113 lines
4.5 KiB
Python
113 lines
4.5 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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"""Utilities to migrate legacy protos to their modern equivalents."""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import numpy as np
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import tensorflow as tf
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from tensorboard.plugins.audio import metadata as audio_metadata
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from tensorboard.plugins.image import metadata as image_metadata
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from tensorboard.plugins.histogram import metadata as histogram_metadata
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from tensorboard.plugins.scalar import metadata as scalar_metadata
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def migrate_value(value):
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"""Convert `value` to a new-style value, if necessary and possible.
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An "old-style" value is a value that uses any `value` field other than
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the `tensor` field. A "new-style" value is a value that uses the
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`tensor` field. TensorBoard continues to support old-style values on
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disk; this method converts them to new-style values so that further
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code need only deal with one data format.
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Arguments:
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value: A `tf.Summary.Value` object. This argument is not modified.
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Returns:
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If the `value` is an old-style value for which there is a new-style
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equivalent, the result is the new-style value. Otherwise---if the
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value is already new-style or does not yet have a new-style
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equivalent---the value will be returned unchanged.
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:type value: tf.Summary.Value
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:rtype: tf.Summary.Value
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"""
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handler = {
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'histo': _migrate_histogram_value,
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'image': _migrate_image_value,
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'audio': _migrate_audio_value,
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'simple_value': _migrate_scalar_value,
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}.get(value.WhichOneof('value'))
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return handler(value) if handler else value
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def _migrate_histogram_value(value):
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histogram_value = value.histo
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bucket_lefts = [histogram_value.min] + histogram_value.bucket_limit[:-1]
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bucket_rights = histogram_value.bucket_limit[:-1] + [histogram_value.max]
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bucket_counts = histogram_value.bucket
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buckets = np.array([bucket_lefts, bucket_rights, bucket_counts]).transpose()
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tensor_proto = tf.make_tensor_proto(buckets)
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summary_metadata = histogram_metadata.create_summary_metadata(
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display_name=value.metadata.display_name or value.tag,
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description=value.metadata.summary_description)
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return tf.Summary.Value(tag=value.tag,
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metadata=summary_metadata,
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tensor=tensor_proto)
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def _migrate_image_value(value):
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image_value = value.image
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data = [tf.compat.as_bytes(str(image_value.width)),
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tf.compat.as_bytes(str(image_value.height)),
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tf.compat.as_bytes(image_value.encoded_image_string)]
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tensor_proto = tf.make_tensor_proto(data)
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summary_metadata = image_metadata.create_summary_metadata(
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display_name=value.metadata.display_name or value.tag,
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description=value.metadata.summary_description)
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return tf.Summary.Value(tag=value.tag,
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metadata=summary_metadata,
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tensor=tensor_proto)
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def _migrate_audio_value(value):
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audio_value = value.audio
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data = [[audio_value.encoded_audio_string, b'']] # empty label
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tensor_proto = tf.make_tensor_proto(data)
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summary_metadata = audio_metadata.create_summary_metadata(
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display_name=value.metadata.display_name or value.tag,
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description=value.metadata.summary_description,
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encoding=audio_metadata.Encoding.Value('WAV'))
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return tf.Summary.Value(tag=value.tag,
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metadata=summary_metadata,
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tensor=tensor_proto)
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def _migrate_scalar_value(value):
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scalar_value = value.simple_value
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tensor_proto = tf.make_tensor_proto(scalar_value)
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summary_metadata = scalar_metadata.create_summary_metadata(
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display_name=value.metadata.display_name or value.tag,
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description=value.metadata.summary_description)
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return tf.Summary.Value(tag=value.tag,
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metadata=summary_metadata,
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tensor=tensor_proto)
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