class Logger:
def __init__(self, send_logs, tags, parameters, experiment=None):
self.stations = 5
self.send_logs = send_logs
if self.send_logs:
if experiment is None:
json_loc = glob.glob("./**/comet_token.json")[0]
with open(json_loc, "r") as f:
kwargs = json.load(f)
self.experiment = OfflineExperiment(**kwargs)
else:
self.experiment = experiment
self.sent_mb = 0
self.speed_window = deque(maxlen=100)
self.step_time = None
self.current_speed = 0
if self.send_logs:
if tags is not None:
self.experiment.add_tags(tags)
if parameters is not None:
self.experiment.log_parameters(parameters)
def begin_logging(self, episode_count, steps_per_ep, sigma, theta, step_time):
self.step_time = step_time
if self.send_logs:
self.experiment.log_parameter("Episode count", episode_count)
self.experiment.log_parameter("Steps per episode", steps_per_ep)
self.experiment.log_parameter("theta", theta)
self.experiment.log_parameter("sigma", sigma)
def log_round(self, states, reward, cumulative_reward, info, loss, observations, step):
self.experiment.log_histogram_3d(states, name="Observations", step=step)
info = [[j for j in i.split("|")] for i in info]
info = np.mean(np.array(info, dtype=np.float32), axis=0)
try:
round_mb = info[0]
except Exception as e:
print(info)
print(reward)
raise e
self.speed_window.append(round_mb)
self.current_speed = np.mean(np.asarray(self.speed_window)/self.step_time)
self.sent_mb += round_mb
CW = info[1]
CW_ax = info[2]
self.stations = info[3]
fairness = info[4]
if self.send_logs:
self.experiment.log_metric("Round reward", np.mean(reward), step=step)
self.experiment.log_metric("Per-ep reward", np.mean(cumulative_reward), step=step)
self.experiment.log_metric("Megabytes sent", self.sent_mb, step=step)
self.experiment.log_metric("Round megabytes sent", round_mb, step=step)
self.experiment.log_metric("Chosen CW for legacy devices", CW, step=step)
self.experiment.log_metric("Chosen CW for 802.11ax devices", CW_ax, step=step)
self.experiment.log_metric("Station count", self.stations, step=step)
self.experiment.log_metric("Current throughput", self.current_speed, step=step)
self.experiment.log_metric("Fairness index", fairness, step=step)
for i, obs in enumerate(observations):
self.experiment.log_metric(f"Observation {i}", obs, step=step)
self.experiment.log_metrics(loss, step=step)
def log_episode(self, cumulative_reward, speed, step):
if self.send_logs:
self.experiment.log_metric("Cumulative reward", cumulative_reward, step=step)
self.experiment.log_metric("Speed", speed, step=step)
self.sent_mb = 0
self.last_speed = speed
self.speed_window = deque(maxlen=100)
self.current_speed = 0
def end(self):
if self.send_logs:
self.experiment.end()
class CometWriter:
def __init__(self,
logger,
project_name: Optional[str] = None,
experiment_name: Optional[str] = None,
api_key: Optional[str] = None,
log_dir: Optional[str] = None,
offline: bool = False,
**kwargs):
if not _COMET_AVAILABLE:
raise ImportError(
"You want to use `comet_ml` logger which is not installed yet,"
" install it with `pip install comet-ml`.")
self.project_name = project_name
self.experiment_name = experiment_name
self.kwargs = kwargs
self.timer = Timer()
if (api_key is not None) and (log_dir is not None):
self.mode = "offline" if offline else "online"
self.api_key = api_key
self.log_dir = log_dir
elif api_key is not None:
self.mode = "online"
self.api_key = api_key
self.log_dir = None
elif log_dir is not None:
self.mode = "offline"
self.log_dir = log_dir
else:
logger.warning(
"CometLogger requires either api_key or save_dir during initialization."
)
if self.mode == "online":
self.experiment = CometExperiment(
api_key=self.api_key,
project_name=self.project_name,
**self.kwargs,
)
else:
self.experiment = CometOfflineExperiment(
offline_directory=self.log_dir,
project_name=self.project_name,
**self.kwargs,
)
if self.experiment_name:
self.experiment.set_name(self.experiment_name)
def set_step(self, step, epoch=None, mode='train') -> None:
self.mode = mode
self.step = step
self.epoch = epoch
if step == 0:
self.timer.reset()
else:
duration = self.timer.check()
self.add_scalar({'steps_per_sec': 1 / duration})
def log_hyperparams(self, params: Dict[str, Any]) -> None:
self.experiment.log_parameters(params)
def log_code(self, file_name=None, folder='models/') -> None:
self.experiment.log_code(file_name=file_name, folder=folder)
def add_scalar(self,
metrics: Dict[str, Union[torch.Tensor, float]],
step: Optional[int] = None,
epoch: Optional[int] = None) -> None:
metrics_renamed = {}
for key, val in metrics.items():
tag = '{}/{}'.format(key, self.mode)
if is_tensor(val):
metrics_renamed[tag] = val.cpu().detach()
else:
metrics_renamed[tag] = val
if epoch is None:
self.experiment.log_metrics(metrics_renamed,
step=self.step,
epoch=self.epoch)
else:
self.experiment.log_metrics(metrics_renamed, epoch=epoch)
def add_plot(self, figure_name, figure):
"""
Primarily for log gate plots
"""
self.experiment.log_figure(figure_name=figure_name, figure=figure)
def add_hist3d(self, hist, name):
"""
Primarily for log gate plots
"""
self.experiment.log_histogram_3d(hist, name=name)
def reset_experiment(self):
self.experiment = None
def finalize(self) -> None:
self.experiment.end()
self.reset_experiment()