def main_loop(self, method: Method) -> ContinualResults:
""" Runs a continual learning training loop, wether in RL or CL. """
# TODO: Add ways of restoring state to continue a given run.
if self.wandb and self.wandb.project:
# Init wandb, and then log the setting's options.
self.wandb_run = self.setup_wandb(method)
method.setup_wandb(self.wandb_run)
train_env = self.train_dataloader()
valid_env = self.val_dataloader()
logger.info(f"Starting training")
method.set_training()
self._start_time = time.process_time()
method.fit(
train_env=train_env,
valid_env=valid_env,
)
train_env.close()
valid_env.close()
logger.info(f"Finished Training.")
results: ContinualResults = self.test_loop(method)
if self.monitor_training_performance:
results._online_training_performance = train_env.get_online_performance(
)
logger.info(f"Resulting objective of Test Loop: {results.objective}")
self._end_time = time.process_time()
runtime = self._end_time - self._start_time
results._runtime = runtime
logger.info(f"Finished main loop in {runtime} seconds.")
self.log_results(method, results)
return results
def main_loop(self, method: Method) -> IncrementalResults:
""" Runs an incremental training loop, wether in RL or CL. """
# TODO: Add ways of restoring state to continue a given run?
# For each training task, for each test task, a list of the Metrics obtained
# during testing on that task.
# NOTE: We could also just store a single metric for each test task, but then
# we'd lose the ability to create a plots to show the performance within a test
# task.
# IDEA: We could use a list of IIDResults! (but that might cause some circular
# import issues)
results = self.Results()
if self.monitor_training_performance:
results._online_training_performance = []
# TODO: Fix this up, need to set the '_objective_scaling_factor' to a different
# value depending on the 'dataset' / environment.
results._objective_scaling_factor = self._get_objective_scaling_factor(
)
if self.wandb:
# Init wandb, and then log the setting's options.
self.wandb_run = self.setup_wandb(method)
method.setup_wandb(self.wandb_run)
method.set_training()
self._start_time = time.process_time()
for task_id in range(self.phases):
logger.info(f"Starting training" +
(f" on task {task_id}." if self.nb_tasks > 1 else "."))
self.current_task_id = task_id
self.task_boundary_reached(method, task_id=task_id, training=True)
# Creating the dataloaders ourselves (rather than passing 'self' as
# the datamodule):
task_train_env = self.train_dataloader()
task_valid_env = self.val_dataloader()
method.fit(
train_env=task_train_env,
valid_env=task_valid_env,
)
task_train_env.close()
task_valid_env.close()
if self.monitor_training_performance:
results._online_training_performance.append(
task_train_env.get_online_performance())
logger.info(f"Finished Training on task {task_id}.")
test_metrics: TaskSequenceResults = self.test_loop(method)
# Add a row to the transfer matrix.
results.append(test_metrics)
logger.info(
f"Resulting objective of Test Loop: {test_metrics.objective}")
if wandb.run:
d = add_prefix(test_metrics.to_log_dict(),
prefix="Test",
sep="/")
# d = add_prefix(test_metrics.to_log_dict(), prefix="Test", sep="/")
d["current_task"] = task_id
wandb.log(d)
self._end_time = time.process_time()
runtime = self._end_time - self._start_time
results._runtime = runtime
logger.info(f"Finished main loop in {runtime} seconds.")
self.log_results(method, results)
return results