def validate(
self,
val_loader: Iterable,
metrics: Optional[List[Union[Metric, str]]] = 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` or str, 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
phase_states = dict()
val_engine = Engine(self.val_step, phase_states=phase_states)
attach_metrics(val_engine, [Loss()] + metrics)
default_logging.attach(val_engine)
attach_callbacks(val_engine, callbacks)
state = val_engine.run(val_loader, -1, 0)
return state.metrics
def fit(self,
train_loader: Iterable,
val_loader: Optional[Iterable] = None,
num_epochs: int = 1,
metrics: 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
process, and performs training itself.
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` or str, 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
phase_states = dict()
train_engine = Engine(self.train_step, phase_states=phase_states)
train_metrics = [Loss()] + metrics if metrics_on_train else [Loss()]
attach_metrics(train_engine, train_metrics)
default_logging.attach(train_engine)
if val_loader is not None:
val_engine = Engine(self.val_step, phase_states=phase_states)
attach_metrics(val_engine, [Loss()] + metrics)
default_logging.attach(val_engine)
attach_callbacks(val_engine, val_callbacks)
@argus.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)
val_engine.run(val_loader, -1, 0)
attach_callbacks(train_engine, callbacks)
train_engine.run(train_loader, 0, num_epochs)
def test_on_decorators(self, step_storage, linear_argus_model_instance):
@argus.callbacks.on_start
def on_start_function(state):
state.call_count = 1
state.on_start_flag = True
@argus.callbacks.on_complete
def on_complete_function(state):
state.call_count += 1
state.on_complete_flag = True
@argus.callbacks.on_epoch_start
def on_epoch_start_function(state):
state.call_count += 1
state.on_epoch_start_flag = True
@argus.callbacks.on_epoch_complete
def on_epoch_complete_function(state):
state.call_count += 1
state.on_epoch_complete_flag = True
@argus.callbacks.on_iteration_start
def on_iteration_start_function(state):
state.call_count += 1
state.on_iteration_start_flag = True
@argus.callbacks.on_iteration_complete
def on_iteration_complete_function(state):
state.call_count += 1
state.on_iteration_complete_flag = True
@argus.callbacks.on_catch_exception
def on_catch_exception_function(state):
state.call_count += 1
state.on_catch_exception_flag = True
engine = Engine(step_storage.step_method,
model=linear_argus_model_instance)
attach_callbacks(engine, [
on_start_function, on_complete_function, on_epoch_start_function,
on_epoch_complete_function, on_iteration_start_function,
on_iteration_complete_function, on_catch_exception_function
])
data_loader = [4, 8, 15, 16, 23, 42]
state = engine.run(data_loader, start_epoch=0, end_epoch=3)
assert state.call_count == len(data_loader) * 3 * 2 + 3 * 2 + 2
assert state.on_start_flag
assert state.on_complete_flag
assert state.on_epoch_start_flag
assert state.on_epoch_complete_flag
assert state.on_iteration_start_flag
assert state.on_iteration_complete_flag
assert not hasattr(state, 'on_catch_exception_flag')
class CustomException(Exception):
pass
@argus.callbacks.on_start
def on_start_raise_exception(state):
raise CustomException
on_start_raise_exception.attach(engine)
with pytest.raises(CustomException):
engine.run(data_loader, start_epoch=0, end_epoch=3)
assert engine.state.on_catch_exception_flag
def test_attach_not_a_callback(self, test_engine):
with pytest.raises(TypeError):
attach_callbacks(test_engine, [None])
with pytest.raises(TypeError):
attach_callbacks(test_engine, [test_engine])
