def _tf_events(self):
try:
from tensorboard.backend.event_processing.event_accumulator \
import _GeneratorFromPath
except ImportError as e:
log.debug("error importing event generator: %s", e)
return None
else:
return _GeneratorFromPath(self.dir).Load()
def _tf_events(self):
try:
from tensorboard.backend.event_processing.event_accumulator \
import _GeneratorFromPath
except ImportError as e:
log.debug("error importing event generator: %s", e)
return None
else:
with warnings.catch_warnings():
warnings.simplefilter("ignore", Warning)
return _GeneratorFromPath(self.dir).Load()
def __iter__(self):
"""Yields (tag, val, step) for scalars.
"""
try:
from tensorboard.backend.event_processing import event_accumulator
except ImportError:
pass
else:
events = event_accumulator._GeneratorFromPath(self.dir).Load()
for event in events:
if not event.HasField("summary"):
continue
for val in event.summary.value:
if not val.HasField("simple_value"):
continue
yield val.tag, val.simple_value, event.step
def __iter__(self):
"""Yields (tag, val, step) for scalars.
"""
try:
from tensorboard.backend.event_processing import event_accumulator
except ImportError:
pass
else:
events = event_accumulator._GeneratorFromPath(self.dir).Load()
try:
for event in events:
if not event.HasField("summary"):
continue
for val in event.summary.value:
if not val.HasField("simple_value"):
continue
yield val.tag, val.simple_value, event.step
except RuntimeError as e:
# PEP 479 landed in Python 3.7 and TB triggers this
# runtime error when there are no events to read.
if e.args[0] != "generator raised StopIteration":
raise
def _maybe_scalars(self, fields, run):
from tensorboard.backend.event_processing import event_multiplexer
from tensorboard.backend.event_processing import event_accumulator
_ensure_tf_logger_patched()
scalars = {}
for path in event_multiplexer.GetLogdirSubdirectories(run.path):
events_checksum_field_name = self._events_checksum_field_name(path)
last_checksum = fields.get(events_checksum_field_name)
cur_checksum = self._events_checksum(path)
log.debug("event path checksums for %s: last=%s, cur=%s", path,
last_checksum, cur_checksum)
if last_checksum == cur_checksum:
continue
scalars[events_checksum_field_name] = cur_checksum
log.debug("indexing events in %s", path)
rel_path = os.path.relpath(path, run.path)
events = event_accumulator._GeneratorFromPath(path).Load()
scalar_vals = self._scalar_vals(events, rel_path)
for key, vals in scalar_vals.items():
if not vals:
continue
self._store_scalar_vals(key, vals, scalars)
return scalars
def _maybe_scalars(self, fields, run):
from tensorboard.backend.event_processing import io_wrapper
from tensorboard.backend.event_processing import event_accumulator
from guild import tfevent
tfevent.ensure_tf_logging_patched()
scalars = {}
scalar_aliases = self._init_scalar_aliases(run)
for path in io_wrapper.GetLogdirSubdirectories(run.path):
if not self._path_in_run(path, run):
log.debug("%s is not part of run %s, skipping", path, run.id)
continue
events_checksum_field_name = self._events_checksum_field_name(path)
last_checksum = fields.get(events_checksum_field_name)
cur_checksum = self._events_checksum(path)
log.debug("event path checksums for %s: last=%s, cur=%s", path,
last_checksum, cur_checksum)
if last_checksum != cur_checksum:
log.debug("indexing events in %s", path)
rel_path = os.path.relpath(path, run.path)
events = event_accumulator._GeneratorFromPath(path).Load()
scalar_vals = self._scalar_vals(events, rel_path)
self._apply_scalar_vals(scalar_vals, scalars, scalar_aliases)
scalars[events_checksum_field_name] = cur_checksum
return scalars
def clean(self):
self.ea_tracker._generator = tfproto._GeneratorFromPath(self.path) # pylint: disable=protected-access
self._clean()
def _init_event_loader(path):
# pylint: disable=protected-access
return event_accumulator._GeneratorFromPath(path)
def iter_tf_events(dir):
from tensorboard.backend.event_processing import event_accumulator
with warnings.catch_warnings():
warnings.simplefilter("ignore", Warning)
return event_accumulator._GeneratorFromPath(dir).Load()
