jpeg": "image/jpeg",
+    "png": "image/png",
+}
+_DEFAULT_IMAGE_MIMETYPE = "application/octet-stream"
+
+
+class LRUCache:
+    """LRU cache.
+
+    Used for storing the last used tensor.
+    """
+
+    def __init__(self, size):
+        if size < 1:
+            raise ValueError("The cache size must be >=1")
+        self._size = size
+        self._dict = collections.OrderedDict()
+
+    def get(self, key):
+        try:
+            value = self._dict.pop(key)
+            self._dict[key] = value
+            return value
+        except KeyError:
+            return None
+
+    def set(self, key, value):
+        if value is None:
+            raise ValueError("value must be != None")
+        try:
+            self._dict.pop(key)
+        except KeyError:
+            if len(self._dict) >= self._size:
+                self._dict.popitem(last=False)
+        self._dict[key] = value
+
+
+class EmbeddingMetadata:
+    """Metadata container for an embedding.
+
+    The metadata holds different columns with values used for
+    visualization (color by, label by) in the "Embeddings" tab in
+    TensorBoard.
+    """
+
+    def __init__(self, num_points):
+        """Constructs a metadata for an embedding of the specified size.
+
+        Args:
+          num_points: Number of points in the embedding.
+        """
+        self.num_points = num_points
+        self.column_names = []
+        self.name_to_values = {}
+
+    def add_column(self, column_name, column_values):
+        """Adds a named column of metadata values.
+
+        Args:
+          column_name: Name of the column.
+          column_values: 1D array/list/iterable holding the column values. Must be
+              of length `num_points`. The i-th value corresponds to the i-th point.
+
+        Raises:
+          ValueError: If `column_values` is not 1D array, or of length `num_points`,
+              or the `name` is already used.
+        """
+        # Sanity checks.
+        if isinstance(column_values, list) and isinstance(
+            column_values[0], list
+        ):
+            raise ValueError(
+                '"column_values" must be a flat list, but we detected '
+                "that its first entry is a list"
+            )
+
+        if isinstance(column_values, np.ndarray) and column_values.ndim != 1:
+            raise ValueError(
+                '"column_values" should be of rank 1, '
+                "but is of rank %d" % column_values.ndim
+            )
+        if len(column_values) != self.num_points:
+            raise ValueError(
+                '"column_values" should be of length %d, but is of '
+                "length %d" % (self.num_points, len(column_values))
+            )
+        if column_name in self.name_to_values:
+            raise ValueError(
+                'The column name "%s" is already used' % column_name
+            )
+
+        self.column_names.append(column_name)
+        self.name_to_values[column_name] = column_values
+
+
+def _read_tensor_tsv_file(fpath):
+    with tf.io.gfile.GFile(fpath, "r") as f:
+        tensor = []
+        for line in f:
+            line = line.rstrip("\n")
+            if line:
+                tensor.append(list(map(float, line.split("\t"))))
+    return np.array(tensor, dtype="float32")
+
+
+def _read_tensor_binary_file(fpath, shape):
+    if len(shape) != 2:
+        raise ValueError("Tensor must be 2D, got shape {}".format(shape))
+    tensor = np.fromfile(fpath, dtype="float32")
+    return tensor.reshape(shape)
+
+
+def _assets_dir_to_logdir(assets_dir):
+    sub_path = os.path.sep + metadata.PLUGINS_DIR + os.path.sep
+    if sub_path in assets_dir:
+        two_parents_up = os.pardir + os.path.sep + os.pardir
+        return os.path.abspath(os.path.join(assets_dir, two_parents_up))
+    return assets_dir
+
+
+def _latest_checkpoints_changed(configs, run_path_pairs):
+    """Returns true if the latest checkpoint has changed in any of the runs."""
+    for run_name, assets_dir in run_path_pairs:
+        if run_name not in configs:
+            config = ProjectorConfig()
+            config_fpath = os.path.join(assets_dir, metadata.PROJECTOR_FILENAME)
+            if tf.io.gfile.exists(config_fpath):
+                with tf.io.gfile.GFile(config_fpath, "r") as f:
+                    file_content = f.read()
+                text_format.Parse(file_content, config)
+        else:
+            config = configs[run_name]
+
+        # See if you can find a checkpoint file in the logdir.
+        logdir = _assets_dir_to_logdir(assets_dir)
+        ckpt_path = _find_latest_checkpoint(logdir)
+        if not ckpt_path:
+            continue
+        if config.model_checkpoint_path != ckpt_path:
+            return True
+    return False
+
+
+def _parse_positive_int_param(request, param_name):
+    """Parses and asserts a positive (>0) integer query parameter.
+
+    Args:
+      request: The Werkzeug Request object
+      param_name: Name of the parameter.
+
+    Returns:
+      Param, or None, or -1 if parameter is not a positive integer.
+    """
+    param = request.args.get(param_name)
+    if not param:
+        return None
+    try:
+        param = int(param)
+        if param <= 0:
+            raise ValueError()
+        return param
+    except ValueError:
+        return -1
+
+
+def _rel_to_abs_asset_path(fpath, config_fpath):
+    fpath = os.path.expanduser(fpath)
+    if not os.path.isabs(fpath):
+        return os.path.join(os.path.dirname(config_fpath), fpath)
+    return fpath
+
+
+def _using_tf():
+    """Return true if we're not using the fake TF API stub implementation."""
+    return tf.__version__ != "stub"
+
+
+class ProjectorPlugin(base_plugin.TBPlugin):
+    """Embedding projector."""
+
+    plugin_name = metadata.PLUGIN_NAME
+
+    def __init__(self, context):
+        """Instantiates ProjectorPlugin via TensorBoard core.
+
+        Args:
+          context: A base_plugin.TBContext instance.
+        """
+        self.data_provider = context.data_provider
+        self.logdir = context.logdir
+        self.readers = {}
+        self._run_paths = None
+        self._configs = {}
+        self.config_fpaths = None
+        self.tensor_cache = LRUCache(_TENSOR_CACHE_CAPACITY)
+
+        # Whether the plugin is active (has meaningful data to process and serve).
+        # Once the plugin is deemed active, we no longer re-compute the value
+        # because doing so is potentially expensive.
+        self._is_active = False
+
+        # The running thread that is currently determining whether the plugin is
+        # active. If such a thread exists, do not start a duplicate thread.
+        self._thread_for_determining_is_active = None
+
+    def get_plugin_apps(self):
+        asset_prefix = "tf_projector_plugin"
+        return {
+            RUNS_ROUTE: self._serve_runs,
+            CONFIG_ROUTE: self._serve_config,
+            TENSOR_ROUTE: self._serve_tensor,
+            METADATA_ROUTE: self._serve_metadata,
+            BOOKMARKS_ROUTE: self._serve_bookmarks,
+            SPRITE_IMAGE_ROUTE: self._serve_sprite_image,
+            "/index.js": functools.partial(
+                self._serve_file,
+                os.path.join(asset_prefix, "index.js"),
+            ),
+            "/projector_binary.html": functools.partial(
+                self._serve_file,
+                os.path.join(asset_prefix, "projector_binary.html"),
+            ),
+            "/projector_binary.js": functools.partial(
+                self._serve_file,
+                os.path.join(asset_prefix, "projector_binary.js"),
+            ),
+        }
+
+    def is_active(self):
+        """Determines whether this plugin is active.
