args = parser.parse_args()
pprint(vars(args))
set_gpu(args.gpu)
save_path1 = '-'.join([args.dataset, args.model_type, 'ProtoNet'])
save_path2 = '_'.join([
str(args.shot),
str(args.query),
str(args.way),
str(args.step_size),
str(args.gamma),
str(args.lr),
str(args.temperature)
])
args.save_path = save_path1 + '_' + save_path2
ensure_path(args.save_path)
if args.dataset == 'MiniImageNet':
# Handle MiniImageNet
from feat.dataloader.mini_imagenet import MiniImageNet as Dataset
elif args.dataset == 'CUB':
from feat.dataloader.cub import CUB as Dataset
else:
raise ValueError('Non-supported Dataset.')
trainset = Dataset('train', args)
train_sampler = CategoriesSampler(trainset.label, 100, args.way,
args.shot + args.query)
train_loader = DataLoader(dataset=trainset,
batch_sampler=train_sampler,
num_workers=8,
args = parser.parse_args()
pprint(vars(args))
set_gpu(args.gpu)
save_path1 = '-'.join([args.dataset, args.model_type, 'ProtoNet'])
save_path2 = '_'.join([
str(args.shot),
str(args.query),
str(args.way),
str(args.step_size),
str(args.gamma),
str(args.lr),
str(args.temperature)
])
args.save_path = osp.join(args.save_path, osp.join(save_path1, save_path2))
ensure_path(save_path1, remove=False)
ensure_path(args.save_path)
if args.dataset == 'MiniImageNet':
# Handle MiniImageNet
from feat.dataloader.mini_imagenet import MiniImageNet as Dataset
elif args.dataset == 'CUB':
from feat.dataloader.cub import CUB as Dataset
elif args.dataset == 'TieredImageNet':
from feat.dataloader.tiered_imagenet import tieredImageNet as Dataset
else:
raise ValueError('Non-supported Dataset.')
trainset = Dataset('train', args)
train_sampler = CategoriesSampler(trainset.label, 5, args.way,
args.shot + args.query)