# 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. # ============================================================================== """Scalar summaries and TensorFlow operations to create them. A scalar summary stores a single floating-point value, as a rank-0 tensor. """ from __future__ import absolute_import from __future__ import division from __future__ import print_function import numpy as np from tensorboard.plugins.scalar import metadata from tensorboard.plugins.scalar import summary_v2 # Export V2 versions. scalar = summary_v2.scalar scalar_pb = summary_v2.scalar_pb def op(name, data, display_name=None, description=None, collections=None): """Create a legacy scalar summary op. Arguments: name: A unique name for the generated summary node. data: A real numeric rank-0 `Tensor`. Must have `dtype` castable to `float32`. display_name: Optional name for this summary in TensorBoard, as a constant `str`. Defaults to `name`. description: Optional long-form description for this summary, as a constant `str`. Markdown is supported. Defaults to empty. collections: Optional list of graph collections keys. The new summary op is added to these collections. Defaults to `[Graph Keys.SUMMARIES]`. Returns: A TensorFlow summary op. """ # TODO(nickfelt): remove on-demand imports once dep situation is fixed. import tensorflow.compat.v1 as tf if display_name is None: display_name = name summary_metadata = metadata.create_summary_metadata( display_name=display_name, description=description ) with tf.name_scope(name): with tf.control_dependencies([tf.assert_scalar(data)]): return tf.summary.tensor_summary( name="scalar_summary", tensor=tf.cast(data, tf.float32), collections=collections, summary_metadata=summary_metadata, ) def pb(name, data, display_name=None, description=None): """Create a legacy scalar summary protobuf. Arguments: name: A unique name for the generated summary, including any desired name scopes. data: A rank-0 `np.array` or array-like form (so raw `int`s and `float`s are fine, too). display_name: Optional name for this summary in TensorBoard, as a `str`. Defaults to `name`. description: Optional long-form description for this summary, as a `str`. Markdown is supported. Defaults to empty. Returns: A `tf.Summary` protobuf object. """ # TODO(nickfelt): remove on-demand imports once dep situation is fixed. import tensorflow.compat.v1 as tf data = np.array(data) if data.shape != (): raise ValueError( "Expected scalar shape for data, saw shape: %s." % data.shape ) if data.dtype.kind not in ("b", "i", "u", "f"): # bool, int, uint, float raise ValueError("Cast %s to float is not supported" % data.dtype.name) tensor = tf.make_tensor_proto(data.astype(np.float32)) if display_name is None: display_name = name summary_metadata = metadata.create_summary_metadata( display_name=display_name, description=description ) tf_summary_metadata = tf.SummaryMetadata.FromString( summary_metadata.SerializeToString() ) summary = tf.Summary() summary.value.add( tag="%s/scalar_summary" % name, metadata=tf_summary_metadata, tensor=tensor, ) return summary