+
+        This plugin is only active if any run has an embedding, and only
+        when running against a local log directory.
+
+        Returns:
+          Whether any run has embedding data to show in the projector.
+        """
+        if not self.data_provider or not self.logdir:
+            return False
+
+        if self._is_active:
+            # We have already determined that the projector plugin should be active.
+            # Do not re-compute that. We have no reason to later set this plugin to be
+            # inactive.
+            return True
+
+        if self._thread_for_determining_is_active:
+            # We are currently determining whether the plugin is active. Do not start
+            # a separate thread.
+            return self._is_active
+
+        # The plugin is currently not active. The frontend might check again later.
+        # For now, spin off a separate thread to determine whether the plugin is
+        # active.
+        new_thread = threading.Thread(
+            target=self._determine_is_active,
+            name="ProjectorPluginIsActiveThread",
+        )
+        self._thread_for_determining_is_active = new_thread
+        new_thread.start()
+        return False
+
+    def frontend_metadata(self):
+        return base_plugin.FrontendMetadata(
+            es_module_path="/index.js",
+            disable_reload=True,
+        )
+
+    def _determine_is_active(self):
+        """Determines whether the plugin is active.
+
+        This method is run in a separate thread so that the plugin can
+        offer an immediate response to whether it is active and
+        determine whether it should be active in a separate thread.
+        """
+        self._update_configs()
+        if self._configs:
+            self._is_active = True
+        self._thread_for_determining_is_active = None
+
+    def _update_configs(self):
+        """Updates `self._configs` and `self._run_paths`."""
+        if self.data_provider and self.logdir:
+            # Create a background context; we may not be in a request.
+            ctx = context.RequestContext()
+            run_paths = {
+                run.run_name: os.path.join(self.logdir, run.run_name)
+                for run in self.data_provider.list_runs(ctx, experiment_id="")
+            }
+        else:
+            run_paths = {}
+        run_paths_changed = run_paths != self._run_paths
+        self._run_paths = run_paths
+
+        run_path_pairs = list(self._run_paths.items())
+        self._append_plugin_asset_directories(run_path_pairs)
+        # Also accept the root logdir as a model checkpoint directory,
+        # so that the projector still works when there are no runs.
+        # (Case on `run` rather than `path` to avoid issues with
+        # absolute/relative paths on any filesystems.)
+        if "." not in self._run_paths:
+            run_path_pairs.append((".", self.logdir))
+        if run_paths_changed or _latest_checkpoints_changed(
+            self._configs, run_path_pairs
+        ):
+            self.readers = {}
+            self._configs, self.config_fpaths = self._read_latest_config_files(
+                run_path_pairs
+            )
+            self._augment_configs_with_checkpoint_info()
+
+    def _augment_configs_with_checkpoint_info(self):
+        for run, config in self._configs.items():
+            for embedding in config.embeddings:
+                # Normalize the name of the embeddings.
+                if embedding.tensor_name.endswith(":0"):
+                    embedding.tensor_name = embedding.tensor_name[:-2]
+                # Find the size of embeddings associated with a tensors file.
+                if embedding.tensor_path:
+                    fpath = _rel_to_abs_asset_path(
+                        embedding.tensor_path, self.config_fpaths[run]
+                    )
+                    tensor = self.tensor_cache.get((run, embedding.tensor_name))
+                    if tensor is None:
+                        try:
+                            tensor = _read_tensor_tsv_file(fpath)
+                        except UnicodeDecodeError:
+                            tensor = _read_tensor_binary_file(
+                                fpath, embedding.tensor_shape
+                            )
+                        self.tensor_cache.set(
+                            (run, embedding.tensor_name), tensor
+                        )
+                    if not embedding.tensor_shape:
+                        embedding.tensor_shape.extend(
+                            [len(tensor), len(tensor[0])]
+                        )
+
+            reader = self._get_reader_for_run(run)
+            if not reader:
+                continue
+            # Augment the configuration with the tensors in the checkpoint file.
+            special_embedding = None
+            if config.embeddings and not config.embeddings[0].tensor_name:
+                special_embedding = config.embeddings[0]
+                config.embeddings.remove(special_embedding)
+            var_map = reader.get_variable_to_shape_map()
+            for tensor_name, tensor_shape in var_map.items():
+                if len(tensor_shape) != 2:
+                    continue
+                # Optimizer slot values are the same shape as embeddings
+                # but are not embeddings.
+                if ".OPTIMIZER_SLOT" in tensor_name:
+                    continue
+                embedding = self._get_embedding(tensor_name, config)
+                if not embedding:
+                    embedding = config.embeddings.add()
+                    embedding.tensor_name = tensor_name
+                    if special_embedding:
+                        embedding.metadata_path = (
+                            special_embedding.metadata_path
+                        )
+                        embedding.bookmarks_path = (
+                            special_embedding.bookmarks_path
+                        )
+                if not embedding.tensor_shape:
+                    embedding.tensor_shape.extend(tensor_shape)
+
+        # Remove configs that do not have any valid (2D) tensors.
+        runs_to_remove = []
+        for run, config in self._configs.items():
+            if not config.embeddings:
+                runs_to_remove.append(run)
+        for run in runs_to_remove:
+            del self._configs[run]
+            del self.config_fpaths[run]
+
+    def _read_latest_config_files(self, run_path_pairs):
+        """Reads and returns the projector config files in every run
+        directory."""
+        configs = {}
+        config_fpaths = {}
+        for run_name, assets_dir in run_path_pairs:
+            config = ProjectorConfig()
+            config_fpath = os.path.join(assets_dir, metadata.PROJECTOR_FILENAME)
+            if tf.io.gfile.exists(config_fpath):
+                with tf.io.gfile.GFile(config_fpath, "r") as f:
+                    file_content = f.read()
+                text_format.Parse(file_content, config)
+            has_tensor_files = False
+            for embedding in config.embeddings:
+                if embedding.tensor_path:
+                    if not embedding.tensor_name:
+                        embedding.tensor_name = os.path.basename(
+                            embedding.tensor_path
+                        )
+                    has_tensor_files = True
+                    break
+
+            if not config.model_checkpoint_path:
+                # See if you can find a checkpoint file in the logdir.
+                logdir = _assets_dir_to_logdir(assets_dir)
+                ckpt_path = _find_latest_checkpoint(logdir)
+                if not ckpt_path and not has_tensor_files:
+                    continue
+                if ckpt_path:
+                    config.model_checkpoint_path = ckpt_path
+
+            # Sanity check for the checkpoint file existing.
