def load_metrics(self):
"""
AUTHORS:
--------
Samuel Westlake, Alix Leroy
DESCRIPTION:
------------
Load metrics into the deeplodocus Metrics class
PARAMETERS:
-----------
None
RETURN:
-------
return: None
"""
self.loading_message("Metrics")
self.metrics = Metrics(self.config.metrics.get())
# Update metrics for trainer, validator, tester and predictor
for item in (self.trainer, self.validator, self.tester,
self.predictor):
if item is not None:
item.metrics = self.metrics
Notification(DEEP_NOTIF_INFO,
"%s : Metrics updated" % item.name)
def __init__(self,
dataset: Dataset,
model,
transform_manager,
losses: Losses,
metrics: Union[Metrics, None] = None,
batch_size: int = 32,
num_workers: int = 1,
shuffle: Flag = DEEP_SHUFFLE_NONE,
name: str = "Inferer"):
self.dataset = dataset
self.model = model
self.transform_manager = transform_manager
self.losses = losses
self.metrics = Metrics() if metrics is None else metrics
self.batch_size = batch_size
self.num_workers = num_workers
self.name = name
self.shuffle = get_corresponding_flag(DEEP_LIST_SHUFFLE,
shuffle,
fatal=False,
default=DEEP_SHUFFLE_NONE)
self.dataloader = DataLoader(dataset=self.dataset,
batch_size=self.batch_size,
shuffle=False,
num_workers=self.num_workers)
def load_metrics(self):
"""
AUTHORS:
--------
:author: Alix Leroy
DESCRIPTION:
------------
Load the metrics
PARAMETERS:
-----------
None
RETURN:
-------
:return loss_functions->dict: The metrics
"""
metrics = {}
if self.config.metrics.get():
for key, config in self.config.metrics.get().items():
# Get the expected loss path (for notification purposes)
if config.module is None:
metric_path = "%s : %s from default modules" % (key, config.name)
else:
metric_path = "%s : %s from %s" % (key, config.name, config.module)
# Notify the user which loss is being collected and from where
Notification(DEEP_NOTIF_INFO, DEEP_MSG_METRIC_LOADING % metric_path)
# Get the metric object
metric, module = get_module(
name=config.name,
module=config.module,
browse={**DEEP_MODULE_METRICS, **DEEP_MODULE_LOSSES},
silence=True
)
# If metric is not found by get_module
if metric is None:
Notification(DEEP_NOTIF_FATAL, DEEP_MSG_METRIC_NOT_FOUND % config.name)
# Check if the metric is a class or a stand-alone function
if inspect.isclass(metric):
method = metric(**config.kwargs.get())
else:
method = metric
# Add to the dictionary of metrics and notify of success
metrics[str(key)] = Metric(name=str(key), method=method)
metrics[str(key)] = Metric(name=str(key), method=method)
Notification(DEEP_NOTIF_SUCCESS, DEEP_MSG_METRIC_LOADED % (key, config.name, module))
else:
Notification(DEEP_NOTIF_INFO, DEEP_MSG_METRIC_NONE)
self.metrics = Metrics(metrics)