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def text_array_to_html(text_arr):
"""Take a numpy.ndarray containing strings, and convert it into html. If the ndarray contains a single scalar string, that stri... |
if not text_arr.shape:
# It is a scalar. No need to put it in a table, just apply markdown
return plugin_util.markdown_to_safe_html(np.asscalar(text_arr))
warning = ''
if len(text_arr.shape) > 2:
warning = plugin_util.markdown_to_safe_html(WARNING_TEMPLATE
... |
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def process_string_tensor_event(event):
"""Convert a TensorEvent into a JSON-compatible response.""" |
string_arr = tensor_util.make_ndarray(event.tensor_proto)
html = text_array_to_html(string_arr)
return {
'wall_time': event.wall_time,
'step': event.step,
'text': html,
} |
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def create_summary_metadata(display_name, description, num_thresholds):
"""Create a `summary_pb2.SummaryMetadata` proto for pr_curves plugin data. Arguments: dis... |
pr_curve_plugin_data = plugin_data_pb2.PrCurvePluginData(
version=PROTO_VERSION, num_thresholds=num_thresholds)
content = pr_curve_plugin_data.SerializeToString()
return summary_pb2.SummaryMetadata(
display_name=display_name,
summary_description=description,
plugin_data=summary_pb2.Summar... |
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def parse_plugin_metadata(content):
"""Parse summary metadata to a Python object. Arguments: content: The `content` field of a `SummaryMetadata` proto correspond... |
if not isinstance(content, bytes):
raise TypeError('Content type must be bytes')
result = plugin_data_pb2.PrCurvePluginData.FromString(content)
if result.version == 0:
return result
else:
logger.warn(
'Unknown metadata version: %s. The latest version known to '
'this build of Tensor... |
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def get_field_to_observations_map(generator, query_for_tag=''):
"""Return a field to `Observations` dict for the event generator. Args: generator: A generator ov... |
def increment(stat, event, tag=''):
assert stat in TRACKED_FIELDS
field_to_obs[stat].append(Observation(step=event.step,
wall_time=event.wall_time,
tag=tag)._asdict())
field_to_obs = dict([(t, []) for t in TRACKED_FIELDS]... |
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def get_unique_tags(field_to_obs):
"""Returns a dictionary of tags that a user could query over. Args: field_to_obs: Dict that maps string field to `Observation`... |
return {field: sorted(set([x.get('tag', '') for x in observations]))
for field, observations in field_to_obs.items()
if field in TAG_FIELDS} |
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def print_dict(d, show_missing=True):
"""Prints a shallow dict to console. Args: d: Dict to print. show_missing: Whether to show keys with empty values. """ |
for k, v in sorted(d.items()):
if (not v) and show_missing:
# No instances of the key, so print missing symbol.
print('{} -'.format(k))
elif isinstance(v, list):
# Value is a list, so print each item of the list.
print(k)
for item in v:
print(' {}'.format(item))
el... |
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def get_dict_to_print(field_to_obs):
"""Transform the field-to-obs mapping into a printable dictionary. Args: field_to_obs: Dict that maps string field to `Obser... |
def compressed_steps(steps):
return {'num_steps': len(set(steps)),
'min_step': min(steps),
'max_step': max(steps),
'last_step': steps[-1],
'first_step': steps[0],
'outoforder_steps': get_out_of_order(steps)}
def full_steps(steps):
return {'steps... |
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def get_out_of_order(list_of_numbers):
"""Returns elements that break the monotonically non-decreasing trend. This is used to find instances of global step value... |
# TODO: Consider changing this to only check for out-of-order
# steps within a particular tag.
result = []
# pylint: disable=consider-using-enumerate
for i in range(len(list_of_numbers)):
if i == 0:
continue
if list_of_numbers[i] < list_of_numbers[i - 1]:
result.append((list_of_numbers[i ... |
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def generators_from_logdir(logdir):
"""Returns a list of event generators for subdirectories with event files. The number of generators returned should equal the... |
subdirs = io_wrapper.GetLogdirSubdirectories(logdir)
generators = [
itertools.chain(*[
generator_from_event_file(os.path.join(subdir, f))
for f in tf.io.gfile.listdir(subdir)
if io_wrapper.IsTensorFlowEventsFile(os.path.join(subdir, f))
]) for subdir in subdirs
]
retur... |
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def get_inspection_units(logdir='', event_file='', tag=''):
"""Returns a list of InspectionUnit objects given either logdir or event_file. If logdir is given, th... |
if logdir:
subdirs = io_wrapper.GetLogdirSubdirectories(logdir)
inspection_units = []
for subdir in subdirs:
generator = itertools.chain(*[
generator_from_event_file(os.path.join(subdir, f))
for f in tf.io.gfile.listdir(subdir)
if io_wrapper.IsTensorFlowEventsFile(os.p... |
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def inspect(logdir='', event_file='', tag=''):
"""Main function for inspector that prints out a digest of event files. Args: logdir: A log directory that contain... |
print(PRINT_SEPARATOR +
'Processing event files... (this can take a few minutes)\n' +
PRINT_SEPARATOR)
inspection_units = get_inspection_units(logdir, event_file, tag)
for unit in inspection_units:
if tag:
print('Event statistics for tag {} in {}:'.format(tag, unit.name))
else:
... |
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def load(self, context):
"""Returns the debugger plugin, if possible. Args: context: The TBContext flags including `add_arguments`. Returns: A DebuggerPlugin ins... |
if not (context.flags.debugger_data_server_grpc_port > 0 or
context.flags.debugger_port > 0):
return None
flags = context.flags
try:
# pylint: disable=g-import-not-at-top,unused-import
import tensorflow
except ImportError:
raise ImportError(
'To use the deb... |
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def create_summary_metadata(hparams_plugin_data_pb):
"""Returns a summary metadata for the HParams plugin. Returns a summary_pb2.SummaryMetadata holding a copy o... |
if not isinstance(hparams_plugin_data_pb, plugin_data_pb2.HParamsPluginData):
raise TypeError('Needed an instance of plugin_data_pb2.HParamsPluginData.'
