def fit(self,
train_loader,
val_loader=None,
max_epochs=1,
metrics=None,
metrics_on_train=False,
callbacks=None,
val_callbacks=None):
metrics = [] if metrics is None else metrics
assert self.train_ready()
setup_logging()
train_engine = Engine(self.train_step, model=self, logger=self.logger)
train_metrics = [Loss()] + metrics if metrics_on_train else [Loss()]
_attach_metrics(train_engine, train_metrics, name_prefix='train_')
metrics_logging.attach(train_engine, train=True)
if val_loader is not None:
self.validate(val_loader, metrics, val_callbacks)
val_engine = Engine(self.val_step, model=self, logger=self.logger)
_attach_metrics(val_engine, [Loss()] + metrics, name_prefix='val_')
_attach_callbacks(val_engine, val_callbacks)
@on_epoch_complete
def validation_epoch(train_state, val_engine, val_loader):
epoch = train_state.epoch
val_state = val_engine.run(val_loader, epoch, epoch + 1)
train_state.metrics.update(val_state.metrics)
validation_epoch.attach(train_engine, val_engine, val_loader)
metrics_logging.attach(train_engine, train=False)
_attach_callbacks(train_engine, callbacks)
train_engine.run(train_loader, 0, max_epochs)
def validate(self,
val_loader: Optional[Iterable],
metrics: Optional[List[Metric]] = None,
callbacks: Optional[List[Callback]] = None) -> Dict[str, float]:
"""Perform a validation.
Args:
val_loader (Iterable): The validation data loader.
metrics (list of :class:`argus.metrics.Metric`, optional):
List of metrics to evaluate with the data. Defaults to `None`.
callbacks (list of :class:`argus.callbacks.Callback`, optional):
List of callbacks to be attached to the validation process.
Defaults to `None`.
Returns:
dict: The metrics dictionary.
"""
self._check_train_ready()
metrics = [] if metrics is None else metrics
val_engine = Engine(self.val_step, model=self,
logger=self.logger, phase='val')
_attach_metrics(val_engine, [Loss()] + metrics)
_attach_callbacks(val_engine, callbacks)
metrics_logging.attach(val_engine, train=False, print_epoch=False)
return val_engine.run(val_loader).metrics
def fit(self,
train_loader: Iterable,
val_loader: Optional[Iterable] = None,
num_epochs: int = 1,
metrics: Optional[List[Union[Metric, str]]] = None,
metrics_on_train: bool = False,
callbacks: Optional[List[Callback]] = None,
val_callbacks: Optional[List[Callback]] = None):
"""Train the argus model.
The method attaches metrics and callbacks to the train and validation,
and runs the training process.
Args:
train_loader (Iterable): The train data loader.
val_loader (Iterable, optional):
The validation data loader. Defaults to `None`.
num_epochs (int, optional): Number of training epochs to
run. Defaults to 1.
metrics (list of :class:`argus.metrics.Metric`, optional):
List of metrics to evaluate. By default, the metrics are
evaluated on the validation data (if any) only.
Defaults to `None`.
metrics_on_train (bool, optional): Evaluate the metrics on train
data as well. Defaults to False.
callbacks (list of :class:`argus.callbacks.Callback`, optional):
List of callbacks to be attached to the training process.
Defaults to `None`.
val_callbacks (list of :class:`argus.callbacks.Callback`, optional):
List of callbacks to be attached to the validation process.
Defaults to `None`.
"""
self._check_train_ready()
metrics = [] if metrics is None else metrics
train_engine = Engine(self.train_step, model=self,
logger=self.logger, phase='train')
train_metrics = [Loss()] + metrics if metrics_on_train else [Loss()]
_attach_metrics(train_engine, train_metrics)
metrics_logging.attach(train_engine, train=True)
if val_loader is not None:
self.validate(val_loader, metrics, val_callbacks)
val_engine = Engine(self.val_step, model=self,
logger=self.logger, phase='val')
_attach_metrics(val_engine, [Loss()] + metrics)
_attach_callbacks(val_engine, val_callbacks)
@on_epoch_complete
def validation_epoch(train_state, val_engine, val_loader):
epoch = train_state.epoch
val_state = val_engine.run(val_loader, epoch, epoch + 1)
train_state.metrics.update(val_state.metrics)
validation_epoch.attach(train_engine, val_engine, val_loader)
metrics_logging.attach(train_engine, train=False)
_attach_callbacks(train_engine, callbacks)
train_engine.run(train_loader, 0, num_epochs)
def validate(self, val_loader, metrics=None, callbacks=None):
metrics = [] if metrics is None else metrics
assert self.train_ready()
val_engine = Engine(self.val_step, model=self, logger=self.logger)
_attach_metrics(val_engine, [Loss()] + metrics, name_prefix='val_')
_attach_callbacks(val_engine, callbacks)
metrics_logging.attach(val_engine, train=False, print_epoch=False)
return val_engine.run(val_loader).metrics