def make_default_logger(
label: str,
save_data: bool = True,
time_delta: float = 1.0,
) -> base.Logger:
"""Make a default Acme logger.
Args:
label: Name to give to the logger.
save_data: Ignored.
time_delta: Time (in seconds) between logging events.
Returns:
A logger (pipe) object that responds to logger.write(some_dict).
"""
terminal_logger = terminal.TerminalLogger(label=label, time_delta=time_delta)
loggers = [terminal_logger]
if save_data:
loggers.append(csv.CSVLogger(label))
logger = aggregators.Dispatcher(loggers)
logger = filters.NoneFilter(logger)
logger = filters.TimeFilter(logger, time_delta)
return logger
def make_default_logger(
logdir: str,
label: str,
save_data: bool = True,
time_delta: float = 0.0,
) -> base.Logger:
"""Make a default Acme logger.
Args:
label: Name to give to the logger.
save_data: Ignored.
time_delta: Time (in seconds) between logging events.
Returns:
A logger (pipe) object that responds to logger.write(some_dict).
"""
loggers = []
# TODO: temporarily disable terminal logger for environment.
if 'agent' in label:
loggers.append(terminal.TerminalLogger(label=label, time_delta=time_delta))
if save_data:
loggers.append(csv.CSVLogger(logdir=logdir, label=label))
loggers.append(tf_summary.TFSummaryLogger(logdir=logdir, label=label))
logger = aggregators.Dispatcher(loggers)
logger = filters.NoneFilter(logger)
logger = filters.TimeFilter(logger, time_delta)
if save_data:
logger = wrapper.CSVDumper(logger, label=label, logdir=logdir)
return logger
def make_default_logger(
label: str,
save_data: bool = True,
time_delta: float = 1.0,
asynchronous: bool = False,
print_fn: Optional[Callable[[str], None]] = None,
serialize_fn: Optional[Callable[[Mapping[str, Any]], str]] = base.to_numpy,
steps_key: str = 'steps',
) -> base.Logger:
"""Makes a default Acme logger.
Args:
label: Name to give to the logger.
save_data: Whether to persist data.
time_delta: Time (in seconds) between logging events.
asynchronous: Whether the write function should block or not.
print_fn: How to print to terminal (defaults to print).
serialize_fn: An optional function to apply to the write inputs before
passing them to the various loggers.
steps_key: Ignored.
Returns:
A logger object that responds to logger.write(some_dict).
"""
del steps_key
if not print_fn:
print_fn = logging.info
terminal_logger = terminal.TerminalLogger(label=label, print_fn=print_fn)
loggers = [terminal_logger]
if save_data:
loggers.append(csv.CSVLogger(label=label))
# Dispatch to all writers and filter Nones and by time.
logger = aggregators.Dispatcher(loggers, serialize_fn)
logger = filters.NoneFilter(logger)
if asynchronous:
logger = async_logger.AsyncLogger(logger)
logger = filters.TimeFilter(logger, time_delta)
return logger
def create_default_logger(
label: str,
tf_summary_logdir: str,
save_data: bool = True,
step_filter_delta: int = 1,
time_delta: float = 1.0,
asynchronous: bool = False,
print_fn: Optional[Callable[[str], None]] = None,
serialize_fn: Optional[Callable[[Mapping[str, Any]], str]] = base.to_numpy,
steps_key: str = 'steps',
extra_primary_keys: Optional[PrimaryKeyList] = None,
) -> base.Logger:
"""Creates a logger that has TerminalLogger and TF Summary."""
loggers = []
terminal_logger = terminal.TerminalLogger(label, print_fn=print)
loggers.append(terminal_logger)
# tensorboard logger
tf_logger = tf_summary.TFSummaryLogger(
logdir=tf_summary_logdir,
label=label,
steps_key=steps_key,
# steps_key=None,
)
loggers.append(tf_logger)
# aggregate and add modifiers
logger = aggregators.Dispatcher(loggers, serialize_fn=serialize_fn)
logger = filters.NoneFilter(logger)
if step_filter_delta > 1:
logger = StepFilter(logger,
steps_label=steps_key,
delta=step_filter_delta)
logger = filters.TimeFilter(logger, time_delta=time_delta)
if asynchronous:
logger = async_logger.AsyncLogger(logger)
# logger = filters.TimeFilter(logger, time_delta=time_delta)
# logger = logger_filters.FlattenDictLogger(logger, label=label)
return logger