The following error occurs when using the DEVISIGN dataset for training

[lanlanguai]

In order to test the usability of the code, we used a small number of samples (about 20) for testing, and the following error occurred during the operation. Please provide a solution, thank you very much.

---------------------------------------------------------------------------
IndexError                                Traceback (most recent call last)
Input In [3], in <cell line: 5>()
      3 model = ClassificationModel(cfg=cfg, trainer=trainer)
      4 model.init_from_checkpoint_if_available()
----> 5 model.fit()

File ~/OpenHands/openhands/apis/classification_model.py:108, in ClassificationModel.fit(self)
    104 def fit(self):
    105     """
    106     Method to be called to start the training.
    107     """
--> 108     self.trainer.fit(self, self.datamodule)

File ~/miniconda3/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py:552, in Trainer.fit(self, model, train_dataloaders, val_dataloaders, datamodule, train_dataloader)
    546 self.data_connector.attach_data(
    547     model, train_dataloaders=train_dataloaders, val_dataloaders=val_dataloaders, datamodule=datamodule
    548 )
    550 self.checkpoint_connector.resume_start()
--> 552 self._run(model)
    554 assert self.state.stopped
    555 self.training = False

File ~/miniconda3/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py:917, in Trainer._run(self, model)
    914 self.checkpoint_connector.restore_training_state()
    916 # dispatch `start_training` or `start_evaluating` or `start_predicting`
--> 917 self._dispatch()
    919 # plugin will finalized fitting (e.g. ddp_spawn will load trained model)
    920 self._post_dispatch()

File ~/miniconda3/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py:985, in Trainer._dispatch(self)
    983     self.accelerator.start_predicting(self)
    984 else:
--> 985     self.accelerator.start_training(self)

File ~/miniconda3/lib/python3.8/site-packages/pytorch_lightning/accelerators/accelerator.py:92, in Accelerator.start_training(self, trainer)
     91 def start_training(self, trainer: "pl.Trainer") -> None:
---> 92     self.training_type_plugin.start_training(trainer)

File ~/miniconda3/lib/python3.8/site-packages/pytorch_lightning/plugins/training_type/training_type_plugin.py:161, in TrainingTypePlugin.start_training(self, trainer)
    159 def start_training(self, trainer: "pl.Trainer") -> None:
    160     # double dispatch to initiate the training loop
--> 161     self._results = trainer.run_stage()

File ~/miniconda3/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py:995, in Trainer.run_stage(self)
    993 if self.predicting:
    994     return self._run_predict()
--> 995 return self._run_train()

File ~/miniconda3/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py:1030, in Trainer._run_train(self)
   1027 if not self.is_global_zero and self.progress_bar_callback is not None:
   1028     self.progress_bar_callback.disable()
-> 1030 self._run_sanity_check(self.lightning_module)
   1032 # enable train mode
   1033 self.model.train()

File ~/miniconda3/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py:1114, in Trainer._run_sanity_check(self, ref_model)
   1112 # run eval step
   1113 with torch.no_grad():
-> 1114     self._evaluation_loop.run()
   1116 self.on_sanity_check_end()
   1118 # reset validation metrics

File ~/miniconda3/lib/python3.8/site-packages/pytorch_lightning/loops/base.py:111, in Loop.run(self, *args, **kwargs)
    109 try:
    110     self.on_advance_start(*args, **kwargs)
--> 111     self.advance(*args, **kwargs)
    112     self.on_advance_end()
    113     self.iteration_count += 1

File ~/miniconda3/lib/python3.8/site-packages/pytorch_lightning/loops/dataloader/evaluation_loop.py:109, in EvaluationLoop.advance(self, *args, **kwargs)
    106 dataloader_iter = enumerate(dataloader)
    107 dl_max_batches = self._max_batches[self.current_dataloader_idx]
--> 109 dl_outputs = self.epoch_loop.run(
    110     dataloader_iter, self.current_dataloader_idx, dl_max_batches, self.num_dataloaders
    111 )
    113 # store batch level output per dataloader
    114 self.outputs.append(dl_outputs)

File ~/miniconda3/lib/python3.8/site-packages/pytorch_lightning/loops/base.py:111, in Loop.run(self, *args, **kwargs)
    109 try:
    110     self.on_advance_start(*args, **kwargs)
--> 111     self.advance(*args, **kwargs)
    112     self.on_advance_end()
    113     self.iteration_count += 1

