# 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. # ============================================================================== """Operations to emit summaries.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import abc import collections import functools import os import re import threading import six from tensorflow.core.framework import graph_pb2 from tensorflow.core.framework import summary_pb2 from tensorflow.core.protobuf import config_pb2 from tensorflow.python.eager import context from tensorflow.python.eager import profiler as _profiler from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes from tensorflow.python.framework import ops from tensorflow.python.framework import smart_cond from tensorflow.python.framework import tensor_util from tensorflow.python.ops import array_ops from tensorflow.python.ops import control_flow_ops from tensorflow.python.ops import gen_summary_ops from tensorflow.python.ops import math_ops from tensorflow.python.ops import resource_variable_ops from tensorflow.python.ops import summary_op_util from tensorflow.python.platform import tf_logging as logging from tensorflow.python.training import training_util from tensorflow.python.util import deprecation from tensorflow.python.util import tf_contextlib from tensorflow.python.util.tf_export import tf_export # Name for graph collection of summary writer init ops, which is only exposed # as a legacy API for tf.contrib.summary in TF 1.x. _SUMMARY_WRITER_INIT_COLLECTION_NAME = "_SUMMARY_WRITER_V2" _EXPERIMENT_NAME_PATTERNS = re.compile(r"^[^\x00-\x1F<>]{0,256}$") _RUN_NAME_PATTERNS = re.compile(r"^[^\x00-\x1F<>]{0,512}$") _USER_NAME_PATTERNS = re.compile(r"^[a-z]([-a-z0-9]{0,29}[a-z0-9])?$", re.I) class _SummaryState(threading.local): def __init__(self): super(_SummaryState, self).__init__() self.is_recording = None # TODO(slebedev): why a separate flag for DS and is it on by default? self.is_recording_distribution_strategy = True self.writer = None self.step = None _summary_state = _SummaryState() def _should_record_summaries_internal(default_state): """Returns boolean Tensor if summaries should/shouldn't be recorded. Now the summary condition is decided by logical "and" of below conditions: First, summary writer must be set. Given this constraint is met, ctx.summary_recording and ctx.summary_recording_distribution_strategy. The former one is usually set by user, and the latter one is controlled by DistributionStrategy (tf.distribute.ReplicaContext). Args: default_state: can be True or False. The default summary behavior when summary writer is set and the user does not specify ctx.summary_recording and ctx.summary_recording_distribution_strategy is True. """ if _summary_state.writer is None: return constant_op.constant(False) if not callable(_summary_state.is_recording): static_cond = tensor_util.constant_value(_summary_state.is_recording) if static_cond is not None and not static_cond: return constant_op.constant(False) resolve = lambda x: x() if callable(x) else x cond_distributed = resolve(_summary_state.is_recording_distribution_strategy) cond = resolve(_summary_state.is_recording) if cond is None: cond = default_state return math_ops.logical_and(cond_distributed, cond) def _should_record_summaries_v2(): """Returns boolean Tensor which is true if summaries should be recorded. If no recording status has been set, this defaults to True, unlike the public should_record_summaries(). """ return _should_record_summaries_internal(default_state=True) @tf_export("summary.should_record_summaries", v1=[]) def should_record_summaries(): """Returns boolean Tensor which is true if summaries should be recorded.""" return _should_record_summaries_internal(default_state=False) @tf_export("summary.record_if", v1=[]) @tf_contextlib.contextmanager def record_if(condition): """Sets summary recording on or off per the provided boolean value. The provided value can be a python boolean, a scalar boolean Tensor, or or a callable providing such a value; if a callable is passed it will be invoked on-demand to determine whether summary writing will occur. Note that when calling record_if() in an eager mode context, if you intend to provide a varying condition like `step % 100 == 0`, you must wrap this in a callable to avoid immediate eager evaluation of the condition. In particular, using a callable is the only way to have your condition evaluated as part of the traced body of an @tf.function that is invoked from within the `record_if()` context. Args: condition: can be True, False, a bool Tensor, or a callable providing such. Yields: Returns a context manager that sets this value on enter and restores the previous value on exit. """ old = _summary_state.is_recording try: _summary_state.is_recording = condition yield finally: _summary_state.is_recording = old # TODO(apassos) consider how to handle local step here. def record_summaries_every_n_global_steps(n, global_step=None): """Sets the should_record_summaries Tensor to true if global_step % n == 0.""" if global_step is None: global_step = training_util.get_or_create_global_step() with ops.device("cpu:0"): should = lambda: math_ops.equal(global_step % n, 0) if not context.executing_eagerly(): should = should() return record_if(should) def always_record_summaries(): """Sets the should_record_summaries Tensor to always true.""" return record_if(True) def never_record_summaries(): """Sets the should_record_summaries Tensor to always false.""" return