' Got: %s' % type(hparams_plugin_data_pb))
content = plugin_data_pb2.HParamsPluginData()
content.CopyFrom(hparams_plugin_data_pb)
cont... |
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def _parse_plugin_data_as(content, data_oneof_field):
"""Returns a data oneof's field from plugin_data.content. Raises HParamsError if the content doesn't have '... |
plugin_data = plugin_data_pb2.HParamsPluginData.FromString(content)
if plugin_data.version != PLUGIN_DATA_VERSION:
raise error.HParamsError(
'Only supports plugin_data version: %s; found: %s in: %s' %
(PLUGIN_DATA_VERSION, plugin_data.version, plugin_data))
if not plugin_data.HasField(data_on... |
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def write_event(self, event):
"""Writes an event proto to disk. This method is threadsafe with respect to invocations of itself. Args: event: The event proto. Ra... |
self._lock.acquire()
try:
self._events_writer.WriteEvent(event)
self._event_count += 1
if self._always_flush:
# We flush on every event within the integration test.
self._events_writer.Flush()
if self._event_count == self._check_this_often:
# Every so often, we ... |
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def dispose(self):
"""Disposes of this events writer manager, making it no longer usable. Call this method when this object is done being used in order to clean ... |
self._lock.acquire()
self._events_writer.Close()
self._events_writer = None
self._lock.release() |
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def _create_events_writer(self, directory):
"""Creates a new events writer. Args: directory: The directory in which to write files containing events. Returns: A ... |
total_size = 0
events_files = self._fetch_events_files_on_disk()
for file_name in events_files:
file_path = os.path.join(self._events_directory, file_name)
total_size += tf.io.gfile.stat(file_path).length
if total_size >= self.total_file_size_cap_bytes:
# The total size written to di... |
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def _fetch_events_files_on_disk(self):
"""Obtains the names of debugger-related events files within the directory. Returns: The names of the debugger-related eve... |
all_files = tf.io.gfile.listdir(self._events_directory)
relevant_files = [
file_name for file_name in all_files
if _DEBUGGER_EVENTS_FILE_NAME_REGEX.match(file_name)
]
return sorted(relevant_files, key=self._obtain_file_index) |
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def reexport_tf_summary():
"""Re-export all symbols from the original tf.summary. This function finds the original tf.summary V2 API and re-exports all the symbo... |
import sys # pylint: disable=g-import-not-at-top
# API packages to check for the original V2 summary API, in preference order
# to avoid going "under the hood" to the _api packages unless necessary.
packages = [
'tensorflow',
'tensorflow.compat.v2',
'tensorflow._api.v2',
'tensorflow._... |
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def bench(image, thread_count):
"""Encode `image` to PNG on `thread_count` threads in parallel. Returns: A `float` representing number of seconds that it takes a... |
threads = [threading.Thread(target=lambda: encoder.encode_png(image))
for _ in xrange(thread_count)]
start_time = datetime.datetime.now()
for thread in threads:
thread.start()
for thread in threads:
thread.join()
end_time = datetime.datetime.now()
delta = (end_time - start_time).total_... |
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def _image_of_size(image_size):
"""Generate a square RGB test image of the given side length.""" |
return np.random.uniform(0, 256, [image_size, image_size, 3]).astype(np.uint8) |
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def _format_line(headers, fields):
"""Format a line of a table. Arguments: headers: A list of strings that are used as the table headers. fields: A list of the s... |
assert len(fields) == len(headers), (fields, headers)
fields = ["%2.4f" % field if isinstance(field, float) else str(field)
for field in fields]
return ' '.join(' ' * max(0, len(header) - len(field)) + field
for (header, field) in zip(headers, fields)) |
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def get_gated_grpc_tensors(self, matching_debug_op=None):
"""Extract all nodes with gated-gRPC debug ops attached. Uses cached values if available. This method i... |
with self._grpc_gated_lock:
matching_debug_op = matching_debug_op or 'DebugIdentity'
if matching_debug_op not in self._grpc_gated_tensors:
