someting wrong when create exemplar_set?

[alexHxun]

==> Current Class: [50, 51, 52, 53, 54, 55, 56, 57, 58, 59]
==> Building model..
in_features: 512 out_features: 50
current net output dim: 60
old net output dim: 50
[50, 51, 52, 53, 54, 55, 56, 57, 58, 59]
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59]
Constructing exemplar set for class-50...
exemplar set shape: 33
Done
Constructing exemplar set for class-51...
exemplar set shape: 33
Done
Constructing exemplar set for class-52...
exemplar set shape: 33
Done
Constructing exemplar set for class-53...
exemplar set shape: 33
Done
Constructing exemplar set for class-54...
exemplar set shape: 33
Done
Constructing exemplar set for class-55...
exemplar set shape: 33
Done
Constructing exemplar set for class-56...
exemplar set shape: 33
Done
Constructing exemplar set for class-57...
exemplar set shape: 33
Done
Constructing exemplar set for class-58...
exemplar set shape: 33
Done
Constructing exemplar set for class-59...
exemplar set shape: 33
Done
start self-distillation for original model.....
setting optimizer and scheduler.................
Traceback (most recent call last):
File "main_imagenet.py", line 472, in
train(model=net, old_model=old_net, epoch=args.epochs, optimizer=optimizer, scheduler=scheduler, lamda=args.lamda, train_loader=trainLoader, use_sd=False, checkPoint=50)
File "main_imagenet.py", line 151, in train
exemplar_set = ExemplarDataset(exemplar_sets, transform=transform_ori)
File "/home/ubuntu/Desktop/Alex/IL/essentials_for_CIL/data/data_loader_imagenet.py", line 17, in init
self.data = np.concatenate(data, axis=0)
File "<array_function internals>", line 6, in concatenate
ValueError: all the input arrays must have same number of dimensions, but the array at index 0 has 4 dimension(s) and the array at index 1 has 1 dimension(s)

[alexHxun]

The reason is that the picture size is inconsistent