Getting IndexError while Training or Inference

[Aayush2007]

This is the command I use: !python main.py --work-dir results --device 0 --num-worker 4 --batch-size 1

Here is the error that I get:
Traceback (most recent call last):
File "main.py", line 219, in
processor.start()
File "main.py", line 46, in start
self.device, epoch, self.recoder)
File "/home/jupyter/VAC_CSLR/seq_scripts.py", line 21, in seq_train
for batch_idx, data in enumerate(loader):
File "/opt/conda/lib/python3.7/site-packages/torch/utils/data/dataloader.py", line 521, in next
data = self._next_data()
File "/opt/conda/lib/python3.7/site-packages/torch/utils/data/dataloader.py", line 1203, in _next_data
return self._process_data(data)
File "/opt/conda/lib/python3.7/site-packages/torch/utils/data/dataloader.py", line 1229, in _process_data
data.reraise()
File "/opt/conda/lib/python3.7/site-packages/torch/_utils.py", line 434, in reraise
raise exception
IndexError: Caught IndexError in DataLoader worker process 0.
Original Traceback (most recent call last):
File "/opt/conda/lib/python3.7/site-packages/torch/utils/data/_utils/worker.py", line 287, in _worker_loop
data = fetcher.fetch(index)
File "/opt/conda/lib/python3.7/site-packages/torch/utils/data/_utils/fetch.py", line 49, in fetch
data = [self.dataset[idx] for idx in possibly_batched_index]
File "/opt/conda/lib/python3.7/site-packages/torch/utils/data/_utils/fetch.py", line 49, in
data = [self.dataset[idx] for idx in possibly_batched_index]
File "/home/jupyter/VAC_CSLR/dataset/dataloader_video.py", line 47, in getitem
input_data, label = self.normalize(input_data, label)
File "/home/jupyter/VAC_CSLR/dataset/dataloader_video.py", line 78, in normalize
video, label = self.data_aug(video, label, file_id)
File "/home/jupyter/VAC_CSLR/utils/video_augmentation.py", line 24, in call
image = t(image)
File "/home/jupyter/VAC_CSLR/utils/video_augmentation.py", line 119, in call
if isinstance(clip[0], np.ndarray):
IndexError: list index out of range

Can you tell me what am I doing wrong?

[sunsn1997]

hello，How did you solve this problem