# First, construct a map from node name to op type.
node_name_to_op_type = dict(
(node.name, node.op) for node in self._graph_def.node)
... |
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def maybe_base_expanded_node_name(self, node_name):
"""Expand the base name if there are node names nested under the node. For example, if there are two nodes in... |
with self._node_name_lock:
# Lazily populate the map from original node name to base-expanded ones.
if self._maybe_base_expanded_node_names is None:
self._maybe_base_expanded_node_names = dict()
# Sort all the node names.
sorted_names = sorted(node.name for node in self._graph_d... |
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def Reload(self):
"""Load events from every detected run.""" |
logger.info('Beginning DbImportMultiplexer.Reload()')
# Defer event sink creation until needed; this ensures it will only exist in
# the thread that calls Reload(), since DB connections must be thread-local.
if not self._event_sink:
self._event_sink = self._CreateEventSink()
# Use collections... |
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def load_batches(self):
"""Returns a batched event iterator over the run directory event files.""" |
event_iterator = self._directory_watcher.Load()
while True:
events = []
event_bytes = 0
start = time.time()
for event_proto in event_iterator:
events.append(event_proto)
event_bytes += len(event_proto)
if len(events) >= self._BATCH_COUNT or event_bytes >= self._B... |
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def _process_event(self, event, tagged_data):
"""Processes a single tf.Event and records it in tagged_data.""" |
event_type = event.WhichOneof('what')
# Handle the most common case first.
if event_type == 'summary':
for value in event.summary.value:
value = data_compat.migrate_value(value)
tag, metadata, values = tagged_data.get(value.tag, (None, None, []))
values.append((event.step, eve... |
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def _buckets(data, bucket_count=None):
"""Create a TensorFlow op to group data into histogram buckets. Arguments: data: A `Tensor` of any shape. Must be castable... |
# TODO(nickfelt): remove on-demand imports once dep situation is fixed.
import tensorflow.compat.v1 as tf
if bucket_count is None:
bucket_count = summary_v2.DEFAULT_BUCKET_COUNT
with tf.name_scope('buckets', values=[data, bucket_count]), \
tf.control_dependencies([tf.assert_scalar(bucket_count),
... |
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def op(name, data, bucket_count=None, display_name=None, description=None, collections=None):
"""Create a legacy histogram summary op. Arguments: name: A unique ... |
# 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):
tensor = _bu... |
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def pb(name, data, bucket_count=None, display_name=None, description=None):
"""Create a legacy histogram summary protobuf. Arguments: name: A unique name for the... |
# TODO(nickfelt): remove on-demand imports once dep situation is fixed.
import tensorflow.compat.v1 as tf
if bucket_count is None:
bucket_count = summary_v2.DEFAULT_BUCKET_COUNT
data = np.array(data).flatten().astype(float)
if data.size == 0:
buckets = np.array([]).reshape((0, 3))
else:
min_ =... |
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def add(self, value):
"""Add a tensor the watch store.""" |
if self._disposed:
raise ValueError(
'Cannot add value: this _WatchStore instance is already disposed')
self._data.append(value)
if hasattr(value, 'nbytes'):
self._in_mem_bytes += value.nbytes
self._ensure_bytes_limits() |
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def num_in_memory(self):
"""Get number of values in memory.""" |
n = len(self._data) - 1
while n >= 0:
if isinstance(self._data[n], _TensorValueDiscarded):
break
n -= 1
return len(self._data) - 1 - n |
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def num_discarded(self):
"""Get the number of values discarded due to exceeding both limits.""" |
if not self._data:
return 0
n = 0
while n < len(self._data):
if not isinstance(self._data[n], _TensorValueDiscarded):
break
n += 1
return n |
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def query(self, time_indices):
"""Query the values at given time indices. Args: time_indices: 0-based time indices to query, as a `list` of `int`. Returns: Value... |
if self._disposed:
raise ValueError(
'Cannot query: this _WatchStore instance is already disposed')
if not isinstance(time_indices, (tuple, list)):
time_indices = [time_indices]
output = []
for time_index in time_indices:
if isinstance(self._data[time_index], _TensorValueDis... |
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def add(self, watch_key, tensor_value):
"""Add a tensor value. Args: watch_key: A string representing the debugger tensor watch, e.g., 'Dense_1/BiasAdd:0:DebugId... |
if watch_key not in self._tensor_data:
self._tensor_data[watch_key] = _WatchStore(
watch_key,
mem_bytes_limit=self._watch_mem_bytes_limit)
self._tensor_data[watch_key].add(tensor_value) |
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def query(self, watch_key, time_indices=None, slicing=None, mapping=None):
"""Query tensor store for a given watch_key. Args: watch_key: The watch key to query. ... |
if watch_key not in self._tensor_data:
raise KeyError("watch_key not found: %s" % watch_key)
if time_indices is None:
time_indices = '-1'
time_slicing = tensor_helper.parse_time_indices(time_indices)
all_time_indices = list(range(self._tensor_data[watch_key].num_total()))
sliced_time_i... |
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def _obtain_sampled_health_pills(self, run, node_names):
"""Obtains the health pills for a run sampled by the event multiplexer. This is much faster than the alt... |
runs_to_tags_to_content = self._event_multiplexer.PluginRunToTagToContent(
constants.DEBUGGER_PLUGIN_NAME)
if run not in runs_to_tags_to_content:
