phoenix T, training is giving issue : I use single GPU. getting the following index out of range error

[mohankrishna12]

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[ Sat Feb 22 13:32:49 2025 ] Parameters:
{'work_dir': './work_dir/baseline/', 'config': './configs/baseline.yaml', 'random_fix': True, 'device': '0', 'phase': 'train', 'save_interval': 5, 'random_seed': 0, 'eval_interval': 1, 'print_log': True, 'log_interval': 10000, 'evaluate_tool': 'sclite', 'feeder': 'dataset.dataloader_video.BaseFeeder', 'dataset': 'phoenix2014-T', 'dataset_info': {'dataset_root': './dataset/phoenix2014-T', 'dict_path': './preprocess/phoenix2014-T/gloss_dict.npy', 'evaluation_dir': './evaluation/slr_eval', 'evaluation_prefix': 'phoenix2014-T-groundtruth'}, 'num_worker': 10, 'feeder_args': {'mode': 'test', 'datatype': 'video', 'num_gloss': -1, 'drop_ratio': 1.0, 'frame_interval': 1, 'image_scale': 1.0, 'input_size': 224, 'prefix': './dataset/phoenix2014-T', 'transform_mode': False}, 'model': 'slr_network.SLRModel', 'model_args': {'num_classes': 1116, 'c2d_type': 'resnet18', 'conv_type': 2, 'use_bn': 1, 'share_classifier': True, 'weight_norm': True}, 'load_weights': None, 'load_checkpoints': None, 'decode_mode': 'beam', 'ignore_weights': [], 'batch_size': 2, 'test_batch_size': 2, 'loss_weights': {'SeqCTC': 1.0, 'ConvCTC': 1.0, 'Dist': 25.0, 'Cu': 0.0005, 'Cp': 0.0005}, 'optimizer_args': {'optimizer': 'Adam', 'base_lr': 0.0001, 'step': [40, 60], 'learning_ratio': 1, 'weight_decay': 0.0001, 'start_epoch': 0, 'nesterov': False}, 'num_epoch': 80}

0%| | 0/3548 [00:00<?, ?it/s]
Traceback (most recent call last):
File "/media/mohan/mohan/CorrNet_Plus/CorrNet_Plus_CSLR/main.py", line 301, in
processor.start()
File "/media/mohan/mohan/CorrNet_Plus/CorrNet_Plus_CSLR/main.py", line 67, in start
seq_train(self.data_loader['train'], self.model, self.optimizer,
File "/media/mohan/mohan/CorrNet_Plus/CorrNet_Plus_CSLR/seq_scripts.py", line 21, in seq_train
for batch_idx, data in enumerate(tqdm(loader)):
File "/media/mohan/mohan/anaconda3/envs/CorrNet_Plus/lib/python3.12/site-packages/tqdm/std.py", line 1181, in iter
for obj in iterable:
File "/media/mohan/mohan/anaconda3/envs/CorrNet_Plus/lib/python3.12/site-packages/torch/utils/data/dataloader.py", line 708, in next
data = self._next_data()
^^^^^^^^^^^^^^^^^
File "/media/mohan/mohan/anaconda3/envs/CorrNet_Plus/lib/python3.12/site-packages/torch/utils/data/dataloader.py", line 1480, in _next_data
return self._process_data(data)
^^^^^^^^^^^^^^^^^^^^^^^^
File "/media/mohan/mohan/anaconda3/envs/CorrNet_Plus/lib/python3.12/site-packages/torch/utils/data/dataloader.py", line 1505, in _process_data
data.reraise()
File "/media/mohan/mohan/anaconda3/envs/CorrNet_Plus/lib/python3.12/site-packages/torch/_utils.py", line 733, in reraise
raise exception
IndexError: Caught IndexError in DataLoader worker process 0.
Original Traceback (most recent call last):
File "/media/mohan/mohan/anaconda3/envs/CorrNet_Plus/lib/python3.12/site-packages/torch/utils/data/_utils/worker.py", line 349, in _worker_loop
data = fetcher.fetch(index) # type: ignore[possibly-undefined]
^^^^^^^^^^^^^^^^^^^^
File "/media/mohan/mohan/anaconda3/envs/CorrNet_Plus/lib/python3.12/site-packages/torch/utils/data/_utils/fetch.py", line 52, in fetch
data = [self.dataset[idx] for idx in possibly_batched_index]

File "/media/mohan/mohan/CorrNet_Plus/CorrNet_Plus_CSLR/dataset/dataloader_video.py", line 53, in getitem
input_data, label = self.normalize(input_data, label)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/media/mohan/mohan/CorrNet_Plus/CorrNet_Plus_CSLR/dataset/dataloader_video.py", line 90, in normalize
video, label = self.data_aug(video, label, file_id)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/media/mohan/mohan/CorrNet_Plus/CorrNet_Plus_CSLR/utils/video_augmentation.py", line 24, in call
image = t(image)
^^^^^^^^
File "/media/mohan/mohan/CorrNet_Plus/CorrNet_Plus_CSLR/utils/video_augmentation.py", line 120, in call
if isinstance(clip[0], np.ndarray):
~~~~^^^
IndexError: list index out of range

[hulianyuyy]

If you get an error like IndexError: list index out of range on the PHOENIX2014-T dataset, you may first check if you have correctly linked the dataset path to the real path, or refer to this issue to tackle the problem.