161 lines
6.3 KiB
Python
161 lines
6.3 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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"""Utilities to migrate legacy protos to their modern equivalents."""
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import numpy as np
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from tensorboard.compat.proto import event_pb2
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from tensorboard.compat.proto import summary_pb2
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from tensorboard.plugins.audio import metadata as audio_metadata
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from tensorboard.plugins.histogram import metadata as histogram_metadata
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from tensorboard.plugins.image import metadata as image_metadata
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from tensorboard.plugins.scalar import metadata as scalar_metadata
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from tensorboard.util import tensor_util
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def migrate_event(event):
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if not event.HasField("summary"):
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return event
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old_values = event.summary.value
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new_values = [migrate_value(value) for value in old_values]
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# Optimization: Don't create a new event if there were no changes.
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if len(old_values) == len(new_values) and all(
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x is y for (x, y) in zip(old_values, new_values)
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):
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return event
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result = event_pb2.Event()
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result.CopyFrom(event)
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del result.summary.value[:]
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result.summary.value.extend(new_values)
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return result
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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 `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: Summary.Value
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:rtype: 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 make_summary(tag, metadata, data):
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tensor_proto = tensor_util.make_tensor_proto(data)
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return summary_pb2.Summary.Value(
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tag=tag, metadata=metadata, tensor=tensor_proto
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)
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def _migrate_histogram_value(value):
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"""Convert `old-style` histogram value to `new-style`.
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The "old-style" format can have outermost bucket limits of -DBL_MAX and
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DBL_MAX, which are problematic for visualization. We replace those here
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with the actual min and max values seen in the input data, but then in
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order to avoid introducing "backwards" buckets (where left edge > right
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edge), we first must drop all empty buckets on the left and right ends.
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"""
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histogram_value = value.histo
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bucket_counts = histogram_value.bucket
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# Find the indices of the leftmost and rightmost non-empty buckets.
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n = len(bucket_counts)
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start = next((i for i in range(n) if bucket_counts[i] > 0), n)
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end = next((i for i in reversed(range(n)) if bucket_counts[i] > 0), -1)
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if start > end:
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# If all input buckets were empty, treat it as a zero-bucket
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# new-style histogram.
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buckets = np.zeros([0, 3], dtype=np.float32)
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else:
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# Discard empty buckets on both ends, and keep only the "inner"
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# edges from the remaining buckets. Note that bucket indices range
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# from `start` to `end` inclusive, but bucket_limit indices are
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# exclusive of `end` - this is because bucket_limit[i] is the
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# right-hand edge for bucket[i].
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bucket_counts = bucket_counts[start : end + 1]
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inner_edges = histogram_value.bucket_limit[start:end]
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# Use min as the left-hand limit for the first non-empty bucket.
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bucket_lefts = [histogram_value.min] + inner_edges
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# Use max as the right-hand limit for the last non-empty bucket.
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bucket_rights = inner_edges + [histogram_value.max]
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buckets = np.array(
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[bucket_lefts, bucket_rights, bucket_counts], dtype=np.float32
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).transpose()
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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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)
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return make_summary(value.tag, summary_metadata, buckets)
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def _migrate_image_value(value):
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image_value = value.image
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data = [
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str(image_value.width).encode("ascii"),
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str(image_value.height).encode("ascii"),
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image_value.encoded_image_string,
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]
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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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converted_to_tensor=True,
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)
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return make_summary(value.tag, summary_metadata, data)
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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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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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converted_to_tensor=True,
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)
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return make_summary(value.tag, summary_metadata, data)
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def _migrate_scalar_value(value):
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scalar_value = value.simple_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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)
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return make_summary(value.tag, summary_metadata, scalar_value)
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