# The run lacks health pills.
return {}
# This is also a mapping between node name and plugin content because this
# plugin tags by nod... |
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def _tensor_proto_to_health_pill(self, tensor_event, node_name, device, output_slot):
"""Converts an event_accumulator.TensorEvent to a HealthPillEvent. Args: te... |
return self._process_health_pill_value(
wall_time=tensor_event.wall_time,
step=tensor_event.step,
device_name=device,
output_slot=output_slot,
node_name=node_name,
tensor_proto=tensor_event.tensor_proto) |
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def _obtain_health_pills_at_step(self, events_directory, node_names, step):
"""Reads disk to obtain the health pills for a run at a specific step. This could be ... |
# Obtain all files with debugger-related events.
pattern = os.path.join(events_directory, _DEBUGGER_EVENTS_GLOB_PATTERN)
file_paths = glob.glob(pattern)
if not file_paths:
raise IOError(
'No events files found that matches the pattern %r.' % pattern)
# Sort by name (and thus by ti... |
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def _process_health_pill_event(self, node_name_set, mapping, target_step, file_path):
"""Creates health pills out of data in an event. Creates health pills out o... |
events_loader = event_file_loader.EventFileLoader(file_path)
for event in events_loader.Load():
if not event.HasField('summary'):
logger.warn(
'An event in a debugger events file lacks a summary.')
continue
if event.step < target_step:
# This event is not of the... |
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def _process_health_pill_value(self, wall_time, step, device_name, output_slot, node_name, tensor_proto, node_name_set=None):
"""Creates a HealthPillEvent contai... |
if node_name_set and node_name not in node_name_set:
# This event is not relevant.
return None
# Since we seek health pills for a specific step, this function
# returns 1 health pill per node per step. The wall time is the
# seconds since the epoch.
elements = list(tensor_util.make_nda... |
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def _info_to_string(info):
"""Convert a `TensorBoardInfo` to string form to be stored on disk. The format returned by this function is opaque and should only be ... |
for key in _TENSORBOARD_INFO_FIELDS:
field_type = _TENSORBOARD_INFO_FIELDS[key]
if not isinstance(getattr(info, key), field_type.runtime_type):
raise ValueError(
"expected %r of type %s, but found: %r" %
(key, field_type.runtime_type, getattr(info, key))
)
if info.version !=... |
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def _info_from_string(info_string):
"""Parse a `TensorBoardInfo` object from its string representation. Args: info_string: A string representation of a `TensorBo... |
try:
json_value = json.loads(info_string)
except ValueError:
raise ValueError("invalid JSON: %r" % (info_string,))
if not isinstance(json_value, dict):
raise ValueError("not a JSON object: %r" % (json_value,))
if json_value.get("version") != version.VERSION:
raise ValueError("incompatible vers... |
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def cache_key(working_directory, arguments, configure_kwargs):
"""Compute a `TensorBoardInfo.cache_key` field. The format returned by this function is opaque. Cl... |
if not isinstance(arguments, (list, tuple)):
raise TypeError(
"'arguments' should be a list of arguments, but found: %r "
"(use `shlex.split` if given a string)"
% (arguments,)
)
datum = {
"working_directory": working_directory,
"arguments": arguments,
"configure_k... |
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def _get_info_dir():
"""Get path to directory in which to store info files. The directory returned by this function is "owned" by this module. If the contents of... |
path = os.path.join(tempfile.gettempdir(), ".tensorboard-info")
try:
os.makedirs(path)
except OSError as e:
if e.errno == errno.EEXIST and os.path.isdir(path):
pass
else:
raise
else:
os.chmod(path, 0o777)
return path |
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def write_info_file(tensorboard_info):
"""Write TensorBoardInfo to the current process's info file. This should be called by `main` once the server is ready. Whe... |
payload = "%s\n" % _info_to_string(tensorboard_info)
with open(_get_info_file_path(), "w") as outfile:
outfile.write(payload) |
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def remove_info_file():
"""Remove the current process's TensorBoardInfo file, if it exists. If the file does not exist, no action is taken and no error is raised... |
try:
os.unlink(_get_info_file_path())
except OSError as e:
if e.errno == errno.ENOENT:
