AssertionError: datasets should not be an empty iterable

[seon1890]

No Transformation module specified
model input parameters 32 100 20 1 512 256 88 25 None VGG BiLSTM CTC
loading pretrained model from saved_models_results/None-VGG-BiLSTM-CTC-Seed1111/best_accuracy.pth

dataset_root: result/evaluation_lmdb/IIIT5k_3000 dataset: /
Traceback (most recent call last):
File "test.py", line 282, in
test(opt)
File "test.py", line 226, in test
benchmark_all_eval(model, criterion, converter, opt)
File "test.py", line 46, in benchmark_all_eval
eval_data, eval_data_log = hierarchical_dataset(root=eval_data_path, opt=opt)
File "/workspace/deep-text-recognition-benchmark/dataset.py", line 124, in hierarchical_dataset
concatenated_dataset = ConcatDataset(dataset_list)
File "/usr/local/lib/python3.8/dist-packages/torch/utils/data/dataset.py", line 225, in init
assert len(self.datasets) > 0, 'datasets should not be an empty iterable' # type: ignore[arg-type]
AssertionError: datasets should not be an empty iterable

I keep getting this error for the evaluation part. I did encounter this error with training, but I was able to fix it once I got the data format correct. I checked the evaluation dataset and it's in the correct data format. I even replaced the training-validation (train.py) with training-evaluation (train.py) just to see if the evaluation dataset would work. It works. But for some reason, I keep getting this error with evaluation dataset against test.py

[KPLinux]

same error here, does anyone know how to correct it?

[Zingo78]

#85 Can solve this problem