class WriterTensorboardX(metaclass=Singleton):
def __init__(self, log_dir, logger, enable):
self.log_dir = log_dir
self.writer = None
if enable:
log_dir = str(log_dir)
try:
self.writer = importlib.import_module(
'tensorboardX').SummaryWriter(log_dir)
except ImportError:
message = (
"Warning: TensorboardX visualization is configured "
"to use, but currently not installed on this machine. "
"Please install the package by 'pip install tensorboardx'"
" command or turn off the option "
"in the 'config.json' file.")
logger.warning(message)
self.step = 0
self.mode = ''
self.tb_writer_ftns = [
'add_scalar', 'add_scalars', 'add_image', 'add_images',
'add_audio', 'add_text', 'add_histogram', 'add_pr_curve',
'add_embedding', 'add_figure'
]
self.tag_mode_exceptions = ['add_histogram', 'add_embedding']
self.timer = Timer()
def set_step(self, step, mode='train'):
self.mode = mode
self.step = step
if step == 0:
self.timer.reset()
else:
duration = self.timer.check()
self.add_scalar('steps_per_sec', 1 / duration)
def __getattr__(self, name):
"""
If visualization is configured to use:
return add_data() methods of tensorboard with additional
information (step, tag) added.
Otherwise:
return a blank function handle that does nothing
"""
if name in self.tb_writer_ftns:
add_data = getattr(self.writer, name, None)
def wrapper(tag, data, *args, **kwargs):
if add_data is not None:
# add mode(train/valid) tag
if name not in self.tag_mode_exceptions:
tag = '{}/{}'.format(tag, self.mode)
add_data(tag, data, self.step, *args, **kwargs)
return wrapper
else:
# default action for returning methods defined in this class,
# set_step() for instance.
try:
attr = object.__getattr__(name)
except AttributeError:
raise AttributeError(
"type object 'WriterTensorboardX' has no attribute '{}'".
format(name))
return attr
class TensorboardWriter:
def __init__(self, log_dir, logger, enabled):
self.writer = None
self.selected_module = ""
if enabled:
log_dir = str(log_dir)
# Retrieve vizualization writer.
succeeded = False
for module in ["torch.utils.tensorboard", "tensorboardX"]:
try:
self.writer = importlib.import_module(module).SummaryWriter(log_dir)
succeeded = True
break
except ImportError:
succeeded = False
self.selected_module = module
if not succeeded:
message = "Warning: visualization (Tensorboard) is configured to use, but currently not installed on " \
"this machine. Please install either TensorboardX with 'pip install tensorboardx', upgrade " \
"PyTorch to version >= 1.1 for using 'torch.utils.tensorboard' or turn off the option in " \
"the 'config.json' file."
logger.warning(message)
self.step = 0
self.mode = ''
self.tb_writer_ftns = {
'add_scalar', 'add_scalars', 'add_image', 'add_images', 'add_audio',
'add_text', 'add_histogram', 'add_pr_curve', 'add_embedding'
}
self.tag_mode_exceptions = {'add_histogram', 'add_embedding'}
self.timer = Timer()
def set_step(self, step, mode='train'):
self.mode = mode
self.step = step
if step == 0:
self.timer.reset()
else:
duration = self.timer.check()
self.add_scalar('steps_per_sec', 1 / duration)
def __getattr__(self, name):
"""
If visualization is configured to use:
return add_data() methods of tensorboard with additional information (step, tag) added.
Otherwise:
return a blank function handle that does nothing
"""
if name in self.tb_writer_ftns:
add_data = getattr(self.writer, name, None)
def wrapper(tag, data, *args, **kwargs):
if add_data is not None:
# add mode(train/valid) tag
if name not in self.tag_mode_exceptions:
tag = '{}/{}'.format(tag, self.mode)
add_data(tag, data, self.step, *args, **kwargs)
return wrapper
else:
# default action for returning methods defined in this class, set_step() for instance.
try:
attr = object.__getattr__(name)
except AttributeError:
raise AttributeError("type object '{}' has no attribute '{}'".format(self.selected_module, name))
return attr