# The user may have wiped their temporary directory or something.
# Not a problem: we're already in the state that we want to be in.
pass
else:
raise |
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def get_all():
"""Return TensorBoardInfo values for running TensorBoard processes. This function may not provide a perfect snapshot of the set of running process... |
info_dir = _get_info_dir()
results = []
for filename in os.listdir(info_dir):
filepath = os.path.join(info_dir, filename)
try:
with open(filepath) as infile:
contents = infile.read()
except IOError as e:
if e.errno == errno.EACCES:
# May have been written by this module in... |
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def start(arguments, timeout=datetime.timedelta(seconds=60)):
"""Start a new TensorBoard instance, or reuse a compatible one. If the cache key determined by the ... |
match = _find_matching_instance(
cache_key(
working_directory=os.getcwd(),
arguments=arguments,
configure_kwargs={},
),
)
if match:
return StartReused(info=match)
(stdout_fd, stdout_path) = tempfile.mkstemp(prefix=".tensorboard-stdout-")
(stderr_fd, stderr_path)... |
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def _find_matching_instance(cache_key):
"""Find a running TensorBoard instance compatible with the cache key. Returns: A `TensorBoardInfo` object, or `None` if n... |
infos = get_all()
candidates = [info for info in infos if info.cache_key == cache_key]
for candidate in sorted(candidates, key=lambda x: x.port):
# TODO(@wchargin): Check here that the provided port is still live.
return candidate
return None |
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def _maybe_read_file(filename):
"""Read the given file, if it exists. Args: filename: A path to a file. Returns: A string containing the file contents, or `None`... |
try:
with open(filename) as infile:
return infile.read()
except IOError as e:
if e.errno == errno.ENOENT:
return None |
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def process_raw_trace(raw_trace):
"""Processes raw trace data and returns the UI data.""" |
trace = trace_events_pb2.Trace()
trace.ParseFromString(raw_trace)
return ''.join(trace_events_json.TraceEventsJsonStream(trace)) |
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def is_active(self):
"""Whether this plugin is active and has any profile data to show. Detecting profile data is expensive, so this process runs asynchronously ... |
# If we are already active, we remain active and don't recompute this.
# Otherwise, try to acquire the lock without blocking; if we get it and
# we're still not active, launch a thread to check if we're active and
# release the lock once the computation is finished. Either way, this
# thread return... |
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def _run_dir(self, run):
"""Helper that maps a frontend run name to a profile "run" directory. The frontend run name consists of the TensorBoard run name (aka th... |
run = run.rstrip('/')
if '/' not in run:
run = './' + run
tb_run_name, _, profile_run_name = run.rpartition('/')
tb_run_directory = self.multiplexer.RunPaths().get(tb_run_name)
if tb_run_directory is None:
# Check if logdir is a directory to handle case where it's actually a
# mul... |
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def generate_run_to_tools(self):
"""Generator for pairs of "run name" and a list of tools for that run. The "run name" here is a "frontend run name" - see _run_d... |
self.start_grpc_stub_if_necessary()
plugin_assets = self.multiplexer.PluginAssets(PLUGIN_NAME)
tb_run_names_to_dirs = self.multiplexer.RunPaths()
# Ensure that we also check the root logdir, even if it isn't a recognized
# TensorBoard run (i.e. has no tfevents file directly under it), to remain
... |
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def host_impl(self, run, tool):
"""Returns available hosts for the run and tool in the log directory. In the plugin log directory, each directory contains profil... |
hosts = {}
run_dir = self._run_dir(run)
if not run_dir:
logger.warn("Cannot find asset directory for: %s", run)
return hosts
tool_pattern = '*' + TOOLS[tool]
try:
files = tf.io.gfile.glob(os.path.join(run_dir, tool_pattern))
hosts = [os.path.basename(f).replace(TOOLS[tool], ... |
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def data_impl(self, request):
"""Retrieves and processes the tool data for a run and a host. Args: request: XMLHttpRequest Returns: A string that can be served t... |
run = request.args.get('run')
tool = request.args.get('tag')
host = request.args.get('host')
run_dir = self._run_dir(run)
# Profile plugin "run" is the last component of run dir.
profile_run = os.path.basename(run_dir)
if tool not in TOOLS:
return None
self.start_grpc_stub_if_ne... |
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def run(logdir, run_name, initial_temperature, ambient_temperature, heat_coefficient):
"""Run a temperature simulation. This will simulate an object at temperatu... |
tf.compat.v1.reset_default_graph()
tf.compat.v1.set_random_seed(0)
with tf.name_scope('temperature'):
# Create a mutable variable to hold the object's temperature, and
# create a scalar summary to track its value over time. The name of
# the summary will appear as "temperature/current" due to the
... |
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def Cleanse(obj, encoding='utf-8'):
"""Makes Python object appropriate for JSON serialization. - Replaces instances of Infinity/-Infinity/NaN with strings. - Tur... |
if isinstance(obj, int):
return obj
elif isinstance(obj, float):
if obj == _INFINITY:
return 'Infinity'
elif obj == _NEGATIVE_INFINITY:
return '-Infinity'
elif math.isnan(obj):
return 'NaN'
else:
return obj
elif isinstance(obj, bytes):
return tf.compat.as_text(obj,... |
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def op(name, data, display_name=None, description=None, collections=None):
"""Create a legacy text summary op. Text data summarized via this plugin will be visib... |
# 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.cont... |
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def pb(name, data, display_name=None, description=None):
"""Create a legacy text summary protobuf. Arguments: name: A name for the generated node. Will also serv... |