+            if (
+                config.model_checkpoint_path
+                and _using_tf()
+                and not tf.io.gfile.glob(config.model_checkpoint_path + "*")
+            ):
+                logger.warning(
+                    'Checkpoint file "%s" not found',
+                    config.model_checkpoint_path,
+                )
+                continue
+            configs[run_name] = config
+            config_fpaths[run_name] = config_fpath
+        return configs, config_fpaths
+
+    def _get_reader_for_run(self, run):
+        if run in self.readers:
+            return self.readers[run]
+
+        config = self._configs[run]
+        reader = None
+        if config.model_checkpoint_path and _using_tf():
+            try:
+                reader = tf.train.load_checkpoint(config.model_checkpoint_path)
+            except Exception:  # pylint: disable=broad-except
+                logger.warning(
+                    'Failed reading "%s"', config.model_checkpoint_path
+                )
+        self.readers[run] = reader
+        return reader
+
+    def _get_metadata_file_for_tensor(self, tensor_name, config):
+        embedding_info = self._get_embedding(tensor_name, config)
+        if embedding_info:
+            return embedding_info.metadata_path
+        return None
+
+    def _get_bookmarks_file_for_tensor(self, tensor_name, config):
+        embedding_info = self._get_embedding(tensor_name, config)
+        if embedding_info:
+            return embedding_info.bookmarks_path
+        return None
+
+    def _canonical_tensor_name(self, tensor_name):
+        if ":" not in tensor_name:
+            return tensor_name + ":0"
+        else:
+            return tensor_name
+
+    def _get_embedding(self, tensor_name, config):
+        if not config.embeddings:
+            return None
+        for info in config.embeddings:
+            if self._canonical_tensor_name(
+                info.tensor_name
+            ) == self._canonical_tensor_name(tensor_name):
+                return info
+        return None
+
+    def _append_plugin_asset_directories(self, run_path_pairs):
+        extra = []
+        plugin_assets_name = metadata.PLUGIN_ASSETS_NAME
+        for run, logdir in run_path_pairs:
+            assets = plugin_asset_util.ListAssets(logdir, plugin_assets_name)
+            if metadata.PROJECTOR_FILENAME not in assets:
+                continue
+            assets_dir = os.path.join(
+                self._run_paths[run], metadata.PLUGINS_DIR, plugin_assets_name
+            )
+            assets_path_pair = (run, os.path.abspath(assets_dir))
+            extra.append(assets_path_pair)
+        run_path_pairs.extend(extra)
+
+    @wrappers.Request.application
+    def _serve_file(self, file_path, request):
+        """Returns a resource file."""
+        res_path = os.path.join(os.path.dirname(__file__), file_path)
+        with open(res_path, "rb") as read_file:
+            mimetype = mimetypes.guess_type(file_path)[0]
+            return Respond(request, read_file.read(), content_type=mimetype)
+
+    @wrappers.Request.application
+    def _serve_runs(self, request):
+        """Returns a list of runs that have embeddings."""
+        self._update_configs()
+        return Respond(request, list(self._configs.keys()), "application/json")
+
+    @wrappers.Request.application
+    def _serve_config(self, request):
+        run = request.args.get("run")
+        if run is None:
+            return Respond(
+                request, 'query parameter "run" is required', "text/plain", 400
+            )
+        self._update_configs()
+        config = self._configs.get(run)
+        if config is None:
+            return Respond(
+                request, 'Unknown run: "%s"' % run, "text/plain", 400
+            )
+        return Respond(
+            request, json_format.MessageToJson(config), "application/json"
+        )
+
+    @wrappers.Request.application
+    def _serve_metadata(self, request):
+        run = request.args.get("run")
+        if run is None:
+            return Respond(
+                request, 'query parameter "run" is required', "text/plain", 400
+            )
+
+        name = request.args.get("name")
+        if name is None:
+            return Respond(
+                request, 'query parameter "name" is required', "text/plain", 400
+            )
+
+        num_rows = _parse_positive_int_param(request, "num_rows")
+        if num_rows == -1:
+            return Respond(
+                request,
+                "query parameter num_rows must be integer > 0",
+                "text/plain",
+                400,
+            )
+
+        self._update_configs()
+        config = self._configs.get(run)
+        if config is None:
+            return Respond(
+                request, 'Unknown run: "%s"' % run, "text/plain", 400
+            )
+        fpath = self._get_metadata_file_for_tensor(name, config)
+        if not fpath:
+            return Respond(
+                request,
+                'No metadata file found for tensor "%s" in the config file "%s"'
+                % (name, self.config_fpaths[run]),
+                "text/plain",
+                400,
+            )
+        fpath = _rel_to_abs_asset_path(fpath, self.config_fpaths[run])
+        if not tf.io.gfile.exists(fpath) or tf.io.gfile.isdir(fpath):
+            return Respond(
+                request,
+                '"%s" not found, or is not a file' % fpath,
+                "text/plain",
+                400,
+            )
+
+        num_header_rows = 0
+        with tf.io.gfile.GFile(fpath, "r") as f:
+            lines = []
+            # Stream reading the file with early break in case the file doesn't fit in
+            # memory.
+            for line in f:
+                lines.append(line)
+                if len(lines) == 1 and "\t" in lines[0]:
+                    num_header_rows = 1
+                if num_rows and len(lines) >= num_rows + num_header_rows:
+                    break
+        return Respond(request, "".join(lines), "text/plain")
+
+    @wrappers.Request.application
+    def _serve_tensor(self, request):
+        run = request.args.get("run")
+        if run is None:
+            return Respond(
+                request, 'query parameter "run" is required', "text/plain", 400
+            )
+
+        name = request.args.get("name")
+        if name is None:
+            return Respond(
+                request, 'query parameter "name" is required', "text/plain", 400
+            )
+
+        num_rows = _parse_positive_int_param(request, "num_rows")
+        if num_rows == -1:
+            return Respond(
+                request,
+                "query parameter num_rows must be integer > 0",
+                "text/plain",
+                400,
+            )
+
+        self._update_configs()
+        config = self._configs.get(run)
+        if config is None:
+            return Respond(
+                request, 'Unknown run: "%s"' % run, "text/plain", 400
+            )
+        tensor = self.tensor_cache.get((run, name))
+        if tensor is None:
+            # See if there is a tensor file in the config.
