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