# TODO(nickfelt): remove on-demand imports once dep situation is fixed.
import tensorflow.compat.v1 as tf
try:
tensor = tf.make_tensor_proto(data, dtype=tf.string)
except TypeError as e:
raise ValueError(e)
if display_name is None:
display_name = name
summary_metadata = metadata.create_summar... |
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def _GeneratorFromPath(path):
"""Create an event generator for file or directory at given path string.""" |
if not path:
raise ValueError('path must be a valid string')
if io_wrapper.IsTensorFlowEventsFile(path):
return event_file_loader.EventFileLoader(path)
else:
return directory_watcher.DirectoryWatcher(
path,
event_file_loader.EventFileLoader,
io_wrapper.IsTensorFlowEventsFile) |
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def _ParseFileVersion(file_version):
"""Convert the string file_version in event.proto into a float. Args: file_version: String file_version from event.proto Ret... |
tokens = file_version.split('brain.Event:')
try:
return float(tokens[-1])
except ValueError:
## This should never happen according to the definition of file_version
## specified in event.proto.
logger.warn(
('Invalid event.proto file_version. Defaulting to use of '
'out-of-order ... |
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def Reload(self):
"""Loads all events added since the last call to `Reload`. If `Reload` was never called, loads all events in the file. Returns: The `EventAccum... |
with self._generator_mutex:
for event in self._generator.Load():
self._ProcessEvent(event)
return self |
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def RetrievePluginAsset(self, plugin_name, asset_name):
"""Return the contents of a given plugin asset. Args: plugin_name: The string name of a plugin. asset_nam... |
return plugin_asset_util.RetrieveAsset(self.path, plugin_name, asset_name) |
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def FirstEventTimestamp(self):
"""Returns the timestamp in seconds of the first event. If the first event has been loaded (either by this method or by `Reload`, ... |
if self._first_event_timestamp is not None:
return self._first_event_timestamp
with self._generator_mutex:
try:
event = next(self._generator.Load())
self._ProcessEvent(event)
return self._first_event_timestamp
except StopIteration:
raise ValueError('No event t... |
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def Graph(self):
"""Return the graph definition, if there is one. If the graph is stored directly, return that. If no graph is stored directly but a metagraph is... |
graph = graph_pb2.GraphDef()
if self._graph is not None:
graph.ParseFromString(self._graph)
return graph
raise ValueError('There is no graph in this EventAccumulator') |
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def MetaGraph(self):
"""Return the metagraph definition, if there is one. Raises: ValueError: If there is no metagraph for this run. Returns: The `meta_graph_def... |
if self._meta_graph is None:
raise ValueError('There is no metagraph in this EventAccumulator')
meta_graph = meta_graph_pb2.MetaGraphDef()
meta_graph.ParseFromString(self._meta_graph)
return meta_graph |
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def _CheckForRestartAndMaybePurge(self, event):
"""Check and discard expired events using SessionLog.START. Check for a SessionLog.START event and purge all prev... |
if event.HasField(
'session_log') and event.session_log.status == event_pb2.SessionLog.START:
self._Purge(event, by_tags=False) |
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def _ProcessHistogram(self, tag, wall_time, step, histo):
"""Processes a proto histogram by adding it to accumulated state.""" |
histo = self._ConvertHistogramProtoToTuple(histo)
histo_ev = HistogramEvent(wall_time, step, histo)
self.histograms.AddItem(tag, histo_ev)
self.compressed_histograms.AddItem(tag, histo_ev, self._CompressHistogram) |
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def _CompressHistogram(self, histo_ev):
"""Callback for _ProcessHistogram.""" |
return CompressedHistogramEvent(
histo_ev.wall_time,
histo_ev.step,
compressor.compress_histogram_proto(
histo_ev.histogram_value, self._compression_bps)) |
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def _ProcessImage(self, tag, wall_time, step, image):
"""Processes an image by adding it to accumulated state.""" |
event = ImageEvent(wall_time=wall_time,
step=step,
encoded_image_string=image.encoded_image_string,
width=image.width,
height=image.height)
self.images.AddItem(tag, event) |
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def _ProcessAudio(self, tag, wall_time, step, audio):
"""Processes a audio by adding it to accumulated state.""" |
event = AudioEvent(wall_time=wall_time,
step=step,
encoded_audio_string=audio.encoded_audio_string,
content_type=audio.content_type,
sample_rate=audio.sample_rate,
length_frames=audio.length_frames)
... |
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def _ProcessScalar(self, tag, wall_time, step, scalar):
"""Processes a simple value by adding it to accumulated state.""" |
sv = ScalarEvent(wall_time=wall_time, step=step, value=scalar)
self.scalars.AddItem(tag, sv) |
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def Load(self):
"""Loads all new events from disk as raw serialized proto bytestrings. Calling Load multiple times in a row will not 'drop' events as long as the... |
logger.debug('Loading events from %s', self._file_path)