+            embedding = self._get_embedding(name, config)
+
+            if embedding and embedding.tensor_path:
+                fpath = _rel_to_abs_asset_path(
+                    embedding.tensor_path, self.config_fpaths[run]
+                )
+                if not tf.io.gfile.exists(fpath):
+                    return Respond(
+                        request,
+                        'Tensor file "%s" does not exist' % fpath,
+                        "text/plain",
+                        400,
+                    )
+                try:
+                    tensor = _read_tensor_tsv_file(fpath)
+                except UnicodeDecodeError:
+                    tensor = _read_tensor_binary_file(
+                        fpath, embedding.tensor_shape
+                    )
+            else:
+                reader = self._get_reader_for_run(run)
+                if not reader or not reader.has_tensor(name):
+                    return Respond(
+                        request,
+                        'Tensor "%s" not found in checkpoint dir "%s"'
+                        % (name, config.model_checkpoint_path),
+                        "text/plain",
+                        400,
+                    )
+                try:
+                    tensor = reader.get_tensor(name)
+                except tf.errors.InvalidArgumentError as e:
+                    return Respond(request, str(e), "text/plain", 400)
+
+            self.tensor_cache.set((run, name), tensor)
+
+        if num_rows:
+            tensor = tensor[:num_rows]
+        if tensor.dtype != "float32":
+            tensor = tensor.astype(dtype="float32", copy=False)
+        data_bytes = tensor.tobytes()
+        return Respond(request, data_bytes, "application/octet-stream")
+
+    @wrappers.Request.application
+    def _serve_bookmarks(self, request):
+        run = request.args.get("run")
+        if not run:
+            return Respond(
+                request, 'query parameter "run" is required', "text/plain", 400
+            )
+
+        name = request.args.get("name")
+        if name is None:
+            return Respond(
+                request, 'query parameter "name" is required', "text/plain", 400
+            )
+
+        self._update_configs()
+        config = self._configs.get(run)
+        if config is None:
+            return Respond(
+                request, 'Unknown run: "%s"' % run, "text/plain", 400
+            )
+        fpath = self._get_bookmarks_file_for_tensor(name, config)
+        if not fpath:
+            return Respond(
+                request,
+                'No bookmarks file found for tensor "%s" in the config file "%s"'
+                % (name, self.config_fpaths[run]),
+                "text/plain",
+                400,
+            )
+        fpath = _rel_to_abs_asset_path(fpath, self.config_fpaths[run])
+        if not tf.io.gfile.exists(fpath) or tf.io.gfile.isdir(fpath):
+            return Respond(
+                request,
+                '"%s" not found, or is not a file' % fpath,
+                "text/plain",
+                400,
+            )
+
+        bookmarks_json = None
+        with tf.io.gfile.GFile(fpath, "rb") as f:
+            bookmarks_json = f.read()
+        return Respond(request, bookmarks_json, "application/json")
+
+    @wrappers.Request.application
+    def _serve_sprite_image(self, request):
+        run = request.args.get("run")
+        if not run:
+            return Respond(
+                request, 'query parameter "run" is required', "text/plain", 400
+            )
+
+        name = request.args.get("name")
+        if name is None:
+            return Respond(
+                request, 'query parameter "name" is required', "text/plain", 400
+            )
+
+        self._update_configs()
+        config = self._configs.get(run)
+        if config is None:
+            return Respond(
+                request, 'Unknown run: "%s"' % run, "text/plain", 400
+            )
+
+        embedding_info = self._get_embedding(name, config)
+        if not embedding_info or not embedding_info.sprite.image_path:
+            return Respond(
+                request,
+                'No sprite image file found for tensor "%s" in the config file "%s"'
+                % (name, self.config_fpaths[run]),
+                "text/plain",
+                400,
+            )
+
+        fpath = os.path.expanduser(embedding_info.sprite.image_path)
+        fpath = _rel_to_abs_asset_path(fpath, self.config_fpaths[run])
+        if not tf.io.gfile.exists(fpath) or tf.io.gfile.isdir(fpath):
+            return Respond(
+                request,
+                '"%s" does not exist or is directory' % fpath,
+                "text/plain",
+                400,
+            )
+        f = tf.io.gfile.GFile(fpath, "rb")
+        encoded_image_string = f.read()
+        f.close()
+        image_type = imghdr.what(None, encoded_image_string)
+        mime_type = _IMGHDR_TO_MIMETYPE.get(image_type, _DEFAULT_IMAGE_MIMETYPE)
+        return Respond(request, encoded_image_string, mime_type)
+
+
+def _find_latest_checkpoint(dir_path):
+    if not _using_tf():
+        return None
+    try:
+        ckpt_path = tf.train.latest_checkpoint(dir_path)
+        if not ckpt_path:
+            # Check the parent directory.
+            ckpt_path = tf.train.latest_checkpoint(
+                os.path.join(dir_path, os.pardir)
+            )
+        return ckpt_path
