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rl/Lib/site-packages/tensorboard/plugins/image/images_plugin.py

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+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""The TensorBoard Images plugin."""
+
+
+import imghdr
+import urllib.parse
+
+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.image import metadata
+
+
+_IMGHDR_TO_MIMETYPE = {
+    "bmp": "image/bmp",
+    "gif": "image/gif",
+    "jpeg": "image/jpeg",
+    "png": "image/png",
+    "svg": "image/svg+xml",
+}
+
+_DEFAULT_IMAGE_MIMETYPE = "application/octet-stream"
+_DEFAULT_DOWNSAMPLING = 10  # images per time series
+
+
+# Extend imghdr.tests to include svg.
+def detect_svg(data, f):
+    del f  # Unused.
+    # Assume XML documents attached to image tag to be SVG.
+    if data.startswith(b"<?xml ") or data.startswith(b"<svg "):
+        return "svg"
+
+
+imghdr.tests.append(detect_svg)
+
+
+class ImagesPlugin(base_plugin.TBPlugin):
+    """Images Plugin for TensorBoard."""
+
+    plugin_name = metadata.PLUGIN_NAME
+
+    def __init__(self, context):
+        """Instantiates ImagesPlugin 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="image",
+            latest_known_version=0,
+        )
+
+    def get_plugin_apps(self):
+        return {
+            "/images": self._serve_image_metadata,
+            "/individualImage": self._serve_individual_image,
+            "/tags": self._serve_tags,
+        }
+
+    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-image-dashboard")
+
+    def _index_impl(self, ctx, experiment):
+        mapping = self._data_provider.list_blob_sequences(
+            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,
+                    "samples": metadatum.max_length - 2,  # width, height
+                }
+        return result
+
+    @wrappers.Request.application
+    def _serve_image_metadata(self, request):
+        """Given a tag and list of runs, serve a list of metadata for images.
+
+        Note that the images themselves are not sent; instead, we respond with URLs
+        to the images. The frontend should treat these URLs as opaque and should not
+        try to parse information about them or generate them itself, as the format
+        may change.
+
+        Args:
+          request: A werkzeug.wrappers.Request object.
+
+        Returns:
+          A werkzeug.Response application.
+        """
+        ctx = plugin_util.context(request.environ)
+        experiment = plugin_util.experiment_id(request.environ)
+        tag = request.args.get("tag")
+        run = request.args.get("run")
+        sample = int(request.args.get("sample", 0))
+        try:
+            response = self._image_response_for_run(
+                ctx, experiment, run, tag, sample
+            )
+        except KeyError:
+            return http_util.Respond(
+                request, "Invalid run or tag", "text/plain", code=400
+            )
+        return http_util.Respond(request, response, "application/json")
+
+    def _image_response_for_run(self, ctx, experiment, run, tag, sample):
+        """Builds a JSON-serializable object with information about images.
+
+        Args:
+          run: The name of the run.
+          tag: The name of the tag the images all belong to.
+          sample: The zero-indexed sample of the image for which to retrieve
+            information. For instance, setting `sample` to `2` will fetch
+            information about only the third image of each batch. Steps with
+            fewer than three images will be omitted from the results.
+
+        Returns:
+          A list of dictionaries containing the wall time, step, and URL
+          for each image.
+
+        Raises:
+          KeyError, NotFoundError: If no image data exists for the given
+            parameters.
+        """
+        all_images = self._data_provider.read_blob_sequences(
+            ctx,
+            experiment_id=experiment,
+            plugin_name=metadata.PLUGIN_NAME,
+            downsample=self._downsample_to,
+            run_tag_filter=provider.RunTagFilter(runs=[run], tags=[tag]),
+        )
+        images = all_images.get(run, {}).get(tag, None)
+        if images is None:
+            raise errors.NotFoundError(
+                "No image data for run=%r, tag=%r" % (run, tag)
+            )
+        return [
+            {
+                "wall_time": datum.wall_time,
+                "step": datum.step,
+                "query": self._data_provider_query(datum.values[sample + 2]),
+            }
+            for datum in images
+            if len(datum.values) - 2 > sample
+        ]
+
+    def _filter_by_sample(self, tensor_events, sample):
+        return [
+            tensor_event
+            for tensor_event in tensor_events
+            if (
+                len(tensor_event.tensor_proto.string_val) - 2  # width, height
+                > sample
+            )
+        ]
+
+    def _query_for_individual_image(self, run, tag, sample, index):
+        """Builds a URL for accessing the specified image.
+
+        This should be kept in sync with _serve_image_metadata. Note that the URL is
+        *not* guaranteed to always return the same image, since images may be
+        unloaded from the reservoir as new images come in.
+
+        Args:
+          run: The name of the run.
+          tag: The tag.
+          sample: The relevant sample index, zero-indexed. See documentation
+            on `_image_response_for_run` for more details.
+          index: The index of the image. Negative values are OK.
+
+        Returns:
+          A string representation of a URL that will load the index-th sampled image
+          in the given run with the given tag.
+        """
+        query_string = urllib.parse.urlencode(
+            {
+                "run": run,
+                "tag": tag,
+                "sample": sample,
+                "index": index,
+            }
+        )
+        return query_string
+
+    def _data_provider_query(self, blob_reference):
+        return urllib.parse.urlencode({"blob_key": blob_reference.blob_key})
+
+    def _get_generic_data_individual_image(self, ctx, blob_key):
+        """Returns the actual image bytes for a given image.
+
+        Args:
+          blob_key: As returned by a previous `read_blob_sequences` call.
+
+        Returns:
+          A bytestring of the raw image bytes.
+        """
+        return self._data_provider.read_blob(ctx, blob_key=blob_key)
+
+    @wrappers.Request.application
+    def _serve_individual_image(self, request):
+        """Serves an individual image."""
+        try:
+            ctx = plugin_util.context(request.environ)
+            blob_key = request.args["blob_key"]
+            data = self._get_generic_data_individual_image(ctx, blob_key)
+        except (KeyError, IndexError):
+            return http_util.Respond(
+                request,
+                "Invalid run, tag, index, or sample",
+                "text/plain",
+                code=400,
+            )
+        image_type = imghdr.what(None, data)
+        content_type = _IMGHDR_TO_MIMETYPE.get(
+            image_type, _DEFAULT_IMAGE_MIMETYPE
+        )
+        return http_util.Respond(request, data, content_type)
+
+    @wrappers.Request.application
+    def _serve_tags(self, request):
+        ctx = plugin_util.context(request.environ)
+        experiment = plugin_util.experiment_id(request.environ)
+        index = self._index_impl(ctx, experiment)
+        return http_util.Respond(request, index, "application/json")

