def __init__(self, args):
super().__init__(args)
self.train_loader, self.valset, self.testset = get_dataloader(args)
init_summary_writer(args.filename)
self.model, self.para_model = prepare_model(args)
# for n, p in self.para_model.named_parameters():
# p.register_hook(save_grad(n))
self.optimizer, self.lr_scheduler = prepare_optimizer(self.model, args)
def __init__(self, args):
super().__init__(args)
self.trainset, self.valset, self.trainvalset, self.testset, self.traintestset, \
self.train_fsl_loader, self.train_gfsl_loader, self.val_fsl_loader, self.val_gfsl_loader, self.test_fsl_loader, self.test_gfsl_loader = get_dataloader(args)
assert (len(self.train_gfsl_loader) == len(self.train_fsl_loader))
if self.val_gfsl_loader is not None:
assert (len(self.val_gfsl_loader) == len(self.val_fsl_loader))
if self.test_gfsl_loader is not None:
assert (len(self.test_gfsl_loader) == len(self.test_fsl_loader))
self.model = prepare_model(args)
self.optimizer, self.lr_scheduler = prepare_optimizer(
self.model, args, len(self.train_gfsl_loader))
self.max_steps = len(self.train_fsl_loader) * args.max_epoch
def __init__(self, args):
super().__init__(args)
self.train_loader, self.val_loader, self.test_loader = get_dataloader(args)
self.model, self.para_model = prepare_model(args)
self.optimizer, self.lr_scheduler = prepare_optimizer(self.model, args)