def get_loader(args):
if args.dataset == 'MiniImageNet':
# Handle MiniImageNet
from model.dataloader.mini_imagenet import MiniImageNet as Dataset
elif args.dataset == 'CUB':
from model.dataloader.cub import CUB as Dataset
elif args.dataset == 'TieredImagenet':
from model.dataloader.tiered_imagenet import tieredImageNet as Dataset
else:
raise ValueError('Non-supported Dataset.')
if args.finetune:
split = 'train_%d' % args.samples_per_class
else:
split = 'train'
trainset = Dataset(split, False, args, augment=args.augment)
train_loader = DataLoader(dataset=trainset,
batch_size=args.batch_size,
shuffle=True,
num_workers=4,
pin_memory=True)
args.num_class = trainset.num_class
valset = Dataset('val', False, args)
args.num_val_class = valset.num_class
val_sampler = CategoriesSampler(valset.label, 200, valset.num_class,
1 + args.query) # test on 16-way 1-shot
val_loader = DataLoader(dataset=valset,
batch_sampler=val_sampler,
num_workers=4,
pin_memory=True)
args.way = valset.num_class
args.shot = 1
return train_loader, val_loader
def get_dataloader(args):
if args.dataset == 'MiniImageNet':
# Handle MiniImageNet
from model.dataloader.mini_imagenet import MiniImageNet as Dataset
args.dropblock_size = 5
elif args.dataset == 'TieredImageNet':
from model.dataloader.tiered_imagenet import tieredImageNet as Dataset
args.dropblock_size = 5
else:
raise ValueError('Non-supported Dataset.')
num_workers = args.num_workers
trainset = Dataset('train', args, augment=False)
args.num_class = trainset.num_class
train_gfsl_loader = None
train_fsl_loader = None
proto_sampler = ClassSampler(trainset.label, 100)
proto_loader = DataLoader(dataset=trainset,
batch_sampler=proto_sampler,
num_workers=num_workers,
pin_memory=True)
valset = Dataset('val', args)
val_sampler = CategoriesSampler(valset.label, 500,
min(args.eval_way, valset.num_class),
args.eval_shot + args.eval_query)
val_fsl_loader = DataLoader(dataset=valset,
batch_sampler=val_sampler,
num_workers=num_workers,
pin_memory=True)
testset = Dataset('test', args)
test_sampler = CategoriesSampler(testset.label, args.num_eval_episodes,
min(args.eval_way, testset.num_class),
args.eval_shot + args.eval_query)
test_fsl_loader = DataLoader(dataset=testset,
batch_sampler=test_sampler,
num_workers=num_workers,
pin_memory=True)
# prepare data loaders for GFSL test
trainvalset = Dataset('aux_val', args)
val_many_shot_sampler = RandomSampler(
trainvalset.label, 500,
min(args.eval_way, valset.num_class) * args.eval_query)
val_gfsl_loader = DataLoader(dataset=trainvalset,
batch_sampler=val_many_shot_sampler,
num_workers=num_workers,
pin_memory=True)
traintestset = Dataset('aux_test', args)
test_many_shot_sampler = RandomSampler(
traintestset.label, args.num_eval_episodes,
min(args.eval_way, testset.num_class) * args.eval_query)
test_gfsl_loader = DataLoader(dataset=traintestset,
batch_sampler=test_many_shot_sampler,
num_workers=num_workers,
pin_memory=True)
return trainset, valset, trainvalset, testset, traintestset, train_fsl_loader, train_gfsl_loader, proto_loader, val_fsl_loader, val_gfsl_loader, test_fsl_loader, test_gfsl_loader
def get_dataloader(args):
if args.dataset == 'MiniImageNet':
# Handle MiniImageNet
from model.dataloader.mini_imagenet import MiniImageNet as Dataset
elif args.dataset == 'CUB':
from model.dataloader.cub import CUB as Dataset
elif args.dataset == 'TieredImageNet':
from model.dataloader.tiered_imagenet import tieredImageNet as Dataset
else:
raise ValueError('Non-supported Dataset.')
num_device = torch.cuda.device_count()
num_episodes = args.episodes_per_epoch*num_device if args.multi_gpu else args.episodes_per_epoch
num_workers=args.num_workers*num_device if args.multi_gpu else args.num_workers
trainset = Dataset('train', args, augment=args.augment)
args.num_class = trainset.num_class
train_sampler = CategoriesSampler(trainset.label,
num_episodes,
max(args.way, args.num_classes),
args.shot + args.query)
train_loader = DataLoader(dataset=trainset,
num_workers=num_workers,
batch_sampler=train_sampler,
pin_memory=True)
#if args.multi_gpu and num_device > 1:
#train_loader = MultiGPUDataloader(train_loader, num_device)
#args.way = args.way * num_device
valset = Dataset('val', args)
val_sampler = CategoriesSampler(valset.label,
args.num_eval_episodes,
args.eval_way, args.eval_shot + args.eval_query)
val_loader = DataLoader(dataset=valset,
batch_sampler=val_sampler,
num_workers=args.num_workers,
pin_memory=True)
testset = Dataset('test', args)
test_sampler = CategoriesSampler(testset.label,
10000, # args.num_eval_episodes,
args.eval_way, args.eval_shot + args.eval_query)
test_loader = DataLoader(dataset=testset,
batch_sampler=test_sampler,
num_workers=args.num_workers,
pin_memory=True)
return train_loader, val_loader, test_loader
args.save_path = osp.join(save_path1, save_path2)
if not osp.exists(save_path1):
os.mkdir(save_path1)
ensure_path(args.save_path)
if args.dataset == 'MiniImageNet':
# Handle MiniImageNet
from model.dataloader.mini_imagenet import MiniImageNet as Dataset
elif args.dataset == 'CUB':
from model.dataloader.cub import CUB as Dataset
elif args.dataset == 'TieredImagenet':
from model.dataloader.tiered_imagenet import tieredImageNet as Dataset
else:
raise ValueError('Non-supported Dataset.')
trainset = Dataset('train', args, augment=True)
train_loader = DataLoader(dataset=trainset,
batch_size=args.batch_size,
shuffle=True,
num_workers=8,
pin_memory=True)
args.num_class = trainset.num_class
valset = Dataset('val', args)
val_sampler = CategoriesSampler(valset.label, 200, valset.num_class,
1 + args.query) # test on 16-way 1-shot
val_loader = DataLoader(dataset=valset,
batch_sampler=val_sampler,
num_workers=8,
pin_memory=True)
args.way = valset.num_class
args.shot = 1