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

@@ -0,0 +1,66 @@
+# 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 images plugin."""
+
+
+from tensorboard.compat.proto import summary_pb2
+from tensorboard.plugins.image import plugin_data_pb2
+
+PLUGIN_NAME = "images"
+
+# The most recent value for the `version` field of the `ImagePluginData`
+# proto.
+PROTO_VERSION = 0
+
+
+def create_summary_metadata(
+    display_name, description, *, converted_to_tensor=None
+):
+    """Create a `summary_pb2.SummaryMetadata` proto for image plugin data.
+
+    Returns:
+      A `summary_pb2.SummaryMetadata` protobuf object.
+    """
+    content = plugin_data_pb2.ImagePluginData(
+        version=PROTO_VERSION,
+        converted_to_tensor=converted_to_tensor,
+    )
+    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 image plugin.
+
+    Returns:
+      An `ImagePluginData` protobuf object.
+    """
+    if not isinstance(content, bytes):
+        raise TypeError("Content type must be bytes")
+    result = plugin_data_pb2.ImagePluginData.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/image/plugin_data_pb2.py

@@ -0,0 +1,34 @@
+# -*- coding: utf-8 -*-
+# Generated by the protocol buffer compiler.  DO NOT EDIT!
+# source: tensorboard/plugins/image/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/image/plugin_data.proto\x12\x0btensorboard\"?\n\x0fImagePluginData\x12\x0f\n\x07version\x18\x01 \x01(\x05\x12\x1b\n\x13\x63onverted_to_tensor\x18\x02 \x01(\x08\x62\x06proto3')
+
+
+
+_IMAGEPLUGINDATA = DESCRIPTOR.message_types_by_name['ImagePluginData']
+ImagePluginData = _reflection.GeneratedProtocolMessageType('ImagePluginData', (_message.Message,), {
+  'DESCRIPTOR' : _IMAGEPLUGINDATA,
+  '__module__' : 'tensorboard.plugins.image.plugin_data_pb2'
+  # @@protoc_insertion_point(class_scope:tensorboard.ImagePluginData)
+  })
+_sym_db.RegisterMessage(ImagePluginData)
+
+if _descriptor._USE_C_DESCRIPTORS == False:
+
+  DESCRIPTOR._options = None
+  _IMAGEPLUGINDATA._serialized_start=60
+  _IMAGEPLUGINDATA._serialized_end=123
+# @@protoc_insertion_point(module_scope)