record_if(False) @tf_export("summary.experimental.get_step", v1=[]) def get_step(): """Returns the default summary step for the current thread. Returns: The step set by `tf.summary.experimental.set_step()` if one has been set, otherwise None. """ return _summary_state.step @tf_export("summary.experimental.set_step", v1=[]) def set_step(step): """Sets the default summary step for the current thread. For convenience, this function sets a default value for the `step` parameter used in summary-writing functions elsewhere in the API so that it need not be explicitly passed in every such invocation. The value can be a constant or a variable, and can be retrieved via `tf.summary.experimental.get_step()`. Note: when using this with @tf.functions, the step value will be captured at the time the function is traced, so changes to the step outside the function will not be reflected inside the function unless using a `tf.Variable` step. Args: step: An `int64`-castable default step value, or None to unset. """ _summary_state.step = step @tf_export("summary.SummaryWriter", v1=[]) @six.add_metaclass(abc.ABCMeta) class SummaryWriter(object): """Interface representing a stateful summary writer object.""" @abc.abstractmethod def set_as_default(self, step=None): """Enables this summary writer for the current thread. For convenience, if `step` is not None, this function also sets a default value for the `step` parameter used in summary-writing functions elsewhere in the API so that it need not be explicitly passed in every such invocation. The value can be a constant or a variable. Note: when setting `step` in a @tf.function, the step value will be captured at the time the function is traced, so changes to the step outside the function will not be reflected inside the function unless using a `tf.Variable` step. Args: step: An `int64`-castable default step value, or `None`. When not `None`, the current step is modified to the given value. When `None`, the current step is not modified. """ raise NotImplementedError() @abc.abstractmethod @tf_contextlib.contextmanager def as_default(self, step=None): """Returns a context manager that enables summary writing. For convenience, if `step` is not None, this function also sets a default value for the `step` parameter used in summary-writing functions elsewhere in the API so that it need not be explicitly passed in every such invocation. The value can be a constant or a variable. Note: when setting `step` in a @tf.function, the step value will be captured at the time the function is traced, so changes to the step outside the function will not be reflected inside the function unless using a `tf.Variable` step. For example, `step` can be used as: ```python with writer_a.as_default(step=10): tf.summary.scalar(tag, value) # Logged to writer_a with step 10 with writer_b.as_default(step=20): tf.summary.scalar(tag, value) # Logged to writer_b with step 20 tf.summary.scalar(tag, value) # Logged to writer_a with step 10 ``` Args: step: An `int64`-castable default step value, or `None`. When not `None`, the current step is captured, replaced by a given one, and the original one is restored when the context manager exits. When `None`, the current step is not modified (and not restored when the context manager exits). """ raise NotImplementedError() def init(self): """Initializes the summary writer.""" raise NotImplementedError() def flush(self): """Flushes any buffered data.""" raise NotImplementedError() def close(self): """Flushes and closes the summary writer.""" raise NotImplementedError() class ResourceSummaryWriter(SummaryWriter): """Implementation of SummaryWriter using a SummaryWriterInterface resource.""" def __init__(self, shared_name, init_op_fn, name=None, v2=False, metadata=None): self._resource = gen_summary_ops.summary_writer( shared_name=shared_name, name=name) # TODO(nickfelt): cache other constructed ops in graph mode self._init_op_fn = init_op_fn self._init_op = init_op_fn(self._resource) self._v2 = v2 self._metadata = {} if metadata is None else metadata self._closed = False if context.executing_eagerly(): self._resource_deleter = resource_variable_ops.EagerResourceDeleter( handle=self._resource, handle_device="cpu:0") else: ops.add_to_collection(_SUMMARY_WRITER_INIT_COLLECTION_NAME, self._init_op) def set_as_default(self, step=None): """Enables this summary writer for the current thread. For convenience, if `step` is not None, this function also sets a default value for the `step` parameter used in summary-writing functions elsewhere in the API so that it need not be explicitly passed in every such invocation. The value can be a constant or a variable. Note: when setting `step` in a @tf.function, the step value will be captured at the time the function is traced, so changes to the step outside the function will not be reflected inside the function unless using a `tf.Variable` step. Args: step: An `int64`-castable default step value, or `None`. When not `None`, the current step is modified to the given value. When `None`, the current step is not modified. """ if self._v2 and context.executing_eagerly() and self._closed: raise RuntimeError("SummaryWriter is already closed") _summary_state.writer = self if step is not None: _summary_state.step = step @tf_contextlib.contextmanager def as_default(self, step=None): """Returns a context manager that enables summary writing. For convenience, if `step` is not None, this function also sets a default value for the `step` parameter used in summary-writing