# GetNext() expects a status argument on TF <= 1.7.
get_next_args = inspect.getargspec(self._reader.GetNext).args # pylint: disable=deprecated-method
# First argument is self
legacy_get_next = (len(get_next_args) > 1)
while True:
... |
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def Load(self):
"""Loads all new events from disk. Calling Load multiple times in a row will not 'drop' events as long as the return value is not iterated over. ... |
for record in super(EventFileLoader, self).Load():
yield event_pb2.Event.FromString(record) |
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def _parse_session_run_index(self, event):
"""Parses the session_run_index value from the event proto. Args: event: The event with metadata that contains the ses... |
metadata_string = event.log_message.message
try:
metadata = json.loads(metadata_string)
except ValueError as e:
logger.error(
"Could not decode metadata string '%s' for step value: %s",
metadata_string, e)
return constants.SENTINEL_FOR_UNDETERMINED_STEP
try:
... |
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def _serve_image_metadata(self, request):
"""Given a tag and list of runs, serve a list of metadata for images. Note that the images themselves are not sent; ins... |
tag = request.args.get('tag')
run = request.args.get('run')
sample = int(request.args.get('sample', 0))
response = self._image_response_for_run(run, tag, sample)
return http_util.Respond(request, response, 'application/json') |
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def _serve_individual_image(self, request):
"""Serves an individual image.""" |
run = request.args.get('run')
tag = request.args.get('tag')
index = int(request.args.get('index'))
sample = int(request.args.get('sample', 0))
data = self._get_individual_image(run, tag, index, sample)
image_type = imghdr.what(None, data)
content_type = _IMGHDR_TO_MIMETYPE.get(image_type, _... |
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def run_all(logdir, steps, thresholds, verbose=False):
"""Generate PR curve summaries. Arguments: logdir: The directory into which to store all the runs' data. s... |
# First, we generate data for a PR curve that assigns even weights for
# predictions of all classes.
run_name = 'colors'
if verbose:
print('--- Running: %s' % run_name)
start_runs(
logdir=logdir,
steps=steps,
run_name=run_name,
thresholds=thresholds)
# Next, we generate data fo... |
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def image(name, data, step=None, max_outputs=3, description=None):
"""Write an image summary. Arguments: name: A name for this summary. The summary tag used for ... |
summary_metadata = metadata.create_summary_metadata(
display_name=None, description=description)
# TODO(https://github.com/tensorflow/tensorboard/issues/2109): remove fallback
summary_scope = (
getattr(tf.summary.experimental, 'summary_scope', None) or
tf.summary.summary_scope)
with summary_s... |
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def set_examples(self, examples):
"""Sets the examples to be displayed in WIT. Args: examples: List of example protos. Returns: self, in order to enabled method ... |
self.store('examples', examples)
if len(examples) > 0:
self.store('are_sequence_examples',
isinstance(examples[0], tf.train.SequenceExample))
return self |
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def set_estimator_and_feature_spec(self, estimator, feature_spec):
"""Sets the model for inference as a TF Estimator. Instead of using TF Serving to host a model... |
# If custom function is set, remove it before setting estimator
self.delete('custom_predict_fn')
self.store('estimator_and_spec', {
'estimator': estimator, 'feature_spec': feature_spec})
self.set_inference_address('estimator')
# If no model name has been set, give a default
if not self.h... |
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def set_compare_estimator_and_feature_spec(self, estimator, feature_spec):
"""Sets a second model for inference as a TF Estimator. If you wish to compare the res... |
# If custom function is set, remove it before setting estimator
self.delete('compare_custom_predict_fn')
self.store('compare_estimator_and_spec', {
'estimator': estimator, 'feature_spec': feature_spec})
self.set_compare_inference_address('estimator')
# If no model name has been set, give a d... |
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def set_custom_predict_fn(self, predict_fn):
"""Sets a custom function for inference. Instead of using TF Serving to host a model for WIT to query, WIT can direc... |
# If estimator is set, remove it before setting predict_fn
self.delete('estimator_and_spec')
self.store('custom_predict_fn', predict_fn)
self.set_inference_address('custom_predict_fn')
# If no model name has been set, give a default
if not self.has_model_name():
self.set_model_name('1')
... |
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def set_compare_custom_predict_fn(self, predict_fn):
"""Sets a second custom function for inference. If you wish to compare the results of two models in WIT, use... |
# If estimator is set, remove it before setting predict_fn
self.delete('compare_estimator_and_spec')
self.store('compare_custom_predict_fn', predict_fn)
self.set_compare_inference_address('custom_predict_fn')
# If no model name has been set, give a default
if not self.has_compare_model_name():... |
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| def _sections_to_variance_sections(self, sections_over_time):
'''Computes the variance of corresponding sections over time.
Returns:
a list of np arrays.
