226 lines
8.4 KiB
Python
226 lines
8.4 KiB
Python
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# Copyright 2020 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 summaries/events to generic data form.
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For legacy summaries, this populates the `SummaryMetadata.data_class`
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field and makes any necessary transformations to the tensor value. For
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`graph_def` events, this creates a new summary event.
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This should be effected after the `data_compat` transformation.
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"""
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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.custom_scalar import (
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metadata as custom_scalars_metadata,
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)
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from tensorboard.plugins.graph import metadata as graphs_metadata
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from tensorboard.plugins.histogram import metadata as histograms_metadata
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from tensorboard.plugins.hparams import metadata as hparams_metadata
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from tensorboard.plugins.image import metadata as images_metadata
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from tensorboard.plugins.mesh import metadata as mesh_metadata
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from tensorboard.plugins.pr_curve import metadata as pr_curves_metadata
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from tensorboard.plugins.scalar import metadata as scalars_metadata
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from tensorboard.plugins.text import metadata as text_metadata
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from tensorboard.util import tensor_util
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def migrate_event(event, initial_metadata):
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"""Migrate an event to a sequence of events.
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Args:
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event: An `event_pb2.Event`. The caller transfers ownership of the
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event to this method; the event may be mutated, and may or may
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not appear in the returned sequence.
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initial_metadata: Map from tag name (string) to `SummaryMetadata`
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proto for the initial occurrence of the given tag within the
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enclosing run. While loading a given run, the caller should
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always pass the same dictionary here, initially `{}`; this
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function will mutate it and reuse it for future calls.
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Returns:
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A sequence of `event_pb2.Event`s to use instead of `event`.
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"""
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what = event.WhichOneof("what")
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if what == "graph_def":
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return _migrate_graph_event(event)
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if what == "tagged_run_metadata":
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return _migrate_tagged_run_metadata_event(event)
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if what == "summary":
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return _migrate_summary_event(event, initial_metadata)
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return (event,)
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def _migrate_graph_event(old_event):
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result = event_pb2.Event()
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result.wall_time = old_event.wall_time
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result.step = old_event.step
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value = result.summary.value.add(tag=graphs_metadata.RUN_GRAPH_NAME)
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graph_bytes = old_event.graph_def
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value.tensor.CopyFrom(tensor_util.make_tensor_proto([graph_bytes]))
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value.metadata.plugin_data.plugin_name = graphs_metadata.PLUGIN_NAME
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# `value.metadata.plugin_data.content` left empty
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value.metadata.data_class = summary_pb2.DATA_CLASS_BLOB_SEQUENCE
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# As long as the graphs plugin still reads the old format, keep both
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# the old event and the new event to maintain compatibility.
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return (old_event, result)
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def _migrate_tagged_run_metadata_event(old_event):
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result = event_pb2.Event()
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result.wall_time = old_event.wall_time
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result.step = old_event.step
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trm = old_event.tagged_run_metadata
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value = result.summary.value.add(tag=trm.tag)
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value.tensor.CopyFrom(tensor_util.make_tensor_proto([trm.run_metadata]))
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value.metadata.plugin_data.plugin_name = (
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graphs_metadata.PLUGIN_NAME_TAGGED_RUN_METADATA
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)
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# `value.metadata.plugin_data.content` left empty
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value.metadata.data_class = summary_pb2.DATA_CLASS_BLOB_SEQUENCE
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return (result,)
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def _migrate_summary_event(event, initial_metadata):
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values = event.summary.value
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new_values = [
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new for old in values for new in _migrate_value(old, initial_metadata)
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]
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# Optimization: Don't create a new event if there were no shallow
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# changes (there may still have been in-place changes).
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if len(values) == len(new_values) and all(
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x is y for (x, y) in zip(values, new_values)
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):
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return (event,)
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del event.summary.value[:]
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event.summary.value.extend(new_values)
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return (event,)
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def _migrate_value(value, initial_metadata):
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"""Convert an old value to a stream of new values. May mutate."""
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metadata = initial_metadata.get(value.tag)
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initial = False
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if metadata is None:
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initial = True
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# Retain a copy of the initial metadata, so that even after we
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# update its data class we know whether to also transform later
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# events in this time series.