+    except tf.errors.NotFoundError:
+        return None

rl/Lib/site-packages/tensorboard/plugins/projector/tf_projector_plugin/index.js

@@ -0,0 +1,32 @@
+// Copyright 2019 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.
+
+export function render() {
+  const style = document.createElement('style');
+  style.innerText = `
+html,
+body,
+iframe {
+  border: 0;
+  display: block;
+  height: 100%;
+  margin: 0;
+  width: 100%;
+}`;
+  document.head.appendChild(style);
+
+  const iframe = document.createElement('iframe');
+  iframe.src = './projector_binary.html';
+  document.body.appendChild(iframe);
+}

rl/Lib/site-packages/tensorboard/plugins/projector/tf_projector_plugin/projector_binary.html

@@ -0,0 +1,523 @@
+<!doctype html><!--
+@license
+Copyright 2019 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.
+--><html><head><style>
+@font-face {
+  font-family: 'Roboto';
+  font-style: normal;
+  font-weight: 400;
+  src: local('Roboto'), local('Roboto-Regular'), url(/font-roboto/uYECMKoHcO9x1wdmbyHIm3-_kf6ByYO6CLYdB4HQE-Y.woff2) format('woff2');
+  unicode-range: U+0400-045F, U+0490-0491, U+04B0-04B1, U+2116;
+}
+@font-face {
+  font-family: 'Roboto';
+  font-style: normal;
+  font-weight: 400;
+  src: local('Roboto'), local('Roboto-Regular'), url(/font-roboto/sTdaA6j0Psb920Vjv-mrzH-_kf6ByYO6CLYdB4HQE-Y.woff2) format('woff2');
+  unicode-range: U+0460-052F, U+20B4, U+2DE0-2DFF, U+A640-A69F;
+}
+@font-face {
+  font-family: 'Roboto';
+  font-style: normal;
+  font-weight: 400;
+  src: local('Roboto'), local('Roboto-Regular'), url(/font-roboto/_VYFx-s824kXq_Ul2BHqYH-_kf6ByYO6CLYdB4HQE-Y.woff2) format('woff2');
+  unicode-range: U+0370-03FF;
+}
+@font-face {
+  font-family: 'Roboto';
+  font-style: normal;
+  font-weight: 400;
+  src: local('Roboto'), local('Roboto-Regular'), url(/font-roboto/tnj4SB6DNbdaQnsM8CFqBX-_kf6ByYO6CLYdB4HQE-Y.woff2) format('woff2');
+  unicode-range: U+1F00-1FFF;
+}
+@font-face {
+  font-family: 'Roboto';
+  font-style: normal;
+  font-weight: 400;
+  src: local('Roboto'), local('Roboto-Regular'), url(/font-roboto/oMMgfZMQthOryQo9n22dcuvvDin1pK8aKteLpeZ5c0A.woff2) format('woff2');
+  unicode-range: U+0000-00FF, U+0131, U+0152-0153, U+02C6, U+02DA, U+02DC, U+2000-206F, U+2074, U+20AC, U+2212, U+2215;
+}
+@font-face {
+  font-family: 'Roboto';
+  font-style: normal;
+  font-weight: 400;
+  src: local('Roboto'), local('Roboto-Regular'), url(/font-roboto/Ks_cVxiCiwUWVsFWFA3Bjn-_kf6ByYO6CLYdB4HQE-Y.woff2) format('woff2');
+  unicode-range: U+0100-024F, U+1E00-1EFF, U+20A0-20AB, U+20AD-20CF, U+2C60-2C7F, U+A720-A7FF;
+}
+@font-face {
+  font-family: 'Roboto';
+  font-style: normal;
+  font-weight: 400;
+  src: local('Roboto'), local('Roboto-Regular'), url(/font-roboto/NJ4vxlgWwWbEsv18dAhqnn-_kf6ByYO6CLYdB4HQE-Y.woff2) format('woff2');
+  unicode-range: U+0102-0103, U+1EA0-1EF9, U+20AB;
+}
+@font-face {
+  font-family: 'Roboto';
+  font-style: normal;
+  font-weight: 700;
+  src: local('Roboto Bold'), local('Roboto-Bold'), url(/font-roboto/isZ-wbCXNKAbnjo6_TwHToX0hVgzZQUfRDuZrPvH3D8.woff2) format('woff2');
+  unicode-range: U+0400-045F, U+0490-0491, U+04B0-04B1, U+2116;
+}
+@font-face {
+  font-family: 'Roboto';
+  font-style: normal;
+  font-weight: 700;
+  src: local('Roboto Bold'), local('Roboto-Bold'), url(/font-roboto/77FXFjRbGzN4aCrSFhlh3oX0hVgzZQUfRDuZrPvH3D8.woff2) format('woff2');
+  unicode-range: U+0460-052F, U+20B4, U+2DE0-2DFF, U+A640-A69F;
+}
+@font-face {
+  font-family: 'Roboto';
+  font-style: normal;
+  font-weight: 700;
+  src: local('Roboto Bold'), local('Roboto-Bold'), url(/font-roboto/jSN2CGVDbcVyCnfJfjSdfIX0hVgzZQUfRDuZrPvH3D8.woff2) format('woff2');
+  unicode-range: U+0370-03FF;
+}
+@font-face {
+  font-family: 'Roboto';
+  font-style: normal;
+  font-weight: 700;
+  src: local('Roboto Bold'), local('Roboto-Bold'), url(/font-roboto/UX6i4JxQDm3fVTc1CPuwqoX0hVgzZQUfRDuZrPvH3D8.woff2) format('woff2');
+  unicode-range: U+1F00-1FFF;
+}
+@font-face {
+  font-family: 'Roboto';
+  font-style: normal;
+  font-weight: 700;
+  src: local('Roboto Bold'), local('Roboto-Bold'), url(/font-roboto/d-6IYplOFocCacKzxwXSOJBw1xU1rKptJj_0jans920.woff2) format('woff2');
+  unicode-range: U+0000-00FF, U+0131, U+0152-0153, U+02C6, U+02DA, U+02DC, U+2000-206F, U+2074, U+20AC, U+2212, U+2215;
+}
+@font-face {
+  font-family: 'Roboto';
+  font-style: normal;
+  font-weight: 700;
+  src: local('Roboto Bold'), local('Roboto-Bold'), url(/font-roboto/97uahxiqZRoncBaCEI3aW4X0hVgzZQUfRDuZrPvH3D8.woff2) format('woff2');
+  unicode-range: U+0100-024F, U+1E00-1EFF, U+20A0-20AB, U+20AD-20CF, U+2C60-2C7F, U+A720-A7FF;
+}
+@font-face {
+  font-family: 'Roboto';
+  font-style: normal;
+  font-weight: 700;
+  src: local('Roboto Bold'), local('Roboto-Bold'), url(/font-roboto/PwZc-YbIL414wB9rB1IAPYX0hVgzZQUfRDuZrPvH3D8.woff2) format('woff2');
+  unicode-range: U+0102-0103, U+1EA0-1EF9, U+20AB;
+}
+@font-face {
+  font-family: 'Roboto';
+  font-style: italic;
+  font-weight: 700;
+  src: local('Roboto Bold Italic'), local('Roboto-BoldItalic'), url(/font-roboto/t6Nd4cfPRhZP44Q5QAjcC14sYYdJg5dU2qzJEVSuta0.woff2) format('woff2');
+  unicode-range: U+0400-045F, U+0490-0491, U+04B0-04B1, U+2116;
+}
+@font-face {
+  font-family: 'Roboto';
+  font-style: italic;
+  font-weight: 700;
+  src: local('Roboto Bold Italic'), local('Roboto-BoldItalic'), url(/font-roboto/t6Nd4cfPRhZP44Q5QAjcC_ZraR2Tg8w2lzm7kLNL0-w.woff2) format('woff2');
+  unicode-range: U+0460-052F, U+20B4, U+2DE0-2DFF, U+A640-A69F;