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

@@ -0,0 +1,161 @@
+# 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.
+# ==============================================================================
+"""Image summaries and TensorFlow operations to create them.
+
+An image summary stores the width, height, and PNG-encoded data for zero
+or more images in a rank-1 string array: `[w, h, png0, png1, ...]`.
+
+NOTE: This module is in beta, and its API is subject to change, but the
+data that it stores to disk will be supported forever.
+"""
+
+
+import numpy as np
+
+from tensorboard.plugins.image import metadata
+from tensorboard.plugins.image import summary_v2
+from tensorboard.util import encoder
+
+
+# Export V2 versions.
+image = summary_v2.image
+
+
+def op(
+    name,
+    images,
+    max_outputs=3,
+    display_name=None,
+    description=None,
+    collections=None,
+):
+    """Create a legacy image summary op for use in a TensorFlow graph.
+
+    Arguments:
+      name: A unique name for the generated summary node.
+      images: A `Tensor` representing pixel data with shape `[k, h, w, c]`,
+        where `k` is the number of images, `h` and `w` are the height and
+        width of the images, and `c` is the number of channels, which
+        should be 1, 3, or 4. Any of the dimensions may be statically
+        unknown (i.e., `None`).
+      max_outputs: Optional `int` or rank-0 integer `Tensor`. At most this
+        many images will be emitted at each step. When more than
+        `max_outputs` many images are provided, the first `max_outputs` many
+        images will be used and the rest silently discarded.
+      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), tf.control_dependencies(
+        [
+            tf.assert_rank(images, 4),
+            tf.assert_type(images, tf.uint8),
+            tf.assert_non_negative(max_outputs),
+        ]
+    ):
+        limited_images = images[:max_outputs]
+        encoded_images = tf.map_fn(
+            tf.image.encode_png,
+            limited_images,
+            dtype=tf.string,
+            name="encode_each_image",
+        )
+        image_shape = tf.shape(input=images)
+        dimensions = tf.stack(
+            [
+                tf.as_string(image_shape[2], name="width"),
+                tf.as_string(image_shape[1], name="height"),
+            ],
+            name="dimensions",
+        )
+        tensor = tf.concat([dimensions, encoded_images], axis=0)
+        return tf.summary.tensor_summary(
+            name="image_summary",
+            tensor=tensor,
+            collections=collections,
+            summary_metadata=summary_metadata,
+        )
+
+
+def pb(name, images, max_outputs=3, display_name=None, description=None):
+    """Create a legacy image summary protobuf.
+
+    This behaves as if you were to create an `op` with the same arguments
+    (wrapped with constant tensors where appropriate) and then execute
+    that summary op in a TensorFlow session.
+
+    Arguments:
+      name: A unique name for the generated summary, including any desired
+        name scopes.
+      images: An `np.array` representing pixel data with shape
+        `[k, h, w, c]`, where `k` is the number of images, `w` and `h` are
+        the width and height of the images, and `c` is the number of
+        channels, which should be 1, 3, or 4.
+      max_outputs: Optional `int`. At most this many images will be
+        emitted. If more than this many images are provided, the first
+        `max_outputs` many images will be used and the rest silently
+        discarded.
+      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
+
+    images = np.array(images).astype(np.uint8)
+    if images.ndim != 4:
+        raise ValueError("Shape %r must have rank 4" % (images.shape,))
+
+    limited_images = images[:max_outputs]
+    encoded_images = [encoder.encode_png(image) for image in limited_images]
+    (width, height) = (images.shape[2], images.shape[1])
+    content = [str(width), str(height)] + encoded_images
+    tensor = tf.make_tensor_proto(content, dtype=tf.string)
+
+    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/image_summary" % name,
+        metadata=tf_summary_metadata,
+        tensor=tensor,
+    )
+    return summary

