def __init__(self, log_dir=None, comment='', **kwargs):
"""
Args:
log_dir (string): save location, default is: runs/**CURRENT_DATETIME_HOSTNAME**, which changes after each
run. Use hierarchical folder structure to compare between runs easily. e.g. 'runs/exp1', 'runs/exp2'
comment (string): comment that appends to the default ``log_dir``. If ``log_dir`` is assigned,
this argument will no effect.
purge_step (int):
When logging crashes at step :math:`T+X` and restarts at step :math:`T`, any events
whose global_step larger or euqal to :math:`T` will be purged and hiding from TensorBoard.
Note that the resumed experiment and the crashed experiment should have the same ``log_dir``.
filename_suffix (string):
Every event file's name is suffixed with suffix. example: ``SummaryWriter(filename_suffix='.123')``
kwargs: extra keyword arguments for FileWriter (e.g. 'flush_secs'
controls how often to flush pending events). For more arguments
please refer to docs for 'tf.summary.FileWriter'.
"""
if not log_dir:
import socket
from datetime import datetime
current_time = datetime.now().strftime('%b%d_%H-%M-%S')
log_dir = os.path.join(
'runs', current_time + '_' + socket.gethostname() + comment)
if 'purge_step' in kwargs.keys():
most_recent_step = kwargs.pop('purge_step')
if not os.path.exists(log_dir):
print('warning: you are purging unexisting data.')
self.file_writer = FileWriter(logdir=log_dir, **kwargs)
self.file_writer.add_event(
Event(step=most_recent_step, file_version='brain.Event:2'))
self.file_writer.add_event(
Event(step=most_recent_step,
session_log=SessionLog(status=SessionLog.START)))
else:
self.file_writer = FileWriter(logdir=log_dir, **kwargs)
# Create default bins for histograms, see generate_testdata.py in
# tensorflow/tensorboard
v = 1E-12
buckets = []
neg_buckets = []
while v < 1E20:
buckets.append(v)
neg_buckets.append(-v)
v *= 1.1
self.default_bins = neg_buckets[::-1] + [0] + buckets
self.all_writers = {self.file_writer.get_logdir(): self.file_writer}
# {writer_id : [[timestamp, step, value],...],...}
self.scalar_dict = {}
# TODO (ml7): Remove try-except when PyTorch 1.0 merges PyTorch and
# Caffe2
try:
import caffe2
from caffe2.python import workspace # workaround for pytorch/issue#10249
self.caffe2_enabled = True
except (SystemExit, ImportError):
self.caffe2_enabled = False
def __iter__(self) -> Event:
"""Iterates over events in the current events file.
Returns:
An Event object
"""
while True:
header_size = struct.calcsize("Q")
header = self._read_and_check(header_size)
if header is None:
break
event_size = struct.unpack("Q", header)[0]
event_raw = self._read_and_check(event_size)
if event_raw is None:
raise EventReadingException("Unexpected end of events file")
event = Event()
event.ParseFromString(event_raw)
yield event
def __iter__(self) -> Event:
"""
Iterates over events in the current events file
:return: An Event object
:except: NotImplementedError if the stream is in non-blocking mode.
:except: EventReadingError on reading error.
"""
while True:
header_size = struct.calcsize('Q')
header = self._read_and_check(header_size)
if header is None:
break
event_size = struct.unpack('Q', header)[0]
event_raw = self._read_and_check(event_size)
if event_raw is None:
raise EventReadingError('Unexpected end of events file')
event = Event()
event.ParseFromString(event_raw)
yield event