functions elsewhere in the API so that it need not be explicitly passed in every such invocation. The value can be a constant or a variable. Note: when setting `step` in a @tf.function, the step value will be captured at the time the function is traced, so changes to the step outside the function will not be reflected inside the function unless using a `tf.Variable` step. For example, `step` can be used as: ```python with writer_a.as_default(step=10): tf.summary.scalar(tag, value) # Logged to writer_a with step 10 with writer_b.as_default(step=20): tf.summary.scalar(tag, value) # Logged to writer_b with step 20 tf.summary.scalar(tag, value) # Logged to writer_a with step 10 ``` Args: step: An `int64`-castable default step value, or `None`. When not `None`, the current step is captured, replaced by a given one, and the original one is restored when the context manager exits. When `None`, the current step is not modified (and not restored when the context manager exits). """ if self._v2 and context.executing_eagerly() and self._closed: raise RuntimeError("SummaryWriter is already closed") old = _summary_state.writer if step is not None: old_step = _summary_state.step try: _summary_state.writer = self if step is not None: _summary_state.step = step yield self # Flushes the summary writer in eager mode or in graph functions, but # not in legacy graph mode (you're on your own there). self.flush() finally: _summary_state.writer = old if step is not None: _summary_state.step = old_step def init(self): """Initializes the summary writer.""" if self._v2: if context.executing_eagerly() and self._closed: raise RuntimeError("SummaryWriter is already closed") return self._init_op # Legacy behavior allows re-initializing the resource. return self._init_op_fn(self._resource) def flush(self): """Flushes any buffered data.""" if self._v2 and context.executing_eagerly() and self._closed: return return _flush_fn(writer=self) def close(self): """Flushes and closes the summary writer.""" if self._v2 and context.executing_eagerly() and self._closed: return try: with ops.control_dependencies([self.flush()]): with ops.device("cpu:0"): return gen_summary_ops.close_summary_writer(self._resource) finally: if self._v2 and context.executing_eagerly(): self._closed = True class NoopSummaryWriter(SummaryWriter): """A summary writer that does nothing, for create_noop_writer().""" def set_as_default(self, step=None): pass @tf_contextlib.contextmanager def as_default(self, step=None): yield def init(self): pass def flush(self): pass def close(self): pass @tf_export(v1=["summary.initialize"]) def initialize( graph=None, # pylint: disable=redefined-outer-name session=None): """Initializes summary writing for graph execution mode. This operation is a no-op when executing eagerly. This helper method provides a higher-level alternative to using `tf.contrib.summary.summary_writer_initializer_op` and `tf.contrib.summary.graph`. Most users will also want to call `tf.compat.v1.train.create_global_step` which can happen before or after this function is called. Args: graph: A `tf.Graph` or `tf.compat.v1.GraphDef` to output to the writer. This function will not write the default graph by default. When writing to an event log file, the associated step will be zero. session: So this method can call `tf.Session.run`. This defaults to `tf.compat.v1.get_default_session`. Raises: RuntimeError: If the current thread has no default `tf.contrib.summary.SummaryWriter`. ValueError: If session wasn't passed and no default session. """ if context.executing_eagerly(): return if _summary_state.writer is None: raise RuntimeError("No default tf.contrib.summary.SummaryWriter found") if session is None: session = ops.get_default_session() if session is None: raise ValueError("session must be passed if no default session exists") session.run(summary_writer_initializer_op()) if graph is not None: data = _serialize_graph(graph) x = array_ops.placeholder(dtypes.string) session.run(graph_v1(x, 0), feed_dict={x: data}) @tf_export("summary.create_file_writer", v1=[]) def create_file_writer_v2(logdir, max_queue=None, flush_millis=None, filename_suffix=None, name=None): """Creates a summary file writer for the given log directory. Args: logdir: a string specifying the directory in which to write an event file. max_queue: the largest number of summaries to keep in a queue; will flush once the queue gets bigger than this. Defaults to 10. flush_millis: the largest interval between flushes. Defaults to 120,000. filename_suffix: optional suffix for the event file name. Defaults to `.v2`. name: a name for the op that creates the writer. Returns: A SummaryWriter object. """ if logdir is None: raise ValueError("logdir cannot be None") inside_function = ops.inside_function() with ops.name_scope(name, "create_file_writer") as scope, ops.device("cpu:0"): # Run init inside an init_scope() to hoist it out of tf.functions. with ops.init_scope(): if context.executing_eagerly(): _check_create_file_writer_args( inside_function, logdir=logdir, max_queue=max_queue, flush_millis=flush_millis, filename_suffix=filename_suffix) logdir = ops.convert_to_tensor(logdir, dtype=dtypes.string) if max_queue is None: max_queue = constant_op.constant(10) if flush_millis is None: flush_millis = constant_op.constant(2 * 60 * 1000) if filename_suffix is None: filename_suffix = constant_op.constant(".v2") # Prepend the PID and a process-local UID to the filename suffix to avoid # filename collisions within the machine (the filename already contains # the hostname to avoid cross-machine collisions). unique_prefix = constant_op.constant(".