diff options
Diffstat (limited to 'tensorflow/contrib/summary/summary_ops.py')
| -rw-r--r-- | tensorflow/contrib/summary/summary_ops.py | 166 |
1 files changed, 27 insertions, 139 deletions
diff --git a/tensorflow/contrib/summary/summary_ops.py b/tensorflow/contrib/summary/summary_ops.py index f6be99f6ae..56e3198593 100644 --- a/tensorflow/contrib/summary/summary_ops.py +++ b/tensorflow/contrib/summary/summary_ops.py @@ -19,12 +19,7 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function -import getpass import os -import re -import time - -import six from tensorflow.contrib.summary import gen_summary_ops from tensorflow.python.eager import context @@ -47,10 +42,6 @@ _SHOULD_RECORD_SUMMARIES_NAME = "ShouldRecordSummaries" _SUMMARY_COLLECTION_NAME = "_SUMMARY_V2" _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) - def should_record_summaries(): """Returns boolean Tensor which is true if summaries should be recorded.""" @@ -66,14 +57,12 @@ def should_record_summaries(): # TODO(apassos) consider how to handle local step here. @tf_contextlib.contextmanager -def record_summaries_every_n_global_steps(n, global_step=None): +def record_summaries_every_n_global_steps(n): """Sets the should_record_summaries Tensor to true if global_step % n == 0.""" - if global_step is None: - global_step = training_util.get_global_step() collection_ref = ops.get_collection_ref(_SHOULD_RECORD_SUMMARIES_NAME) old = collection_ref[:] with ops.device("cpu:0"): - collection_ref[:] = [math_ops.equal(global_step % n, 0)] + collection_ref[:] = [math_ops.equal(training_util.get_global_step() % n, 0)] yield collection_ref[:] = old @@ -141,8 +130,7 @@ def create_summary_file_writer(logdir, flush once the queue gets bigger than this. flush_millis: the largest interval between flushes. filename_suffix: optional suffix for the event file name. - name: Shared name for this SummaryWriter resource stored to default - Graph. + name: name for the summary writer. Returns: Either a summary writer or an empty object which can be used as a @@ -157,81 +145,14 @@ def create_summary_file_writer(logdir, flush_millis = constant_op.constant(2 * 60 * 1000) if filename_suffix is None: filename_suffix = constant_op.constant("") - return _make_summary_writer( - name, - gen_summary_ops.create_summary_file_writer, - logdir=logdir, - max_queue=max_queue, - flush_millis=flush_millis, - filename_suffix=filename_suffix) - - -def create_summary_db_writer(db_uri, - experiment_name=None, - run_name=None, - user_name=None, - name=None): - """Creates a summary database writer in the current context. - - This can be used to write tensors from the execution graph directly - to a database. Only SQLite is supported right now. This function - will create the schema if it doesn't exist. Entries in the Users, - Experiments, and Runs tables will be created automatically if they - don't already exist. - - Args: - db_uri: For example "file:/tmp/foo.sqlite". - experiment_name: Defaults to YYYY-MM-DD in local time if None. - Empty string means the Run will not be associated with an - Experiment. Can't contain ASCII control characters or <>. Case - sensitive. - run_name: Defaults to HH:MM:SS in local time if None. Empty string - means a Tag will not be associated with any Run. Can't contain - ASCII control characters or <>. Case sensitive. - user_name: Defaults to system username if None. Empty means the - Experiment will not be associated with a User. Must be valid as - both a DNS label and Linux username. - name: Shared name for this SummaryWriter resource stored to default - Graph. - - Returns: - A new SummaryWriter instance. - """ - with ops.device("cpu:0"): - if experiment_name is None: - experiment_name = time.strftime("%Y-%m-%d", time.localtime(time.time())) - if run_name is None: - run_name = time.strftime("%H:%M:%S", time.localtime(time.time())) - if user_name is None: - user_name = getpass.getuser() - experiment_name = _cleanse_string( - "experiment_name", _EXPERIMENT_NAME_PATTERNS, experiment_name) - run_name = _cleanse_string("run_name", _RUN_NAME_PATTERNS, run_name) - user_name = _cleanse_string("user_name", _USER_NAME_PATTERNS, user_name) - return _make_summary_writer( - name, - gen_summary_ops.create_summary_db_writer, - db_uri=db_uri, - experiment_name=experiment_name, - run_name=run_name, - user_name=user_name) - - -def _make_summary_writer(name, factory, **kwargs): - resource = gen_summary_ops.summary_writer(shared_name=name) - # TODO(apassos): Consider doing this instead. - # node = factory(resource, **kwargs) - # if not context.in_eager_mode(): - # ops.get_default_session().run(node) - ops.add_to_collection(_SUMMARY_WRITER_INIT_COLLECTION_NAME, - factory(resource, **kwargs)) - return SummaryWriter(resource) - - -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) + resource = gen_summary_ops.summary_writer(shared_name=name) + # TODO(apassos) ensure the initialization op runs when in graph mode; + # consider calling session.run here. + ops.add_to_collection( + _SUMMARY_WRITER_INIT_COLLECTION_NAME, + gen_summary_ops.create_summary_file_writer( + resource, logdir, max_queue, flush_millis, filename_suffix)) + return SummaryWriter(resource) def _nothing(): @@ -283,81 +204,68 @@ def summary_writer_function(name, tensor, function, family=None): return op -def generic(name, tensor, metadata=None, family=None, global_step=None): +def generic(name, tensor, metadata, family=None): """Writes a tensor summary if possible.""" - if global_step is None: - global_step = training_util.get_global_step() + 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( context.context().summary_writer_resource, - global_step, array_ops.identity(tensor), - tag, serialized_metadata, name=scope) + training_util.get_global_step(), array_ops.identity(tensor), + tag, metadata, name=scope) return summary_writer_function(name, tensor, function, family=family) -def scalar(name, tensor, family=None, global_step=None): +def scalar(name, tensor, family=None): """Writes a scalar summary if possible.""" - if global_step is None: - global_step = training_util.get_global_step() + def function(tag, scope): # Note the identity to move the tensor to the CPU. return gen_summary_ops.write_scalar_summary( context.context().summary_writer_resource, - global_step, tag, array_ops.identity(tensor), + training_util.get_global_step(), tag, array_ops.identity(tensor), name=scope) return summary_writer_function(name, tensor, function, family=family) -def histogram(name, tensor, family=None, global_step=None): +def histogram(name, tensor, family=None): """Writes a histogram summary if possible.""" - if global_step is None: - global_step = training_util.get_global_step() + def function(tag, scope): # Note the identity to move the tensor to the CPU. return gen_summary_ops.write_histogram_summary( context.context().summary_writer_resource, - global_step, tag, array_ops.identity(tensor), + training_util.get_global_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, - global_step=None): +def image(name, tensor, bad_color=None, max_images=3, family=None): """Writes an image summary if possible.""" - if global_step is None: - global_step = training_util.get_global_step() + 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( context.context().summary_writer_resource, - global_step, tag, array_ops.identity(tensor), + training_util.get_global_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, - global_step=None): +def audio(name, tensor, sample_rate, max_outputs, family=None): """Writes an audio summary if possible.""" - if global_step is None: - global_step = training_util.get_global_step() + def function(tag, scope): # Note the identity to move the tensor to the CPU. return gen_summary_ops.write_audio_summary( context.context().summary_writer_resource, - global_step, + training_util.get_global_step(), tag, array_ops.identity(tensor), sample_rate=sample_rate, @@ -367,26 +275,6 @@ def audio(name, tensor, sample_rate, max_outputs, family=None, return summary_writer_function(name, tensor, function, family=family) -def import_event(tensor, name=None): - """Writes a tf.Event binary proto. - - When using create_summary_db_writer(), this can be used alongside - tf.TFRecordReader to load event logs into the database. Please note - that this is lower level than the other summary functions and will - ignore any conditions set by methods like should_record_summaries(). - - Args: - tensor: A `Tensor` of type `string` containing a serialized `Event` - proto. - name: A name for the operation (optional). - - Returns: - The created Operation. - """ - return gen_summary_ops.import_event( - context.context().summary_writer_resource, tensor, name=name) - - 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) |