def load(config, model_parameters):
"""
AUTHORS:
--------
:author: Alix Leroy
:author: Samuel Westlake
DESCRIPTION:
------------
Load the optimizer in memory
PARAMETERS:
-----------
None
RETURN:
-------
:return: None
"""
optimizer = get_module(config=config, modules=DEEP_MODULE_OPTIMIZERS)
kwargs = check_kwargs(config.kwargs)
return optimizer(model_parameters, **kwargs)
def load(config: Namespace):
"""
AUTHORS:
--------
:author: Samuel Westlake
:author: Alix Leroy
DESCRIPTION:
------------
Load the model
PARAMETERS:
-----------
:param config(Namespace): The parameters from the model config file
RETURN:
-------
:return: None
"""
model = get_module(config=config, modules=DEEP_MODULE_MODELS)
kwargs = check_kwargs(config.kwargs)
return model(**kwargs)
def __fill_transform_list(self, config_transforms: Namespace):
"""
AUTHORS:
--------
author: Alix Leroy
DESCRIPTION:
------------
Fill the list of transforms with the corresponding methods and arguments
PARAMETERS:
-----------
:param transforms-> list: A list of transforms
RETURN:
-------
:return: None
"""
list_transforms = []
for key, value in config_transforms.get().items():
list_transforms.append([
key,
get_module(value, modules=DEEP_MODULE_TRANSFORMS),
check_kwargs(get_kwargs(value.get()))
])
return list_transforms
def load_losses(self):
"""
AUTHORS:
--------
:author: Alix Leroy
DESCRIPTION:
------------
Load the losses
PARAMETERS:
-----------
None
RETURN:
-------
:return loss_functions->dict: The losses
"""
losses = {}
for key, value in self.config.losses.get().items():
loss = get_module(config=value, modules=DEEP_MODULE_LOSSES)
kwargs = check_kwargs(get_kwargs(self.config.losses.get(key)))
method = loss(**kwargs)
# Check the weight
if self.config.losses.check("weight", key):
if get_int_or_float(value.weight) not in (DEEP_TYPE_INTEGER,
DEEP_TYPE_FLOAT):
Notification(
DEEP_NOTIF_FATAL,
"The loss function %s doesn't have a correct weight argument"
% key)
else:
Notification(
DEEP_NOTIF_FATAL,
"The loss function %s doesn't have any weight argument" %
key)
# Create the loss
if isinstance(method, torch.nn.Module):
losses[str(key)] = Loss(name=str(key),
weight=float(value.weight),
loss=method)
Notification(
DEEP_NOTIF_SUCCESS,
DEEP_MSG_LOSS_LOADED % (key, value.name, loss.__module__))
else:
Notification(
DEEP_NOTIF_FATAL,
"The loss function %s is not a torch.nn.Module instance" %
key)
self.losses = losses
def load_metrics(self):
"""
AUTHORS:
--------
:author: Alix Leroy
DESCRIPTION:
------------
Load the metrics
PARAMETERS:
-----------
None
RETURN:
-------
:return loss_functions->dict: The metrics
"""
metrics = {}
for key, value in self.config.metrics.get().items():
metric = get_module(config=value, modules=DEEP_MODULE_METRICS)
kwargs = check_kwargs(get_kwargs(self.config.metrics.get(key)))
if inspect.isclass(metric):
method = metric(**kwargs)
else:
method = metric
metrics[str(key)] = Metric(name=str(key), method=method)
Notification(
DEEP_NOTIF_SUCCESS,
DEEP_MSG_METRIC_LOADED % (key, value.name, metric.__module__))
self.metrics = metrics