%s.%s" % (os.getpid(), ops.uid())) filename_suffix = unique_prefix + filename_suffix # Use a unique shared_name to prevent resource sharing. if context.executing_eagerly(): shared_name = context.shared_name() else: shared_name = ops.name_from_scope_name(scope) # pylint: disable=protected-access return ResourceSummaryWriter( shared_name=shared_name, init_op_fn=functools.partial( gen_summary_ops.create_summary_file_writer, logdir=logdir, max_queue=max_queue, flush_millis=flush_millis, filename_suffix=filename_suffix), name=name, v2=True, metadata={"logdir": logdir}) def create_file_writer(logdir, max_queue=None, flush_millis=None, filename_suffix=None, name=None): """Creates a summary file writer in the current context under the given name. Args: logdir: a string, or None. If a string, creates a summary file writer which writes to the directory named by the string. If None, returns a mock object which acts like a summary writer but does nothing, useful to use as a context manager. max_queue: the largest number of summaries to keep in a queue; will flush once the queue gets bigger than this. Defaults to 10. flush_millis: the largest interval between flushes. Defaults to 120,000. filename_suffix: optional suffix for the event file name. Defaults to `.v2`. name: Shared name for this SummaryWriter resource stored to default Graph. Defaults to the provided logdir prefixed with `logdir:`. Note: if a summary writer resource with this shared name already exists, the returned SummaryWriter wraps that resource and the other arguments have no effect. Returns: Either a summary writer or an empty object which can be used as a summary writer. """ if logdir is None: return NoopSummaryWriter() logdir = str(logdir) with ops.device("cpu:0"): if max_queue is None: max_queue = constant_op.constant(10) if flush_millis is None: flush_millis = constant_op.constant(2 * 60 * 1000) if filename_suffix is None: filename_suffix = constant_op.constant(".v2") if name is None: name = "logdir:" + logdir return ResourceSummaryWriter( shared_name=name, init_op_fn=functools.partial( gen_summary_ops.create_summary_file_writer, logdir=logdir, max_queue=max_queue, flush_millis=flush_millis, filename_suffix=filename_suffix)) @tf_export("summary.create_noop_writer", v1=[]) def create_noop_writer(): """Returns a summary writer that does nothing. This is useful as a placeholder in code that expects a context manager. """ return NoopSummaryWriter() def _cleanse_string(name, pattern, value): if isinstance(value, six.string_types) and pattern.search(value) is None: raise ValueError("%s (%s) must match %s" % (name, value, pattern.pattern)) return ops.convert_to_tensor(value, dtypes.string) def _nothing(): """Convenient else branch for when summaries do not record.""" return constant_op.constant(False) @tf_export(v1=["summary.all_v2_summary_ops"]) def all_v2_summary_ops(): """Returns all V2-style summary ops defined in the current default graph. This includes ops from TF 2.0 tf.summary and TF 1.x tf.contrib.summary (except for `tf.contrib.summary.graph` and `tf.contrib.summary.import_event`), but does *not* include TF 1.x tf.summary ops. Returns: List of summary ops, or None if called under eager execution. """ if context.executing_eagerly(): return None return ops.get_collection(ops.GraphKeys._SUMMARY_COLLECTION) # pylint: disable=protected-access def summary_writer_initializer_op(): """Graph-mode only. Returns the list of ops to create all summary writers. Returns: The initializer ops. Raises: RuntimeError: If in Eager mode. """ if context.executing_eagerly(): raise RuntimeError( "tf.contrib.summary.summary_writer_initializer_op is only " "supported in graph mode.") return ops.get_collection(_SUMMARY_WRITER_INIT_COLLECTION_NAME) _INVALID_SCOPE_CHARACTERS = re.compile(r"[^-_/.A-Za-z0-9]") @tf_export("summary.experimental.summary_scope", v1=[]) @tf_contextlib.contextmanager def summary_scope(name, default_name="summary", values=None): """Experimental context manager for use when defining a custom summary op. This behaves similarly to `tf.name_scope`, except that it returns a generated summary tag in addition to the scope name. The tag is structurally similar to the scope name - derived from the user-provided name, prefixed with enclosing name scopes if any - but we relax the constraint that it be uniquified, as well as the character set limitation (so the user-provided name can contain characters not legal for scope names; in the scope name these are removed). This makes the summary tag more predictable and consistent for the user. For example, to define a new summary op called `my_op`: ```python def my_op(name, my_value, step): with tf.summary.summary_scope(name, "MyOp", [my_value]) as (tag, scope): my_value = tf.convert_to_tensor(my_value) return tf.summary.write(tag, my_value, step=step) ``` Args: name: string name for the summary. default_name: Optional; if provided, used as default name of the summary. values: Optional; passed as `values` parameter to name_scope. Yields: A tuple `(tag, scope)` as described above. """ name = name or default_name current_scope = ops.get_name_scope() tag = current_scope + "/" + name if current_scope else name # Strip illegal characters from the scope name, and if that leaves nothing, # use None instead so we pick up the default name. name = _INVALID_SCOPE_CHARACTERS.sub("", name) or None with