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# Copyright 2021 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.
# ==============================================================================
"""Generalized output options for writing tensor-formatted summary data."""
from tensorboard.compat.proto import event_pb2
from tensorboard.compat.proto import summary_pb2
from tensorboard.summary.writer import event_file_writer
from tensorboard.util import tensor_util
import abc
class Output(abc.ABC):
"""Interface for emitting tensor-formatted summary data.
Implementations of this interface can be passed to Writer to customize
how summary data is actually persisted (e.g. to disk, to memory, over
the network, etc.).
TODO(#4581): This API should be considered EXPERIMENTAL and subject to
backwards-incompatible changes without notice.
"""
@abc.abstractmethod
def emit_scalar(
self,
*,
plugin_name,
tag,
data,
step,
wall_time,
tag_metadata=None,
description=None,
):
"""Emits one scalar data point to this Output.
Args:
plugin_name: string name to uniquely identify the type of time series
(historically associated with a TensorBoard plugin).
tag: string tag used to uniquely identify this time series.
data: `np.float32` scalar value for this data point.
step: `np.int64` scalar step value for this data point.
wall_time: `float` seconds since the Unix epoch, representing the
real-world timestamp for this data point.
tag_metadata: optional bytes containing metadata for this entire time
series. This should be constant for a given tag; only the first
value encountered will be used.
description: optional string description for this entire time series.
This should be constant for a given tag; only the first value
encountered will be used.
"""
pass
@abc.abstractmethod
def flush(self):
"""Flushes any data that has been buffered."""
pass
@abc.abstractmethod
def close(self):
"""Closes the Output and also flushes any buffered data."""
pass
class DirectoryOutput(Output):
"""Outputs summary data by writing event files to a log directory.
TODO(#4581): This API should be considered EXPERIMENTAL and subject to
backwards-incompatible changes without notice.
"""
def __init__(self, path):
"""Creates a `DirectoryOutput` for the given path."""
self._ev_writer = event_file_writer.EventFileWriter(path)
def emit_scalar(
self,
*,
plugin_name,
tag,
data,
step,
wall_time,
tag_metadata=None,
description=None,
):
"""See `Output`."""
# TODO(#4581): cache summary metadata to emit only once.
summary_metadata = summary_pb2.SummaryMetadata(
plugin_data=summary_pb2.SummaryMetadata.PluginData(
plugin_name=plugin_name, content=tag_metadata
),
summary_description=description,
data_class=summary_pb2.DataClass.DATA_CLASS_SCALAR,
)
tensor_proto = tensor_util.make_tensor_proto(data)
event = event_pb2.Event(wall_time=wall_time, step=step)
event.summary.value.add(
tag=tag, tensor=tensor_proto, metadata=summary_metadata
)
self._ev_writer.add_event(event)
def flush(self):
"""See `Output`."""
self._ev_writer.flush()
def close(self):
"""See `Output`."""
# No need to call flush first since EventFileWriter already
# will do this for us when we call close().
self._ev_writer.close()
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