+}
+@font-face {
+  font-family: 'Roboto';
+  font-style: italic;
+  font-weight: 700;
+  src: local('Roboto Bold Italic'), local('Roboto-BoldItalic'), url(/font-roboto/t6Nd4cfPRhZP44Q5QAjcCwt_Rm691LTebKfY2ZkKSmI.woff2) format('woff2');
+  unicode-range: U+0370-03FF;
+}
+@font-face {
+  font-family: 'Roboto';
+  font-style: italic;
+  font-weight: 700;
+  src: local('Roboto Bold Italic'), local('Roboto-BoldItalic'), url(/font-roboto/t6Nd4cfPRhZP44Q5QAjcC1BW26QxpSj-_ZKm_xT4hWw.woff2) format('woff2');
+  unicode-range: U+1F00-1FFF;
+}
+@font-face {
+  font-family: 'Roboto';
+  font-style: italic;
+  font-weight: 700;
+  src: local('Roboto Bold Italic'), local('Roboto-BoldItalic'), url(/font-roboto/t6Nd4cfPRhZP44Q5QAjcC4gp9Q8gbYrhqGlRav_IXfk.woff2) format('woff2');
+  unicode-range: U+0000-00FF, U+0131, U+0152-0153, U+02C6, U+02DA, U+02DC, U+2000-206F, U+2074, U+20AC, U+2212, U+2215;
+}
+@font-face {
+  font-family: 'Roboto';
+  font-style: italic;
+  font-weight: 700;
+  src: local('Roboto Bold Italic'), local('Roboto-BoldItalic'), url(/font-roboto/t6Nd4cfPRhZP44Q5QAjcC6E8kM4xWR1_1bYURRojRGc.woff2) format('woff2');
+  unicode-range: U+0100-024F, U+1E00-1EFF, U+20A0-20AB, U+20AD-20CF, U+2C60-2C7F, U+A720-A7FF;
+}
+@font-face {
+  font-family: 'Roboto';
+  font-style: italic;
+  font-weight: 700;
+  src: local('Roboto Bold Italic'), local('Roboto-BoldItalic'), url(/font-roboto/t6Nd4cfPRhZP44Q5QAjcC9DiNsR5a-9Oe_Ivpu8XWlY.woff2) format('woff2');
+  unicode-range: U+0102-0103, U+1EA0-1EF9, U+20AB;
+}
+@font-face {
+  font-family: 'Roboto';
+  font-style: italic;
+  font-weight: 400;
+  src: local('Roboto Italic'), local('Roboto-Italic'), url(/font-roboto/OpXUqTo0UgQQhGj_SFdLWBkAz4rYn47Zy2rvigWQf6w.woff2) format('woff2');
+  unicode-range: U+0400-045F, U+0490-0491, U+04B0-04B1, U+2116;
+}
+@font-face {
+  font-family: 'Roboto';
+  font-style: italic;
+  font-weight: 400;
+  src: local('Roboto Italic'), local('Roboto-Italic'), url(/font-roboto/WxrXJa0C3KdtC7lMafG4dRkAz4rYn47Zy2rvigWQf6w.woff2) format('woff2');
+  unicode-range: U+0460-052F, U+20B4, U+2DE0-2DFF, U+A640-A69F;
+}
+@font-face {
+  font-family: 'Roboto';
+  font-style: italic;
+  font-weight: 400;
+  src: local('Roboto Italic'), local('Roboto-Italic'), url(/font-roboto/cDKhRaXnQTOVbaoxwdOr9xkAz4rYn47Zy2rvigWQf6w.woff2) format('woff2');
+  unicode-range: U+0370-03FF;
+}
+@font-face {
+  font-family: 'Roboto';
+  font-style: italic;
+  font-weight: 400;
+  src: local('Roboto Italic'), local('Roboto-Italic'), url(/font-roboto/1hZf02POANh32k2VkgEoUBkAz4rYn47Zy2rvigWQf6w.woff2) format('woff2');
+  unicode-range: U+1F00-1FFF;
+}
+@font-face {
+  font-family: 'Roboto';
+  font-style: italic;
+  font-weight: 400;
+  src: local('Roboto Italic'), local('Roboto-Italic'), url(/font-roboto/vPcynSL0qHq_6dX7lKVByXYhjbSpvc47ee6xR_80Hnw.woff2) format('woff2');
+  unicode-range: U+0000-00FF, U+0131, U+0152-0153, U+02C6, U+02DA, U+02DC, U+2000-206F, U+2074, U+20AC, U+2212, U+2215;
+}
+@font-face {
+  font-family: 'Roboto';
+  font-style: italic;
+  font-weight: 400;
+  src: local('Roboto Italic'), local('Roboto-Italic'), url(/font-roboto/vSzulfKSK0LLjjfeaxcREhkAz4rYn47Zy2rvigWQf6w.woff2) format('woff2');
+  unicode-range: U+0100-024F, U+1E00-1EFF, U+20A0-20AB, U+20AD-20CF, U+2C60-2C7F, U+A720-A7FF;
+}
+@font-face {
+  font-family: 'Roboto';
+  font-style: italic;
+  font-weight: 400;
+  src: local('Roboto Italic'), local('Roboto-Italic'), url(/font-roboto/K23cxWVTrIFD6DJsEVi07RkAz4rYn47Zy2rvigWQf6w.woff2) format('woff2');
+  unicode-range: U+0102-0103, U+1EA0-1EF9, U+20AB;
+}
+@font-face {
+  font-family: 'Roboto';
+  font-style: normal;
+  font-weight: 300;
+  src: local('Roboto Light'), local('Roboto-Light'), url(/font-roboto/Fl4y0QdOxyyTHEGMXX8kcYX0hVgzZQUfRDuZrPvH3D8.woff2) format('woff2');
+  unicode-range: U+0400-045F, U+0490-0491, U+04B0-04B1, U+2116;
+}
+@font-face {
+  font-family: 'Roboto';
+  font-style: normal;
+  font-weight: 300;
+  src: local('Roboto Light'), local('Roboto-Light'), url(/font-roboto/0eC6fl06luXEYWpBSJvXCIX0hVgzZQUfRDuZrPvH3D8.woff2) format('woff2');
+  unicode-range: U+0460-052F, U+20B4, U+2DE0-2DFF, U+A640-A69F;
+}
+@font-face {
+  font-family: 'Roboto';
+  font-style: normal;
+  font-weight: 300;
+  src: local('Roboto Light'), local('Roboto-Light'), url(/font-roboto/I3S1wsgSg9YCurV6PUkTOYX0hVgzZQUfRDuZrPvH3D8.woff2) format('woff2');
+  unicode-range: U+0370-03FF;
+}
+@font-face {
+  font-family: 'Roboto';
+  font-style: normal;
+  font-weight: 300;
+  src: local('Roboto Light'), local('Roboto-Light'), url(/font-roboto/-L14Jk06m6pUHB-5mXQQnYX0hVgzZQUfRDuZrPvH3D8.woff2) format('woff2');
+  unicode-range: U+1F00-1FFF;
+}
+@font-face {
+  font-family: 'Roboto';
+  font-style: normal;
+  font-weight: 300;
+  src: local('Roboto Light'), local('Roboto-Light'), url(/font-roboto/Hgo13k-tfSpn0qi1SFdUfZBw1xU1rKptJj_0jans920.woff2) format('woff2');
+  unicode-range: U+0000-00FF, U+0131, U+0152-0153, U+02C6, U+02DA, U+02DC, U+2000-206F, U+2074, U+20AC, U+2212, U+2215;
+}
+@font-face {
+  font-family: 'Roboto';
+  font-style: normal;
+  font-weight: 300;
+  src: local('Roboto Light'), local('Roboto-Light'), url(/font-roboto/Pru33qjShpZSmG3z6VYwnYX0hVgzZQUfRDuZrPvH3D8.woff2) format('woff2');
+  unicode-range: U+0100-024F, U+1E00-1EFF, U+20A0-20AB, U+20AD-20CF, U+2C60-2C7F, U+A720-A7FF;
+}
+@font-face {
+  font-family: 'Roboto';
+  font-style: normal;
+  font-weight: 300;
