def __init__(self, args, transform=None, mode='test'):
np.random.seed(args.seed)
super(Hotel_DB, self).__init__()
self.transform = transform
total = True
if 'seen' in mode: # mode == *_seen or *_unseen
mode_tmp = mode
total = False
else:
mode_tmp = mode + '_seen'
total = True
self.datas, self.num_classes, _, self.labels, self.datas_bg = loadDataToMem(args.dataset_path,
args.dataset_name,
args.dataset_split_type,
mode=mode_tmp,
split_file_name=args.splits_file_name,
portion=args.portion)
# if total:
self.all_shuffled_data = get_shuffled_data(self.datas_bg,
seed=args.seed,
one_hot=False,
both_seen_unseen=True,
shuffle=False)
# else: # todo
# self.all_shuffled_data = get_shuffled_data(self.datas, seed=args.seed, one_hot=False)
print(f'hotel {mode} classes: ', self.num_classes)
print(f'hotel {mode} length: ', self.__len__())
def __init__(self, args, transform=None, mode='train'): # train or val
super(CUBClassification, self).__init__()
np.random.seed(args.seed)
self.transform = transform
self.datas, self.num_classes, self.length, self.labels, _ = loadDataToMem(
args.dataset_path,
args.dataset_name,
args.dataset_split_type,
mode=mode)
self.shuffled_data = get_shuffled_data(self.datas, seed=args.seed)
def __init__(self, args, transform=None, mode='test'):
np.random.seed(args.seed)
super(CUBTest_Fewshot, self).__init__()
self.transform = transform
self.times = args.times
self.way = args.way
self.img1 = None
self.c1 = None
self.datas, self.num_classes, _, self.labels, _ = loadDataToMem(
args.dataset_path,
args.dataset_name,
args.dataset_split_type,
mode=mode) # todo not updated
def __init__(self, args, transform=None, mode='train'):
super(CUBTrain_Top, self).__init__()
np.random.seed(args.seed)
# self.dataset = dataset
self.transform = transform
self.datas, self.num_classes, self.length, self.labels, _ = loadDataToMem(
args.dataset_path,
args.dataset_name,
args.dataset_split_type,
mode=mode)
self.shuffled_data = get_shuffled_data(datas=self.datas,
seed=args.seed)
def __init__(self, args, transform=None, mode='test_seen', save_pictures=False):
np.random.seed(args.seed)
super(HotelTest, self).__init__()
self.transform = transform
self.save_pictures = save_pictures
self.times = args.times
self.way = args.way
self.img1 = None
self.c1 = None
self.datas, self.num_classes, _, self.labels, self.datas_bg = loadDataToMem(args.dataset_path,
args.dataset_name,
args.dataset_split_type, mode=mode,
split_file_name=args.splits_file_name,
portion=args.portion)
print(f'hotel {mode} classes: ', self.num_classes)
print(f'hotel {mode} length: ', self.__len__())
def __init__(self, args, transform=None, mode='train', save_pictures=False):
super(HotelTrain, self).__init__()
np.random.seed(args.seed)
self.transform = transform
self.save_pictures = save_pictures
self.class1 = 0
self.image1 = None
self.no_negative = args.no_negative
self.datas, self.num_classes, self.length, self.labels, _ = loadDataToMem(args.dataset_path, args.dataset_name,
args.dataset_split_type,
mode=mode,
split_file_name=args.splits_file_name,
portion=args.portion)
self.shuffled_data = get_shuffled_data(datas=self.datas, seed=args.seed)
print('hotel train classes: ', self.num_classes)
print('hotel train length: ', self.length)