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

@@ -0,0 +1,131 @@
+# 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.
+# ==============================================================================
+"""Image summaries and TensorFlow operations to create them, V2 versions.
+
+An image summary stores the width, height, and PNG-encoded data for zero
+or more images in a rank-1 string array: `[w, h, png0, png1, ...]`.
+"""
+
+
+from tensorboard.compat import tf2 as tf
+from tensorboard.plugins.image import metadata
+from tensorboard.util import lazy_tensor_creator
+
+
+def image(name, data, step=None, max_outputs=3, description=None):
+    """Write an image summary.
+
+    See also `tf.summary.scalar`, `tf.summary.SummaryWriter`.
+
+    Writes a collection of images to the current default summary writer. Data
+    appears in TensorBoard's 'Images' dashboard. Like `tf.summary.scalar` points,
+    each collection of images is associated with a `step` and a `name`.  All the
+    image collections with the same `name` constitute a time series of image
+    collections.
+
+    This example writes 2 random grayscale images:
+
+    ```python
+    w = tf.summary.create_file_writer('test/logs')
+    with w.as_default():
+      image1 = tf.random.uniform(shape=[8, 8, 1])
+      image2 = tf.random.uniform(shape=[8, 8, 1])
+      tf.summary.image("grayscale_noise", [image1, image2], step=0)
+    ```
+
+    To avoid clipping, data should be converted to one of the following:
+
+    - floating point values in the range [0,1], or
+    - uint8 values in the range [0,255]
+
+    ```python
+    # Convert the original dtype=int32 `Tensor` into `dtype=float64`.
+    rgb_image_float = tf.constant([
+      [[1000, 0, 0], [0, 500, 1000]],
+    ]) / 1000
+    tf.summary.image("picture", [rgb_image_float], step=0)
+
+    # Convert original dtype=uint8 `Tensor` into proper range.
+    rgb_image_uint8 = tf.constant([
+      [[1, 1, 0], [0, 0, 1]],
+    ], dtype=tf.uint8) * 255
+    tf.summary.image("picture", [rgb_image_uint8], step=1)
+    ```
+
+    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 `Tensor` representing pixel data with shape `[k, h, w, c]`,
+        where `k` is the number of images, `h` and `w` are the height and
+        width of the images, and `c` is the number of channels, which
+        should be 1, 2, 3, or 4 (grayscale, grayscale with alpha, RGB, RGBA).
+        Any of the dimensions may be statically unknown (i.e., `None`).
+        Floating point data will be clipped to the range [0,1]. Other data types
+        will be clipped into an allowed range for safe casting to uint8, using
+        `tf.image.convert_image_dtype`.
+      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.
+      max_outputs: Optional `int` or rank-0 integer `Tensor`. At most this
+        many images will be emitted at each step. When more than
+        `max_outputs` many images are provided, the first `max_outputs` many
+        images will be used and the rest silently discarded.
+      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 emitted 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, "image_summary", values=[data, max_outputs, step]
+    ) as (tag, _):
+        # Defer image encoding preprocessing by passing it as a callable to write(),
+        # wrapped in a LazyTensorCreator for backwards compatibility, so that we
+        # only do this work when summaries are actually written.
+        @lazy_tensor_creator.LazyTensorCreator
+        def lazy_tensor():
+            tf.debugging.assert_rank(data, 4)
+            tf.debugging.assert_non_negative(max_outputs)
+            images = tf.image.convert_image_dtype(data, tf.uint8, saturate=True)
+            limited_images = images[:max_outputs]
+            encoded_images = tf.image.encode_png(limited_images)
+            image_shape = tf.shape(input=images)
+            dimensions = tf.stack(
+                [
+                    tf.as_string(image_shape[2], name="width"),
+                    tf.as_string(image_shape[1], name="height"),
+                ],
+                name="dimensions",
+            )
+            return tf.concat([dimensions, encoded_images], axis=0)
+
+        # To ensure that image encoding logic is only executed when summaries
+        # are written, we pass callable to `tensor` parameter.
+        return tf.summary.write(
+            tag=tag, tensor=lazy_tensor, step=step, metadata=summary_metadata
+        )