+  src: local('Roboto Light'), local('Roboto-Light'), url(/font-roboto/NYDWBdD4gIq26G5XYbHsFIX0hVgzZQUfRDuZrPvH3D8.woff2) format('woff2');
+  unicode-range: U+0102-0103, U+1EA0-1EF9, U+20AB;
+}
+@font-face {
+  font-family: 'Roboto';
+  font-style: italic;
+  font-weight: 300;
+  src: local('Roboto Light Italic'), local('Roboto-LightItalic'), url(/font-roboto/7m8l7TlFO-S3VkhHuR0at14sYYdJg5dU2qzJEVSuta0.woff2) format('woff2');
+  unicode-range: U+0400-045F, U+0490-0491, U+04B0-04B1, U+2116;
+}
+@font-face {
+  font-family: 'Roboto';
+  font-style: italic;
+  font-weight: 300;
+  src: local('Roboto Light Italic'), local('Roboto-LightItalic'), url(/font-roboto/7m8l7TlFO-S3VkhHuR0at_ZraR2Tg8w2lzm7kLNL0-w.woff2) format('woff2');
+  unicode-range: U+0460-052F, U+20B4, U+2DE0-2DFF, U+A640-A69F;
+}
+@font-face {
+  font-family: 'Roboto';
+  font-style: italic;
+  font-weight: 300;
+  src: local('Roboto Light Italic'), local('Roboto-LightItalic'), url(/font-roboto/7m8l7TlFO-S3VkhHuR0atwt_Rm691LTebKfY2ZkKSmI.woff2) format('woff2');
+  unicode-range: U+0370-03FF;
+}
+@font-face {
+  font-family: 'Roboto';
+  font-style: italic;
+  font-weight: 300;
+  src: local('Roboto Light Italic'), local('Roboto-LightItalic'), url(/font-roboto/7m8l7TlFO-S3VkhHuR0at1BW26QxpSj-_ZKm_xT4hWw.woff2) format('woff2');
+  unicode-range: U+1F00-1FFF;
+}
+@font-face {
+  font-family: 'Roboto';
+  font-style: italic;
+  font-weight: 300;
+  src: local('Roboto Light Italic'), local('Roboto-LightItalic'), url(/font-roboto/7m8l7TlFO-S3VkhHuR0at4gp9Q8gbYrhqGlRav_IXfk.woff2) format('woff2');
+  unicode-range: U+0000-00FF, U+0131, U+0152-0153, U+02C6, U+02DA, U+02DC, U+2000-206F, U+2074, U+20AC, U+2212, U+2215;
+}
+@font-face {
+  font-family: 'Roboto';
+  font-style: italic;
+  font-weight: 300;
+  src: local('Roboto Light Italic'), local('Roboto-LightItalic'), url(/font-roboto/7m8l7TlFO-S3VkhHuR0at6E8kM4xWR1_1bYURRojRGc.woff2) format('woff2');
+  unicode-range: U+0100-024F, U+1E00-1EFF, U+20A0-20AB, U+20AD-20CF, U+2C60-2C7F, U+A720-A7FF;
+}
+@font-face {
+  font-family: 'Roboto';
+  font-style: italic;
+  font-weight: 300;
+  src: local('Roboto Light Italic'), local('Roboto-LightItalic'), url(/font-roboto/7m8l7TlFO-S3VkhHuR0at9DiNsR5a-9Oe_Ivpu8XWlY.woff2) format('woff2');
+  unicode-range: U+0102-0103, U+1EA0-1EF9, U+20AB;
+}
+@font-face {
+  font-family: 'Roboto';
+  font-style: normal;
+  font-weight: 500;
+  src: local('Roboto Medium'), local('Roboto-Medium'), url(/font-roboto/oHi30kwQWvpCWqAhzHcCSIX0hVgzZQUfRDuZrPvH3D8.woff2) format('woff2');
+  unicode-range: U+0400-045F, U+0490-0491, U+04B0-04B1, U+2116;
+}
+@font-face {
+  font-family: 'Roboto';
+  font-style: normal;
+  font-weight: 500;
+  src: local('Roboto Medium'), local('Roboto-Medium'), url(/font-roboto/ZLqKeelYbATG60EpZBSDy4X0hVgzZQUfRDuZrPvH3D8.woff2) format('woff2');
+  unicode-range: U+0460-052F, U+20B4, U+2DE0-2DFF, U+A640-A69F;
+}
+@font-face {
+  font-family: 'Roboto';
+  font-style: normal;
+  font-weight: 500;
+  src: local('Roboto Medium'), local('Roboto-Medium'), url(/font-roboto/mx9Uck6uB63VIKFYnEMXrYX0hVgzZQUfRDuZrPvH3D8.woff2) format('woff2');
+  unicode-range: U+0370-03FF;
+}
+@font-face {
+  font-family: 'Roboto';
+  font-style: normal;
+  font-weight: 500;
+  src: local('Roboto Medium'), local('Roboto-Medium'), url(/font-roboto/rGvHdJnr2l75qb0YND9NyIX0hVgzZQUfRDuZrPvH3D8.woff2) format('woff2');
+  unicode-range: U+1F00-1FFF;
+}
+@font-face {
+  font-family: 'Roboto';
+  font-style: normal;
+  font-weight: 500;
+  src: local('Roboto Medium'), local('Roboto-Medium'), url(/font-roboto/RxZJdnzeo3R5zSexge8UUZBw1xU1rKptJj_0jans920.woff2) format('woff2');
+  unicode-range: U+0000-00FF, U+0131, U+0152-0153, U+02C6, U+02DA, U+02DC, U+2000-206F, U+2074, U+20AC, U+2212, U+2215;
+}
+@font-face {
+  font-family: 'Roboto';
+  font-style: normal;
+  font-weight: 500;
+  src: local('Roboto Medium'), local('Roboto-Medium'), url(/font-roboto/oOeFwZNlrTefzLYmlVV1UIX0hVgzZQUfRDuZrPvH3D8.woff2) format('woff2');
+  unicode-range: U+0100-024F, U+1E00-1EFF, U+20A0-20AB, U+20AD-20CF, U+2C60-2C7F, U+A720-A7FF;
+}
+@font-face {
+  font-family: 'Roboto';
+  font-style: normal;
+  font-weight: 500;
+  src: local('Roboto Medium'), local('Roboto-Medium'), url(/font-roboto/mbmhprMH69Zi6eEPBYVFhYX0hVgzZQUfRDuZrPvH3D8.woff2) format('woff2');
+  unicode-range: U+0102-0103, U+1EA0-1EF9, U+20AB;
+}
+@font-face {
+  font-family: 'Roboto';
+  font-style: italic;
+  font-weight: 500;
+  src: local('Roboto Medium Italic'), local('Roboto-MediumItalic'), url(/font-roboto/OLffGBTaF0XFOW1gnuHF0V4sYYdJg5dU2qzJEVSuta0.woff2) format('woff2');
+  unicode-range: U+0400-045F, U+0490-0491, U+04B0-04B1, U+2116;
+}
+@font-face {
+  font-family: 'Roboto';
+  font-style: italic;
+  font-weight: 500;
+  src: local('Roboto Medium Italic'), local('Roboto-MediumItalic'), url(/font-roboto/OLffGBTaF0XFOW1gnuHF0fZraR2Tg8w2lzm7kLNL0-w.woff2) format('woff2');
+  unicode-range: U+0460-052F, U+20B4, U+2DE0-2DFF, U+A640-A69F;
+}
+@font-face {
+  font-family: 'Roboto';
+  font-style: italic;
+  font-weight: 500;
+  src: local('Roboto Medium Italic'), local('Roboto-MediumItalic'), url(/font-roboto/OLffGBTaF0XFOW1gnuHF0Qt_Rm691LTebKfY2ZkKSmI.woff2) format('woff2');
+  unicode-range: U+0370-03FF;
+}
+@font-face {
+  font-family: 'Roboto';
+  font-style: italic;
+  font-weight: 500;
+  src: local('Roboto Medium Italic'), local('Roboto-MediumItalic'), url(/font-roboto/OLffGBTaF0XFOW1gnuHF0VBW26QxpSj-_ZKm_xT4hWw.woff2) format('woff2');
+  unicode-range: U+1F00-1FFF;
+}
+@font-face {
