def __init__(self,
model_factory,
dataset,
params,
dataset_cfg,
experiment_cfg,
restore_checkpoint,
logger=None):
items = model_factory(params)
self.logger = logger if logger else Logger.get()
self.model = items['model']
# self.optimizer = items['optimizer']
self.criterion = items['criterion']
self.metrics = items['metrics']
# restore model from checkpoint
checkpoint = Checkpoint(
checkpoint_dir=experiment_cfg.experiment_dir(),
filename=experiment_cfg.checkpoint_filename(),
best_checkpoint=experiment_cfg.best_checkpoint(),
latest_checkpoint=experiment_cfg.latest_checkpoint(),
logger=self.logger)
status = checkpoint.restore(self.model, None, restore_checkpoint)
assert status, "Restore model from the checkpoint: {}, failed".format(
restore_checkpoint)
self.dataset = dataset
self.metrics_file = experiment_cfg.metrics_file(
restore_checkpoint, self.dataset.dataset_name)
def __init__(self,
model_factory,
trainloader,
valloader,
params,
dataset_cfg,
experiment_cfg,
best_metric_recorder,
restore_checkpoint=None,
logger=None):
logger = logger if logger else Logger.get()
items = model_factory(params)
self.model = items['model']
self.optimizer = items['optimizer']
self.criterion = items['criterion']
self.metrics = items['metrics']
self.best_metric_recorder = best_metric_recorder
self.trainloader = trainloader
self.valloader = valloader
self.num_epochs = params.num_epochs
self.running_avg_steps = params.running_avg_steps
self.checkpoint = Checkpoint(
checkpoint_dir=experiment_cfg.experiment_dir(),
filename=experiment_cfg.checkpoint_filename(),
best_checkpoint=experiment_cfg.best_checkpoint(),
latest_checkpoint=experiment_cfg.latest_checkpoint(),
logger=logger)
self.best_metrics_file = experiment_cfg.best_metrics_file(
dataset_cfg.val_name())
self.latest_metrics_file = experiment_cfg.latest_metrics_file(
dataset_cfg.val_name())
self.restore_checkpoint = restore_checkpoint
self.logger = logger