'''
variance_sections = []
for i in range(len(sections_over_time[0])):
time_sections = [sections[i] for sections in sections_over_ti... |
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| def _maybe_clear_deque(self):
'''Clears the deque if certain parts of the config have changed.'''
for config_item in ['values', 'mode', 'show_all']:
if self.config[config_item] != self.old_config[config_item]:
self.sections_over_time.clear()
break
self.old_config = self.config
w... |
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def lazy_load(name):
"""Decorator to define a function that lazily loads the module 'name'. This can be used to defer importing troublesome dependencies - e.g. o... |
def wrapper(load_fn):
# Wrap load_fn to call it exactly once and update __dict__ afterwards to
# make future lookups efficient (only failed lookups call __getattr__).
@_memoize
def load_once(self):
if load_once.loading:
raise ImportError("Circular import when resolving LazyModule %r" % ... |
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def _memoize(f):
"""Memoizing decorator for f, which must have exactly 1 hashable argument.""" |
nothing = object() # Unique "no value" sentinel object.
cache = {}
# Use a reentrant lock so that if f references the resulting wrapper we die
# with recursion depth exceeded instead of deadlocking.
lock = threading.RLock()
@functools.wraps(f)
def wrapper(arg):
if cache.get(arg, nothing) is nothing:... |
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def tf():
"""Provide the root module of a TF-like API for use within TensorBoard. By default this is equivalent to `import tensorflow as tf`, but it can be used ... |
try:
from tensorboard.compat import notf # pylint: disable=g-import-not-at-top
except ImportError:
try:
import tensorflow # pylint: disable=g-import-not-at-top
return tensorflow
except ImportError:
pass
from tensorboard.compat import tensorflow_stub # pylint: disable=g-import-not... |
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def tf2():
"""Provide the root module of a TF-2.0 API for use within TensorBoard. Returns: The root module of a TF-2.0 API, if available. Raises: ImportError: if... |
# Import the `tf` compat API from this file and check if it's already TF 2.0.
if tf.__version__.startswith('2.'):
return tf
elif hasattr(tf, 'compat') and hasattr(tf.compat, 'v2'):
# As a fallback, try `tensorflow.compat.v2` if it's defined.
return tf.compat.v2
raise ImportError('cannot import tens... |
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def _pywrap_tensorflow():
"""Provide pywrap_tensorflow access in TensorBoard. pywrap_tensorflow cannot be accessed from tf.python.pywrap_tensorflow and needs to ... |
try:
from tensorboard.compat import notf # pylint: disable=g-import-not-at-top
except ImportError:
try:
from tensorflow.python import pywrap_tensorflow # pylint: disable=g-import-not-at-top
return pywrap_tensorflow
except ImportError:
pass
from tensorboard.compat.tensorflow_stub i... |
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def create_experiment_summary():
"""Returns a summary proto buffer holding this experiment.""" |
# Convert TEMPERATURE_LIST to google.protobuf.ListValue
temperature_list = struct_pb2.ListValue()
temperature_list.extend(TEMPERATURE_LIST)
materials = struct_pb2.ListValue()
materials.extend(HEAT_COEFFICIENTS.keys())
return summary.experiment_pb(
hparam_infos=[
api_pb2.HParamInfo(name='in... |
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def run(logdir, session_id, hparams, group_name):
"""Runs a temperature simulation. This will simulate an object at temperature `initial_temperature` sitting at ... |
tf.reset_default_graph()
tf.set_random_seed(0)
initial_temperature = hparams['initial_temperature']
ambient_temperature = hparams['ambient_temperature']
heat_coefficient = HEAT_COEFFICIENTS[hparams['material']]
session_dir = os.path.join(logdir, session_id)
writer = tf.summary.FileWriter(session_dir)
... |
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def get_filesystem(filename):
"""Return the registered filesystem for the given file.""" |
filename = compat.as_str_any(filename)
prefix = ""
index = filename.find("://")
if index >= 0:
prefix = filename[:index]
fs = _REGISTERED_FILESYSTEMS.get(prefix, None)
if fs is None:
raise ValueError("No recognized filesystem for prefix %s" % prefix)
return fs |
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def walk(top, topdown=True, onerror=None):
"""Recursive directory tree generator for directories. Args: top: string, a Directory name topdown: bool, Traverse pre... |
top = compat.as_str_any(top)
fs = get_filesystem(top)
try:
listing = listdir(top)
except errors.NotFoundError as err:
if onerror:
onerror(err)
else:
return
files = []
subdirs = []
for item in listing:
full_path = fs.join(top, compat.a... |
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Description:
def bucket_and_path(self, url):
"""Split an S3-prefixed URL into bucket and path.""" |
url = compat.as_str_any(url)
if url.startswith("s3://"):
url = url[len("s3://"):]
idx = url.index("/")
bucket = url[:idx]
path = url[(idx + 1):]
return bucket, path |
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Description:
def exists(self, filename):
"""Determines whether a path exists or not.""" |
client = boto3.client("s3")
bucket, path = self.bucket_and_path(filename)
r = client.list_objects(Bucket=bucket, Prefix=path, Delimiter="/")
if r.get("Contents") or r.get("CommonPrefixes"):
return True
return False |
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