117 lines
4.3 KiB
Python
117 lines
4.3 KiB
Python
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# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""Text summaries and TensorFlow operations to create them."""
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from tensorboard.plugins.text import metadata
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from tensorboard.plugins.text import summary_v2
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# Export V2 versions.
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text = summary_v2.text
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text_pb = summary_v2.text_pb
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def op(name, data, display_name=None, description=None, collections=None):
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"""Create a legacy text summary op.
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Text data summarized via this plugin will be visible in the Text Dashboard
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in TensorBoard. The standard TensorBoard Text Dashboard will render markdown
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in the strings, and will automatically organize 1D and 2D tensors into tables.
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If a tensor with more than 2 dimensions is provided, a 2D subarray will be
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displayed along with a warning message. (Note that this behavior is not
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intrinsic to the text summary API, but rather to the default TensorBoard text
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plugin.)
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Args:
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name: A name for the generated node. Will also serve as a series name in
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TensorBoard.
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data: A string-type Tensor to summarize. The text must be encoded in UTF-8.
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display_name: Optional name for this summary in TensorBoard, as a
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constant `str`. Defaults to `name`.
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description: Optional long-form description for this summary, as a
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constant `str`. Markdown is supported. Defaults to empty.
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collections: Optional list of ops.GraphKeys. The collections to which to add
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the summary. Defaults to [Graph Keys.SUMMARIES].
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Returns:
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A TensorSummary op that is configured so that TensorBoard will recognize
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that it contains textual data. The TensorSummary is a scalar `Tensor` of
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type `string` which contains `Summary` protobufs.
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Raises:
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ValueError: If tensor has the wrong type.
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"""
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# TODO(nickfelt): remove on-demand imports once dep situation is fixed.
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import tensorflow.compat.v1 as tf
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if display_name is None:
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display_name = name
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summary_metadata = metadata.create_summary_metadata(
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display_name=display_name, description=description
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)
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with tf.name_scope(name):
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with tf.control_dependencies([tf.assert_type(data, tf.string)]):
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return tf.summary.tensor_summary(
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name="text_summary",
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tensor=data,
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collections=collections,
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summary_metadata=summary_metadata,
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)
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def pb(name, data, display_name=None, description=None):
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"""Create a legacy text summary protobuf.
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Arguments:
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name: A name for the generated node. Will also serve as a series name in
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TensorBoard.
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data: A Python bytestring (of type bytes), or Unicode string. Or a numpy
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data array of those types.
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display_name: Optional name for this summary in TensorBoard, as a
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`str`. Defaults to `name`.
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description: Optional long-form description for this summary, as a
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`str`. Markdown is supported. Defaults to empty.
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Raises:
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ValueError: If the type of the data is unsupported.
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Returns:
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A `tf.Summary` protobuf object.
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"""
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# TODO(nickfelt): remove on-demand imports once dep situation is fixed.
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import tensorflow.compat.v1 as tf
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try:
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tensor = tf.make_tensor_proto(data, dtype=tf.string)
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except TypeError as e:
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raise ValueError(e)
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if display_name is None:
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display_name = name
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summary_metadata = metadata.create_summary_metadata(
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display_name=display_name, description=description
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)
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tf_summary_metadata = tf.SummaryMetadata.FromString(
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summary_metadata.SerializeToString()
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)
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summary = tf.Summary()
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summary.value.add(
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tag="%s/text_summary" % name,
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metadata=tf_summary_metadata,
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tensor=tensor,
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)
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return summary
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