+  font-family: 'Roboto';
+  font-style: italic;
+  font-weight: 500;
+  src: local('Roboto Medium Italic'), local('Roboto-MediumItalic'), url(/font-roboto/OLffGBTaF0XFOW1gnuHF0Ygp9Q8gbYrhqGlRav_IXfk.woff2) format('woff2');
+  unicode-range: U+0000-00FF, U+0131, U+0152-0153, U+02C6, U+02DA, U+02DC, U+2000-206F, U+2074, U+20AC, U+2212, U+2215;
+}
+@font-face {
+  font-family: 'Roboto';
+  font-style: italic;
+  font-weight: 500;
+  src: local('Roboto Medium Italic'), local('Roboto-MediumItalic'), url(/font-roboto/OLffGBTaF0XFOW1gnuHF0aE8kM4xWR1_1bYURRojRGc.woff2) format('woff2');
+  unicode-range: U+0100-024F, U+1E00-1EFF, U+20A0-20AB, U+20AD-20CF, U+2C60-2C7F, U+A720-A7FF;
+}
+@font-face {
+  font-family: 'Roboto';
+  font-style: italic;
+  font-weight: 500;
+  src: local('Roboto Medium Italic'), local('Roboto-MediumItalic'), url(/font-roboto/OLffGBTaF0XFOW1gnuHF0dDiNsR5a-9Oe_Ivpu8XWlY.woff2) format('woff2');
+  unicode-range: U+0102-0103, U+1EA0-1EF9, U+20AB;
+}
+@font-face {
+  font-family: 'Roboto Mono';
+  font-style: normal;
+  font-weight: 400;
+  src: local('Roboto Mono'), local('RobotoMono-Regular'), url(/font-roboto/hMqPNLsu_dywMa4C_DEpY14sYYdJg5dU2qzJEVSuta0.woff2) format('woff2');
+  unicode-range: U+0400-045F, U+0490-0491, U+04B0-04B1, U+2116;
+}
+@font-face {
+  font-family: 'Roboto Mono';
+  font-style: normal;
+  font-weight: 400;
+  src: local('Roboto Mono'), local('RobotoMono-Regular'), url(/font-roboto/hMqPNLsu_dywMa4C_DEpY_ZraR2Tg8w2lzm7kLNL0-w.woff2) format('woff2');
+  unicode-range: U+0460-052F, U+20B4, U+2DE0-2DFF, U+A640-A69F;
+}
+@font-face {
+  font-family: 'Roboto Mono';
+  font-style: normal;
+  font-weight: 400;
+  src: local('Roboto Mono'), local('RobotoMono-Regular'), url(/font-roboto/hMqPNLsu_dywMa4C_DEpYwt_Rm691LTebKfY2ZkKSmI.woff2) format('woff2');
+  unicode-range: U+0370-03FF;
+}
+@font-face {
+  font-family: 'Roboto Mono';
+  font-style: normal;
+  font-weight: 400;
+  src: local('Roboto Mono'), local('RobotoMono-Regular'), url(/font-roboto/hMqPNLsu_dywMa4C_DEpY1BW26QxpSj-_ZKm_xT4hWw.woff2) format('woff2');
+  unicode-range: U+1F00-1FFF;
+}
+@font-face {
+  font-family: 'Roboto Mono';
+  font-style: normal;
+  font-weight: 400;
+  src: local('Roboto Mono'), local('RobotoMono-Regular'), url(/font-roboto/hMqPNLsu_dywMa4C_DEpY4gp9Q8gbYrhqGlRav_IXfk.woff2) format('woff2');
+  unicode-range: U+0000-00FF, U+0131, U+0152-0153, U+02C6, U+02DA, U+02DC, U+2000-206F, U+2074, U+20AC, U+2212, U+2215;
+}
+@font-face {
+  font-family: 'Roboto Mono';
+  font-style: normal;
+  font-weight: 400;
+  src: local('Roboto Mono'), local('RobotoMono-Regular'), url(/font-roboto/hMqPNLsu_dywMa4C_DEpY6E8kM4xWR1_1bYURRojRGc.woff2) format('woff2');
+  unicode-range: U+0100-024F, U+1E00-1EFF, U+20A0-20AB, U+20AD-20CF, U+2C60-2C7F, U+A720-A7FF;
+}
+@font-face {
+  font-family: 'Roboto Mono';
+  font-style: normal;
+  font-weight: 400;
+  src: local('Roboto Mono'), local('RobotoMono-Regular'), url(/font-roboto/hMqPNLsu_dywMa4C_DEpY9DiNsR5a-9Oe_Ivpu8XWlY.woff2) format('woff2');
+  unicode-range: U+0102-0103, U+1EA0-1EF9, U+20AB;
+}
+@font-face {
+  font-family: 'Roboto Mono';
+  font-style: normal;
+  font-weight: 700;
+  src: local('Roboto Mono Bold'), local('RobotoMono-Bold'), url(/font-roboto/N4duVc9C58uwPiY8_59Fz1x-M1I1w5OMiqnVF8xBLhU.woff2) format('woff2');
+  unicode-range: U+0400-045F, U+0490-0491, U+04B0-04B1, U+2116;
+}
+@font-face {
+  font-family: 'Roboto Mono';
+  font-style: normal;
+  font-weight: 700;
+  src: local('Roboto Mono Bold'), local('RobotoMono-Bold'), url(/font-roboto/N4duVc9C58uwPiY8_59FzwXaAXup5mZlfK6xRLrhsco.woff2) format('woff2');
+  unicode-range: U+0460-052F, U+20B4, U+2DE0-2DFF, U+A640-A69F;
+}
+@font-face {
+  font-family: 'Roboto Mono';
+  font-style: normal;
+  font-weight: 700;
+  src: local('Roboto Mono Bold'), local('RobotoMono-Bold'), url(/font-roboto/N4duVc9C58uwPiY8_59Fzwn6Wqxo-xwxilDXPU8chVU.woff2) format('woff2');
+  unicode-range: U+0370-03FF;
+}
+@font-face {
+  font-family: 'Roboto Mono';
+  font-style: normal;
+  font-weight: 700;
+  src: local('Roboto Mono Bold'), local('RobotoMono-Bold'), url(/font-roboto/N4duVc9C58uwPiY8_59Fz1T7aJLK6nKpn36IMwTcMMc.woff2) format('woff2');
+  unicode-range: U+1F00-1FFF;
+}
+@font-face {
+  font-family: 'Roboto Mono';
+  font-style: normal;
+  font-weight: 700;
+  src: local('Roboto Mono Bold'), local('RobotoMono-Bold'), url(/font-roboto/N4duVc9C58uwPiY8_59Fz_79_ZuUxCigM2DespTnFaw.woff2) format('woff2');
+  unicode-range: U+0000-00FF, U+0131, U+0152-0153, U+02C6, U+02DA, U+02DC, U+2000-206F, U+2074, U+20AC, U+2212, U+2215;
+}
+@font-face {
+  font-family: 'Roboto Mono';
+  font-style: normal;
+  font-weight: 700;
+  src: local('Roboto Mono Bold'), local('RobotoMono-Bold'), url(/font-roboto/N4duVc9C58uwPiY8_59Fz4gd9OEPUCN3AdYW0e8tat4.woff2) format('woff2');
+  unicode-range: U+0100-024F, U+1E00-1EFF, U+20A0-20AB, U+20AD-20CF, U+2C60-2C7F, U+A720-A7FF;
+}
+@font-face {
+  font-family: 'Roboto Mono';
+  font-style: normal;
+  font-weight: 700;
+  src: local('Roboto Mono Bold'), local('RobotoMono-Bold'), url(/font-roboto/N4duVc9C58uwPiY8_59Fz8bIQSYZnWLaWC9QNCpTK_U.woff2) format('woff2');
+  unicode-range: U+0102-0103, U+1EA0-1EF9, U+20AB;
+}
+</style>
+
+
+
+
+<style>
+  html,
+  body {
+    height: 100%;
+    margin: 0;
+    width: 100%;
+    font-family: Roboto, sans-serif;
+  }
+</style>
+
+
+</head><body><vz-projector-dashboard></vz-projector-dashboard><script src="projector_binary.js"></script></body></html>