3RNN/Lib/site-packages/tensorboard/plugins/text/summary_v2.py
2024-05-26 19:49:15 +02:00

126 lines
4.8 KiB
Python

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