126 lines
4.8 KiB
Python
126 lines
4.8 KiB
Python
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# Copyright 2018 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, V2 versions."""
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import numpy as np
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from tensorboard.compat import tf2 as tf
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from tensorboard.compat.proto import summary_pb2
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from tensorboard.plugins.text import metadata
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from tensorboard.util import tensor_util
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def text(name, data, step=None, description=None):
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r"""Write a text summary.
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See also `tf.summary.scalar`, `tf.summary.SummaryWriter`, `tf.summary.image`.
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Writes text Tensor values for later visualization and analysis in TensorBoard.
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Writes go to the current default summary writer. Like `tf.summary.scalar`
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points, text points are each associated with a `step` and a `name`.
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All the points with the same `name` constitute a time series of text values.
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For Example:
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```python
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test_summary_writer = tf.summary.create_file_writer('test/logdir')
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with test_summary_writer.as_default():
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tf.summary.text('first_text', 'hello world!', step=0)
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tf.summary.text('first_text', 'nice to meet you!', step=1)
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```
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The text summary can also contain Markdown, and TensorBoard will render the text
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as such.
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```python
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with test_summary_writer.as_default():
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text_data = '''
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| *hello* | *there* |
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|---------|---------|
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| this | is |
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| a | table |
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'''
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text_data = '\n'.join(l.strip() for l in text_data.splitlines())
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tf.summary.text('markdown_text', text_data, step=0)
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```
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Since text is Tensor valued, each text point may be a Tensor of string values.
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rank-1 and rank-2 Tensors are rendered as tables in TensorBoard. For higher ranked
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Tensors, you'll see just a 2D slice of the data. To avoid this, reshape the Tensor
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to at most rank-2 prior to passing it to this function.
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Demo notebook at
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["Displaying text data in TensorBoard"](https://www.tensorflow.org/tensorboard/text_summaries).
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Arguments:
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name: A name for this summary. The summary tag used for TensorBoard will
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be this name prefixed by any active name scopes.
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data: A UTF-8 string Tensor value.
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step: Explicit `int64`-castable monotonic step value for this summary. If
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omitted, this defaults to `tf.summary.experimental.get_step()`, which must
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not be None.
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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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Returns:
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True on success, or false if no summary was emitted because no default
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summary writer was available.
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Raises:
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ValueError: if a default writer exists, but no step was provided and
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`tf.summary.experimental.get_step()` is None.
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"""
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summary_metadata = metadata.create_summary_metadata(
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display_name=None, description=description
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)
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# TODO(https://github.com/tensorflow/tensorboard/issues/2109): remove fallback
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summary_scope = (
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getattr(tf.summary.experimental, "summary_scope", None)
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or tf.summary.summary_scope
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)
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with summary_scope(name, "text_summary", values=[data, step]) as (tag, _):
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tf.debugging.assert_type(data, tf.string)
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return tf.summary.write(
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tag=tag, tensor=data, step=step, metadata=summary_metadata
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)
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def text_pb(tag, data, description=None):
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"""Create a text tf.Summary protobuf.
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Arguments:
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tag: String tag for the summary.
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data: A Python bytestring (of type bytes), a Unicode string, or a numpy data
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array of those types.
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description: Optional long-form description for this summary, as a `str`.
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Markdown is supported. Defaults to empty.
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Raises:
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TypeError: 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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try:
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tensor = tensor_util.make_tensor_proto(data, dtype=np.object_)
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except TypeError as e:
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raise TypeError("tensor must be of type string", e)
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summary_metadata = metadata.create_summary_metadata(
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display_name=None, description=description
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)
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summary = summary_pb2.Summary()
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summary.value.add(tag=tag, metadata=summary_metadata, tensor=tensor)
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return summary
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