img_size,
shots=shots,
shuffle=True,
phase=args.phase,
inter=(args.dataset == "inter"))
metaclass = metadataset.metaclass
elif args.dataset == 'pascal_voc_0712':
if args.phase == 1:
img_set = [('2007', 'trainval'), ('2012', 'trainval')]
else:
img_set = [('2007', 'trainval')]
metadataset = MetaDataset('data/VOCdevkit',
img_set,
metaclass,
img_size,
shots=shots,
shuffle=True,
phase=args.phase)
elif args.dataset == "object3d":
metadataset = MetaDataset3D('/home/xiao/Datasets/ObjectNet3D',
'ObjectNet3D_new.txt',
img_size,
'train',
shots=shots,
shuffle=True,
phase=args.phase)
metaclass = metadataset.metaclass
elif args.dataset == "custom":
else:
# Second phase only use fewshot number of base and novel classes
shots = args.shots
if args.meta_type == 1: # use the first sets of all classes
metaclass = cfg.TRAIN.ALLCLASSES_FIRST
if args.meta_type == 2: # use the second sets of all classes
metaclass = cfg.TRAIN.ALLCLASSES_SECOND
if args.meta_type == 3: # use the third sets of all classes
metaclass = cfg.TRAIN.ALLCLASSES_THIRD
# prepare meta sets for meta training
if args.meta_train:
# construct the input dataset of PRN network
img_size = 224
metadataset = MetaDataset('data/VOCdevkit2007', [('2007', 'trainval')],
metaclass,
img_size,
shots=shots,
shuffle=True)
metaloader = torch.utils.data.DataLoader(metadataset,
batch_size=1,
shuffle=False,
num_workers=0,
pin_memory=True)
imdb, roidb, ratio_list, ratio_index = combined_roidb(args.imdb_name)
# filter roidb for the second phase
if args.phase == 2:
roidb = filter_class_roidb(roidb, args.shots, imdb)
ratio_list, ratio_index = rank_roidb_ratio(roidb)
imdb.set_roidb(roidb)