ops.name_scope(name, default_name, values, skip_on_eager=False) as scope: yield tag, scope @tf_export("summary.write", v1=[]) def write(tag, tensor, step=None, metadata=None, name=None): """Writes a generic summary to the default SummaryWriter if one exists. This exists primarily to support the definition of type-specific summary ops like scalar() and image(), and is not intended for direct use unless defining a new type-specific summary op. Args: tag: string tag used to identify the summary (e.g. in TensorBoard), usually generated with `tf.summary.summary_scope` tensor: the Tensor holding the summary data to write or a callable that returns this Tensor. If a callable is passed, it will only be called when a default SummaryWriter exists and the recording condition specified by `record_if()` is met. step: Explicit `int64`-castable monotonic step value for this summary. If omitted, this defaults to `tf.summary.experimental.get_step()`, which must not be None. metadata: Optional SummaryMetadata, as a proto or serialized bytes name: Optional string name for this op. Returns: True on success, or false if no summary was written because no default summary writer was available. Raises: ValueError: if a default writer exists, but no step was provided and `tf.summary.experimental.get_step()` is None. """ with ops.name_scope(name, "write_summary") as scope: if _summary_state.writer is None: return constant_op.constant(False) if step is None: step = get_step() if metadata is None: serialized_metadata = b"" elif hasattr(metadata, "SerializeToString"): serialized_metadata = metadata.SerializeToString() else: serialized_metadata = metadata def record(): """Record the actual summary and return True.""" if step is None: raise ValueError("No step set via 'step' argument or " "tf.summary.experimental.set_step()") # Note the identity to move the tensor to the CPU. with ops.device("cpu:0"): summary_tensor = tensor() if callable(tensor) else array_ops.identity( tensor) write_summary_op = gen_summary_ops.write_summary( _summary_state.writer._resource, # pylint: disable=protected-access step, summary_tensor, tag, serialized_metadata, name=scope) with ops.control_dependencies([write_summary_op]): return constant_op.constant(True) op = smart_cond.smart_cond( _should_record_summaries_v2(), record, _nothing, name="summary_cond") if not context.executing_eagerly(): ops.add_to_collection(ops.GraphKeys._SUMMARY_COLLECTION, op) # pylint: disable=protected-access return op @tf_export("summary.experimental.write_raw_pb", v1=[]) def write_raw_pb(tensor, step=None, name=None): """Writes a summary using raw `tf.compat.v1.Summary` protocol buffers. Experimental: this exists to support the usage of V1-style manual summary writing (via the construction of a `tf.compat.v1.Summary` protocol buffer) with the V2 summary writing API. Args: tensor: the string Tensor holding one or more serialized `Summary` protobufs step: Explicit `int64`-castable monotonic step value for this summary. If omitted, this defaults to `tf.summary.experimental.get_step()`, which must not be None. name: Optional string name for this op. Returns: True on success, or false if no summary was written because no default summary writer was available. Raises: ValueError: if a default writer exists, but no step was provided and `tf.summary.experimental.get_step()` is None. """ with ops.name_scope(name, "write_raw_pb") as scope: if _summary_state.writer is None: return constant_op.constant(False) if step is None: step = get_step() if step is None: raise ValueError("No step set via 'step' argument or " "tf.summary.experimental.set_step()") def record(): """Record the actual summary and return True.""" # Note the identity to move the tensor to the CPU. with ops.device("cpu:0"): raw_summary_op = gen_summary_ops.write_raw_proto_summary( _summary_state.writer._resource, # pylint: disable=protected-access step, array_ops.identity(tensor), name=scope) with ops.control_dependencies([raw_summary_op]): return constant_op.constant(True) with ops.device("cpu:0"): op = smart_cond.smart_cond( _should_record_summaries_v2(), record, _nothing, name="summary_cond") if not context.executing_eagerly(): ops.add_to_collection(ops.GraphKeys._SUMMARY_COLLECTION, op) # pylint: disable=protected-access return op def summary_writer_function(name, tensor, function, family=None): """Helper function to write summaries. Args: name: name of the summary tensor: main tensor to form the summary function: function taking a tag and a scope which writes the summary family: optional, the summary's family Returns: The result of writing the summary. """ name_scope = ops.get_name_scope() if name_scope: # Add a slash to allow reentering the name scope. name_scope += "/" def record(): with ops.name_scope(name_scope), summary_op_util.summary_scope( name, family, values=[tensor]) as (tag, scope): with ops.control_dependencies([function(tag, scope)]): return constant_op.constant(True) if _summary_state.writer is None: return control_flow_ops.no_op() with ops.device("cpu:0"): op = smart_cond.smart_cond( should_record_summaries(), record, _nothing, name="") if not context.executing_eagerly(): ops.add_to_collection(ops.GraphKeys._SUMMARY_COLLECTION, op) # pylint: disable=protected-access return op def generic(name, tensor, metadata=None, family=None, step=None): """Writes a tensor summary if possible.""" def function(tag, scope): if metadata is None: serialized_metadata = constant_op.constant("") elif