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metadata = summary_pb2.SummaryMetadata()
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metadata.CopyFrom(value.metadata)
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initial_metadata[value.tag] = metadata
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if metadata.data_class != summary_pb2.DATA_CLASS_UNKNOWN:
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return (value,)
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plugin_name = metadata.plugin_data.plugin_name
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if plugin_name == histograms_metadata.PLUGIN_NAME:
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return _migrate_histogram_value(value)
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if plugin_name == images_metadata.PLUGIN_NAME:
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return _migrate_image_value(value)
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if plugin_name == audio_metadata.PLUGIN_NAME:
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return _migrate_audio_value(value)
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if plugin_name == scalars_metadata.PLUGIN_NAME:
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return _migrate_scalar_value(value)
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if plugin_name == text_metadata.PLUGIN_NAME:
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return _migrate_text_value(value)
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if plugin_name == hparams_metadata.PLUGIN_NAME:
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return _migrate_hparams_value(value)
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if plugin_name == pr_curves_metadata.PLUGIN_NAME:
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return _migrate_pr_curve_value(value)
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if plugin_name == mesh_metadata.PLUGIN_NAME:
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return _migrate_mesh_value(value)
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if plugin_name == custom_scalars_metadata.PLUGIN_NAME:
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return _migrate_custom_scalars_value(value)
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if plugin_name in [
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graphs_metadata.PLUGIN_NAME_RUN_METADATA,
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graphs_metadata.PLUGIN_NAME_RUN_METADATA_WITH_GRAPH,
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graphs_metadata.PLUGIN_NAME_KERAS_MODEL,
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]:
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return _migrate_graph_sub_plugin_value(value)
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return (value,)
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def _migrate_scalar_value(value):
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if value.HasField("metadata"):
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value.metadata.data_class = summary_pb2.DATA_CLASS_SCALAR
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return (value,)
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def _migrate_histogram_value(value):
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if value.HasField("metadata"):
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value.metadata.data_class = summary_pb2.DATA_CLASS_TENSOR
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return (value,)
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def _migrate_image_value(value):
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if value.HasField("metadata"):
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value.metadata.data_class = summary_pb2.DATA_CLASS_BLOB_SEQUENCE
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return (value,)
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def _migrate_text_value(value):
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if value.HasField("metadata"):
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value.metadata.data_class = summary_pb2.DATA_CLASS_TENSOR
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return (value,)
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def _migrate_audio_value(value):
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if value.HasField("metadata"):
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value.metadata.data_class = summary_pb2.DATA_CLASS_BLOB_SEQUENCE
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tensor = value.tensor
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# Project out just the first axis: actual audio clips.
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stride = 1
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while len(tensor.tensor_shape.dim) > 1:
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stride *= tensor.tensor_shape.dim.pop().size
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if stride != 1:
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tensor.string_val[:] = tensor.string_val[::stride]
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return (value,)
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def _migrate_hparams_value(value):
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if value.HasField("metadata"):
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value.metadata.data_class = summary_pb2.DATA_CLASS_TENSOR
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if not value.HasField("tensor"):
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value.tensor.CopyFrom(hparams_metadata.NULL_TENSOR)
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return (value,)
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def _migrate_pr_curve_value(value):
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if value.HasField("metadata"):
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value.metadata.data_class = summary_pb2.DATA_CLASS_TENSOR
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return (value,)
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def _migrate_mesh_value(value):
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if value.HasField("metadata"):
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value.metadata.data_class = summary_pb2.DATA_CLASS_TENSOR
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return (value,)
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def _migrate_custom_scalars_value(value):
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if value.HasField("metadata"):
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value.metadata.data_class = summary_pb2.DATA_CLASS_TENSOR
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return (value,)
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def _migrate_graph_sub_plugin_value(value):
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if value.HasField("metadata"):
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value.metadata.data_class = summary_pb2.DATA_CLASS_BLOB_SEQUENCE
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shape = value.tensor.tensor_shape.dim
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if not shape:
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shape.add(size=1)
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return (value,)
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