File ~/miniconda3/lib/python3.8/site-packages/pytorch_lightning/loops/epoch/evaluation_epoch_loop.py:111, in EvaluationEpochLoop.advance(self, dataloader_iter, dataloader_idx, dl_max_batches, num_dataloaders)
    109 # lightning module methods
    110 with self.trainer.profiler.profile("evaluation_step_and_end"):
--> 111     output = self.evaluation_step(batch, batch_idx, dataloader_idx)
    112     output = self.evaluation_step_end(output)
    114 self.batch_progress.increment_processed()

File ~/miniconda3/lib/python3.8/site-packages/pytorch_lightning/loops/epoch/evaluation_epoch_loop.py:158, in EvaluationEpochLoop.evaluation_step(self, batch, batch_idx, dataloader_idx)
    156     self.trainer.lightning_module._current_fx_name = "validation_step"
    157     with self.trainer.profiler.profile("validation_step"):
--> 158         output = self.trainer.accelerator.validation_step(step_kwargs)
    160 return output

File ~/miniconda3/lib/python3.8/site-packages/pytorch_lightning/accelerators/accelerator.py:211, in Accelerator.validation_step(self, step_kwargs)
    199 """The actual validation step.
    200 
    201 Args:
   (...)
    208           (only if multiple val dataloaders used)
    209 """
    210 with self.precision_plugin.val_step_context(), self.training_type_plugin.val_step_context():
--> 211     return self.training_type_plugin.validation_step(*step_kwargs.values())

File ~/miniconda3/lib/python3.8/site-packages/pytorch_lightning/plugins/training_type/training_type_plugin.py:178, in TrainingTypePlugin.validation_step(self, *args, **kwargs)
    177 def validation_step(self, *args, **kwargs):
--> 178     return self.model.validation_step(*args, **kwargs)

File ~/OpenHands/openhands/apis/classification_model.py:43, in ClassificationModel.validation_step(self, batch, batch_idx)
     38 """
     39 Lightning calls this inside the training loop with the data from the validation dataloader
     40 passed in as `batch` and calculates the loss and the accuracy.
     41 """
     42 y_hat = self.model(batch["frames"])
---> 43 loss = self.loss(y_hat, batch["labels"])
     44 preds = F.softmax(y_hat, dim=-1)
     45 acc_top1 = self.accuracy_metric(preds, batch["labels"])

File ~/miniconda3/lib/python3.8/site-packages/torch/nn/modules/module.py:889, in Module._call_impl(self, *input, **kwargs)
    887     result = self._slow_forward(*input, **kwargs)
    888 else:
--> 889     result = self.forward(*input, **kwargs)
    890 for hook in itertools.chain(
    891         _global_forward_hooks.values(),
    892         self._forward_hooks.values()):
    893     hook_result = hook(self, input, result)

File ~/miniconda3/lib/python3.8/site-packages/torch/nn/modules/loss.py:1047, in CrossEntropyLoss.forward(self, input, target)
   1045 def forward(self, input: Tensor, target: Tensor) -> Tensor:
   1046     assert self.weight is None or isinstance(self.weight, Tensor)
-> 1047     return F.cross_entropy(input, target, weight=self.weight,
   1048                            ignore_index=self.ignore_index, reduction=self.reduction)

File ~/miniconda3/lib/python3.8/site-packages/torch/nn/functional.py:2693, in cross_entropy(input, target, weight, size_average, ignore_index, reduce, reduction)
   2691 if size_average is not None or reduce is not None:
   2692     reduction = _Reduction.legacy_get_string(size_average, reduce)
-> 2693 return nll_loss(log_softmax(input, 1), target, weight, None, ignore_index, None, reduction)

File ~/miniconda3/lib/python3.8/site-packages/torch/nn/functional.py:2388, in nll_loss(input, target, weight, size_average, ignore_index, reduce, reduction)
   2384     raise ValueError(
   2385         "Expected input batch_size ({}) to match target batch_size ({}).".format(input.size(0), target.size(0))
   2386     )
   2387 if dim == 2:
-> 2388     ret = torch._C._nn.nll_loss(input, target, weight, _Reduction.get_enum(reduction), ignore_index)
   2389 elif dim == 4:
   2390     ret = torch._C._nn.nll_loss2d(input, target, weight, _Reduction.get_enum(reduction), ignore_index)

IndexError: Target 11 is out of bounds.

Looking forward to your reply, best wishes！