hasattr(metadata, "SerializeToString"): serialized_metadata = constant_op.constant(metadata.SerializeToString()) else: serialized_metadata = metadata # Note the identity to move the tensor to the CPU. return gen_summary_ops.write_summary( _summary_state.writer._resource, # pylint: disable=protected-access _choose_step(step), array_ops.identity(tensor), tag, serialized_metadata, name=scope) return summary_writer_function(name, tensor, function, family=family) def scalar(name, tensor, family=None, step=None): """Writes a scalar summary if possible. Unlike `tf.contrib.summary.generic` this op may change the dtype depending on the writer, for both practical and efficiency concerns. Args: name: An arbitrary name for this summary. tensor: A `tf.Tensor` Must be one of the following types: `float32`, `float64`, `int32`, `int64`, `uint8`, `int16`, `int8`, `uint16`, `half`, `uint32`, `uint64`. family: Optional, the summary's family. step: The `int64` monotonic step variable, which defaults to `tf.compat.v1.train.get_global_step`. Returns: The created `tf.Operation` or a `tf.no_op` if summary writing has not been enabled for this context. """ def function(tag, scope): # Note the identity to move the tensor to the CPU. return gen_summary_ops.write_scalar_summary( _summary_state.writer._resource, # pylint: disable=protected-access _choose_step(step), tag, array_ops.identity(tensor), name=scope) return summary_writer_function(name, tensor, function, family=family) def histogram(name, tensor, family=None, step=None): """Writes a histogram summary if possible.""" def function(tag, scope): # Note the identity to move the tensor to the CPU. return gen_summary_ops.write_histogram_summary( _summary_state.writer._resource, # pylint: disable=protected-access _choose_step(step), tag, array_ops.identity(tensor), name=scope) return summary_writer_function(name, tensor, function, family=family) def image(name, tensor, bad_color=None, max_images=3, family=None, step=None): """Writes an image summary if possible.""" def function(tag, scope): bad_color_ = (constant_op.constant([255, 0, 0, 255], dtype=dtypes.uint8) if bad_color is None else bad_color) # Note the identity to move the tensor to the CPU. return gen_summary_ops.write_image_summary( _summary_state.writer._resource, # pylint: disable=protected-access _choose_step(step), tag, array_ops.identity(tensor), bad_color_, max_images, name=scope) return summary_writer_function(name, tensor, function, family=family) def audio(name, tensor, sample_rate, max_outputs, family=None, step=None): """Writes an audio summary if possible.""" def function(tag, scope): # Note the identity to move the tensor to the CPU. return gen_summary_ops.write_audio_summary( _summary_state.writer._resource, # pylint: disable=protected-access _choose_step(step), tag, array_ops.identity(tensor), sample_rate=sample_rate, max_outputs=max_outputs, name=scope) return summary_writer_function(name, tensor, function, family=family) def graph_v1(param, step=None, name=None): """Writes a TensorFlow graph to the summary interface. The graph summary is, strictly speaking, not a summary. Conditions like `tf.summary.should_record_summaries` do not apply. Only a single graph can be associated with a particular run. If multiple graphs are written, then only the last one will be considered by TensorBoard. When not using eager execution mode, the user should consider passing the `graph` parameter to `tf.compat.v1.summary.initialize` instead of calling this function. Otherwise special care needs to be taken when using the graph to record the graph. Args: param: A `tf.Tensor` containing a serialized graph proto. When eager execution is enabled, this function will automatically coerce `tf.Graph`, `tf.compat.v1.GraphDef`, and string types. step: The global step variable. This doesn't have useful semantics for graph summaries, but is used anyway, due to the structure of event log files. This defaults to the global step. name: A name for the operation (optional). Returns: The created `tf.Operation` or a `tf.no_op` if summary writing has not been enabled for this context. Raises: TypeError: If `param` isn't already a `tf.Tensor` in graph mode. """ if not context.executing_eagerly() and not isinstance(param, ops.Tensor): raise TypeError("graph() needs a tf.Tensor (e.g. tf.placeholder) in graph " "mode, but was: %s" % type(param)) writer = _summary_state.writer if writer is None: return control_flow_ops.no_op() with ops.device("cpu:0"): if isinstance(param, (ops.Graph, graph_pb2.GraphDef)): tensor = ops.convert_to_tensor(_serialize_graph(param), dtypes.string) else: tensor = array_ops.identity(param) return gen_summary_ops.write_graph_summary( writer._resource, _choose_step(step), tensor, name=name) # pylint: disable=protected-access @tf_export("summary.graph", v1=[]) def graph(graph_data): """Writes a TensorFlow graph summary. Write an instance of `tf.Graph` or `tf.compat.v1.GraphDef` as summary only in an eager mode. Please prefer to use the trace APIs (`tf.summary.trace_on`, `tf.summary.trace_off`, and `tf.summary.trace_export`) when using `tf.function` which can automatically collect and record graphs from executions. Usage Example: ```py graph = tf.Graph() with graph.as_default(): c = tf.constant(30.0) writer = tf.summary.create_file_writer("/tmp/mylogs") with writer.as_default(): tf.summary.graph(graph) # Another example; must attain the concrete function graph manually. @tf.function def f(): x = constant_op.constant(2) y = constant_op.constant(3) return