set_gpu(args.gpu)
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.')
model = FEAT(args, dropout=0.5)
if torch.cuda.is_available():
torch.backends.cudnn.benchmark = True
model = model.cuda()
test_set = Dataset('test', args)
sampler = CategoriesSampler(test_set.label, 10000, args.way,
args.shot + args.query)
loader = DataLoader(test_set,
batch_sampler=sampler,
num_workers=8,
pin_memory=True)
test_acc_record = np.zeros((10000, ))
model.load_state_dict(torch.load(args.model_path)['params'])
model.eval()
ave_acc = Averager()
label = torch.arange(args.way).repeat(args.query)
if torch.cuda.is_available():
label = label.type(torch.cuda.LongTensor)
append=args.load_train_checkpoint)
pprint(vars(args))
set_gpu(args.gpu)
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, 100, args.way,
args.shot + args.query)
train_loader = DataLoader(dataset=trainset,
batch_sampler=train_sampler,
num_workers=8,
pin_memory=True)
valset = Dataset('val', args)
val_sampler = CategoriesSampler(valset.label, 500, args.way,
args.shot + args.query)
val_loader = DataLoader(dataset=valset,
batch_sampler=val_sampler,
num_workers=8,
pin_memory=True)
])
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)
train_loader = DataLoader(dataset=trainset,
batch_sampler=train_sampler,
num_workers=8,
pin_memory=True)
print("training data loading is done")
valset = Dataset('val', args)
val_sampler = CategoriesSampler(valset.label, 10, args.way,
args.shot + args.query)
val_loader = DataLoader(dataset=valset,
batch_sampler=val_sampler,
num_workers=8,
pin_memory=True)