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# Copyright 2017 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.
# ==============================================================================
"""Utilities to migrate legacy protos to their modern equivalents."""
import numpy as np
from tensorboard.compat.proto import event_pb2
from tensorboard.compat.proto import summary_pb2
from tensorboard.plugins.audio import metadata as audio_metadata
from tensorboard.plugins.histogram import metadata as histogram_metadata
from tensorboard.plugins.image import metadata as image_metadata
from tensorboard.plugins.scalar import metadata as scalar_metadata
from tensorboard.util import tensor_util
def migrate_event(event):
if not event.HasField("summary"):
return event
old_values = event.summary.value
new_values = [migrate_value(value) for value in old_values]
# Optimization: Don't create a new event if there were no changes.
if len(old_values) == len(new_values) and all(
x is y for (x, y) in zip(old_values, new_values)
):
return event
result = event_pb2.Event()
result.CopyFrom(event)
del result.summary.value[:]
result.summary.value.extend(new_values)
return result
def migrate_value(value):
"""Convert `value` to a new-style value, if necessary and possible.
An "old-style" value is a value that uses any `value` field other than
the `tensor` field. A "new-style" value is a value that uses the
`tensor` field. TensorBoard continues to support old-style values on
disk; this method converts them to new-style values so that further
code need only deal with one data format.
Arguments:
value: A `Summary.Value` object. This argument is not modified.
Returns:
If the `value` is an old-style value for which there is a new-style
equivalent, the result is the new-style value. Otherwise---if the
value is already new-style or does not yet have a new-style
equivalent---the value will be returned unchanged.
:type value: Summary.Value
:rtype: Summary.Value
"""
handler = {
"histo": _migrate_histogram_value,
"image": _migrate_image_value,
"audio": _migrate_audio_value,
"simple_value": _migrate_scalar_value,
}.get(value.WhichOneof("value"))
return handler(value) if handler else value
def make_summary(tag, metadata, data):
tensor_proto = tensor_util.make_tensor_proto(data)
return summary_pb2.Summary.Value(
tag=tag, metadata=metadata, tensor=tensor_proto
)
def _migrate_histogram_value(value):
"""Convert `old-style` histogram value to `new-style`.
The "old-style" format can have outermost bucket limits of -DBL_MAX and
DBL_MAX, which are problematic for visualization. We replace those here
with the actual min and max values seen in the input data, but then in
order to avoid introducing "backwards" buckets (where left edge > right
edge), we first must drop all empty buckets on the left and right ends.
"""
histogram_value = value.histo
bucket_counts = histogram_value.bucket
# Find the indices of the leftmost and rightmost non-empty buckets.
n = len(bucket_counts)
start = next((i for i in range(n) if bucket_counts[i] > 0), n)
end = next((i for i in reversed(range(n)) if bucket_counts[i] > 0), -1)
if start > end:
# If all input buckets were empty, treat it as a zero-bucket
# new-style histogram.
buckets = np.zeros([0, 3], dtype=np.float32)
else:
# Discard empty buckets on both ends, and keep only the "inner"
# edges from the remaining buckets. Note that bucket indices range
# from `start` to `end` inclusive, but bucket_limit indices are
# exclusive of `end` - this is because bucket_limit[i] is the
# right-hand edge for bucket[i].
bucket_counts = bucket_counts[start : end + 1]
inner_edges = histogram_value.bucket_limit[start:end]
# Use min as the left-hand limit for the first non-empty bucket.
bucket_lefts = [histogram_value.min] + inner_edges
# Use max as the right-hand limit for the last non-empty bucket.
bucket_rights = inner_edges + [histogram_value.max]
buckets = np.array(
[bucket_lefts, bucket_rights, bucket_counts], dtype=np.float32
).transpose()
summary_metadata = histogram_metadata.create_summary_metadata(
display_name=value.metadata.display_name or value.tag,
description=value.metadata.summary_description,
)
return make_summary(value.tag, summary_metadata, buckets)
def _migrate_image_value(value):
image_value = value.image
data = [
str(image_value.width).encode("ascii"),
str(image_value.height).encode("ascii"),
image_value.encoded_image_string,
]
summary_metadata = image_metadata.create_summary_metadata(
display_name=value.metadata.display_name or value.tag,
description=value.metadata.summary_description,
converted_to_tensor=True,
)
return make_summary(value.tag, summary_metadata, data)
def _migrate_audio_value(value):
audio_value = value.audio
data = [[audio_value.encoded_audio_string, b""]] # empty label
summary_metadata = audio_metadata.create_summary_metadata(
display_name=value.metadata.display_name or value.tag,
description=value.metadata.summary_description,
encoding=audio_metadata.Encoding.Value("WAV"),
converted_to_tensor=True,
)
return make_summary(value.tag, summary_metadata, data)
def _migrate_scalar_value(value):
scalar_value = value.simple_value
summary_metadata = scalar_metadata.create_summary_metadata(
display_name=value.metadata.display_name or value.tag,
description=value.metadata.summary_description,
)
return make_summary(value.tag, summary_metadata, scalar_value)
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