x**y with writer.as_default(): tf.summary.graph(f.get_concrete_function().graph) ``` Args: graph_data: The TensorFlow graph to write, as a `tf.Graph` or a `tf.compat.v1.GraphDef`. Returns: True on success, or False if no summary was written because no default summary writer was available. Raises: ValueError: `graph` summary API is invoked in a graph mode. """ if not context.executing_eagerly(): raise ValueError("graph() cannot be invoked inside a graph context.") writer = _summary_state.writer if writer is None: return constant_op.constant(False) with ops.device("cpu:0"): if not _should_record_summaries_v2(): return constant_op.constant(False) if isinstance(graph_data, (ops.Graph, graph_pb2.GraphDef)): tensor = ops.convert_to_tensor( _serialize_graph(graph_data), dtypes.string) else: raise ValueError("'graph_data' is not tf.Graph or tf.compat.v1.GraphDef") gen_summary_ops.write_graph_summary( writer._resource, # pylint: disable=protected-access # Graph does not have step. Set to 0. 0, tensor, ) return constant_op.constant(True) def import_event(tensor, name=None): """Writes a `tf.compat.v1.Event` binary proto. This can be used to import existing event logs into a new summary writer sink. Please note that this is lower level than the other summary functions and will ignore the `tf.summary.should_record_summaries` setting. Args: tensor: A `tf.Tensor` of type `string` containing a serialized `tf.compat.v1.Event` proto. name: A name for the operation (optional). Returns: The created `tf.Operation`. """ return gen_summary_ops.import_event( _summary_state.writer._resource, tensor, name=name) # pylint: disable=protected-access @tf_export("summary.flush", v1=[]) def flush(writer=None, name=None): """Forces summary writer to send any buffered data to storage. This operation blocks until that finishes. Args: writer: The `tf.summary.SummaryWriter` resource to flush. The thread default will be used if this parameter is None. Otherwise a `tf.no_op` is returned. name: A name for the operation (optional). Returns: The created `tf.Operation`. """ if writer is None: writer = _summary_state.writer if writer is None: return control_flow_ops.no_op() if isinstance(writer, ResourceSummaryWriter): resource = writer._resource # pylint: disable=protected-access else: # Assume we were passed a raw resource tensor. resource = writer with ops.device("cpu:0"): return gen_summary_ops.flush_summary_writer(resource, name=name) _flush_fn = flush # for within SummaryWriter.flush() def eval_dir(model_dir, name=None): """Construct a logdir for an eval summary writer.""" return os.path.join(model_dir, "eval" if not name else "eval_" + name) @deprecation.deprecated(date=None, instructions="Renamed to create_file_writer().") def create_summary_file_writer(*args, **kwargs): """Please use `tf.contrib.summary.create_file_writer`.""" logging.warning("Deprecation Warning: create_summary_file_writer was renamed " "to create_file_writer") return create_file_writer(*args, **kwargs) def _serialize_graph(arbitrary_graph): if isinstance(arbitrary_graph, ops.Graph): return arbitrary_graph.as_graph_def(add_shapes=True).SerializeToString() else: return arbitrary_graph.SerializeToString() def _choose_step(step): if step is None: return training_util.get_or_create_global_step() if not isinstance(step, ops.Tensor): return ops.convert_to_tensor(step, dtypes.int64) return step def _check_create_file_writer_args(inside_function, **kwargs): """Helper to check the validity of arguments to a create_file_writer() call. Args: inside_function: whether the create_file_writer() call is in a tf.function **kwargs: the arguments to check, as kwargs to give them names. Raises: ValueError: if the arguments are graph tensors. """ for arg_name, arg in kwargs.items(): if not isinstance(arg, ops.EagerTensor) and tensor_util.is_tf_type(arg): if inside_function: raise ValueError( "Invalid graph Tensor argument \"%s=%s\" to create_file_writer() " "inside an @tf.function. The create call will be lifted into the " "outer eager execution context, so it cannot consume graph tensors " "defined inside the function body." % (arg_name, arg)) else: raise ValueError( "Invalid graph Tensor argument \"%s=%s\" to eagerly executed " "create_file_writer()." % (arg_name, arg)) def run_metadata(name, data, step=None): """Writes entire RunMetadata summary. A RunMetadata can contain DeviceStats, partition graphs, and function graphs. Please refer to the proto for definition of each field. Args: name: A name for this summary. The summary tag used for TensorBoard will be this name prefixed by any active name scopes. data: A RunMetadata proto to write. step: Explicit `int64`-castable monotonic step value for this summary. If omitted, this defaults to `tf.summary.experimental.get_step()`, which must not be None. Returns: True on success, or false if no summary was written because no default summary writer was available. Raises: ValueError: if a default writer exists, but no step was provided and `tf.summary.experimental.get_step()` is None. """ summary_metadata = summary_pb2.SummaryMetadata() # Hard coding a plugin name. Please refer to go/tb-plugin-name-hardcode for # the rationale. summary_metadata.plugin_data.plugin_name = "graph_run_metadata" # version number = 1 summary_metadata.plugin_data.content = b"1" with summary_scope(name, "graph_run_metadata_summary", [data, step]) as (tag, _): with ops.device("cpu:0"): tensor = constant_op.constant(data.SerializeToString(), dtype=dtypes.string) return write( tag=tag, tensor=tensor, step=step, metadata=summary_metadata) def run_metadata_graphs(name, data, step=None): """Writes graphs from a RunMetadata summary. Args: name: A name for this summary. The summary tag used for TensorBoard will be this name prefixed by any active name scopes. data: A RunMetadata proto to write. step: Explicit `int64`-castable monotonic step value for this summary. If omitted, this defaults to `tf.summary.experimental.get_step()`, which must not be None. Returns: True on success, or false if no summary was written because no default summary writer was available. Raises: ValueError: if a default writer exists, but no step was provided and `tf.summary.experimental.get_step()` is None. """ summary_metadata = summary_pb2.SummaryMetadata() # Hard coding a plugin name. Please refer to go/tb-plugin-name-hardcode for # the rationale. summary_metadata.plugin_data.plugin_name = "graph_run_metadata_graph" # version number = 1 summary_metadata.plugin_data.content = b"1" data = config_pb2.RunMetadata( function_graphs=data.function_graphs, partition_graphs=data.partition_graphs) with summary_scope(name, "graph_run_metadata_graph_summary", [data, step]) as (tag, _): with ops.device("cpu:0"): tensor = constant_op.constant(data.SerializeToString(), dtype=dtypes.string) return write( tag=tag, tensor=tensor, step=step, metadata=summary_metadata) _TraceContext = collections.namedtuple("TraceContext", ("graph", "profiler")) _current_trace_context_lock = threading.Lock() _current_trace_context = None @tf_export("summary.trace_on", v1=[]) def trace_on(graph=True, profiler=False): # pylint: disable=redefined-outer-name """Starts a trace to record computation graphs and profiling information. Must be invoked in eager mode. When enabled, TensorFlow runtime will collection information that can later be exported and consumed by TensorBoard. The trace is activated across the entire TensorFlow runtime and affects all threads of execution. To stop the trace and export the collected information, use `tf.summary.trace_export`. To stop the trace without exporting, use `tf.summary.trace_off`. Args: graph: If True, enables collection of executed graphs. It includes ones from tf.function invocation and ones from the legacy graph mode. The default is True. profiler: If True, enables the advanced profiler. Enabling profiler implicitly enables the graph collection. The profiler may incur a high memory overhead. The default is False. """ if ops.inside_function(): logging.warn("Cannot enable trace inside a tf.function.") return if not context.executing_eagerly(): logging.warn("Must enable trace in eager mode.") return global _current_trace_context with _current_trace_context_lock: if _current_trace_context: logging.warn("Trace already enabled") return if graph and not profiler: context.context().enable_graph_collection() if profiler: context.context().enable_run_metadata() _profiler.start() _current_trace_context = _TraceContext(graph=graph, profiler=profiler) @tf_export("summary.trace_export", v1=[]) def trace_export(name, step=None, profiler_outdir=None): """Stops and exports the active trace as a Summary and/or profile file. Stops the trace and exports all metadata collected during the trace to the default SummaryWriter, if one has been set. Args: name: A name for the summary to be written. step: Explicit `int64`-castable monotonic step value for this summary. If omitted, this defaults to `tf.summary.experimental.get_step()`, which must not be None. profiler_outdir: Output directory for profiler. This is only used when the profiler was enabled when the trace was started. In that case, if there is a logdir-based default SummaryWriter, this defaults to the same directory, but otherwise the argument must be passed. Raises: ValueError: if a default writer exists, but no step was provided and `tf.summary.experimental.get_step()` is None. """ global _current_trace_context if ops.inside_function(): logging.warn("Cannot export trace inside a tf.function.") return if not context.executing_eagerly(): logging.warn("Can only export trace while executing eagerly.") return with _current_trace_context_lock: if _current_trace_context is None: raise ValueError("Must enable trace before export.") graph, profiler = _current_trace_context # pylint: disable=redefined-outer-name if profiler_outdir is None \ and isinstance(_summary_state.writer, ResourceSummaryWriter): logdir = _summary_state.writer._metadata.get("logdir") # pylint: disable=protected-access if logdir is not None: profiler_outdir = logdir if profiler and profiler_outdir is None: raise ValueError("Must set profiler_outdir or " "enable summary writer with logdir.") run_meta = context.context().export_run_metadata() if graph and not profiler: run_metadata_graphs(name, run_meta, step) else: run_metadata(name, run_meta, step) if profiler: _profiler.save(profiler_outdir, _profiler.stop()) trace_off() @tf_export("summary.trace_off", v1=[]) def trace_off(): """Stops the current trace and discards any collected information.""" global _current_trace_context with _current_trace_context_lock: if _current_trace_context is None: return # tracing already off graph, profiler = _current_trace_context # pylint: disable=redefined-outer-name, unpacking-non-sequence _current_trace_context = None if graph: # Disabling run_metadata disables graph collection as well. context.context().disable_run_metadata() if profiler: try: _profiler.stop() except _profiler.ProfilerNotRunningError: pass