How to build a custom dataset for LayoutLMv2ForSequenceClassification?
## Environment info
- `transformers` version: 4.10.0
- Platform: Linux
- Python version: 3.8.8
- PyTorch version (GPU?): 1.8.0+cu101 (True)
- Tensorflow version (GPU?): 2.2.0 (False)
- Flax version (CPU?/GPU?/TPU?): not installed (NA)
- Jax version: not installed
- JaxLib version: not installed
- Using GPU in script?: no
- Using distributed or parallel set-up in script?: no

### Who can help

Documentation: @sgugger

## Information

Model I am using (Bert, XLNet ...): LayoutLMv2ForSequenceClassification

The problem arises when using:
* [ ] the official example scripts: (give details below)
* [x] my own modified scripts: (give details below)

The tasks I am working on is:
* [ ] an official GLUE/SQUaD task: (give the name)
* [x] my own task or dataset: (give details below)

## To reproduce

I am trying to build a custom dataset to fine tune LayoutLMv2ForSequenceClassification. 

For that I am building a torch.utils.data.Dataset, with the following getitem function:

```python
def __getitem__(self, idx):
        if torch.is_tensor(idx):
            idx = idx.tolist()

        img = Image.open(self.files[idx]).convert('RGB')
        label = self.labels[idx]

        if self.transforms is not None:
            img = self.transforms(img)

        encoding = self.processor(img, return_tensors="pt")
        encoding['input_ids'] = encoding['input_ids'][:,:512]
        encoding['token_type_ids'] = encoding['token_type_ids'][:,:512]
        encoding['attention_mask'] = encoding['attention_mask'][:,:512]
        encoding['bbox'] = encoding['bbox'][:,:512,:4]

        return {
            **encoding,
            "label": label
        }
```

Here is how I defined the processor: 

```python
feature_extractor = LayoutLMv2FeatureExtractor() 
tokenizer = LayoutLMv2TokenizerFast.from_pretrained("microsoft/layoutlmv2-base-uncased")
processor = LayoutLMv2Processor(feature_extractor, tokenizer)
```

The training starts but when it starts loading the data batches it fails.

Output: 
```bash
Traceback (most recent call last):
  File "main.py", line 82, in <module>
    trainer.train()
  File "env_3.8/lib/python3.8/site-packages/transformers/trainer.py", line 1258, in train
    for step, inputs in enumerate(epoch_iterator):
  File "env_3.8/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 517, in __next__
    data = self._next_data()
  File "env_3.8/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 557, in _next_data
    data = self._dataset_fetcher.fetch(index)  # may raise StopIteration
  File "env_3.8/lib/python3.8/site-packages/torch/utils/data/_utils/fetch.py", line 47, in fetch
    return self.collate_fn(data)
  File "env_3.8/lib/python3.8/site-packages/transformers/data/data_collator.py", line 66, in default_data_collator
    return torch_default_data_collator(features)
  File "env_3.8/lib/python3.8/site-packages/transformers/data/data_collator.py", line 105, in torch_default_data_collator
    batch[k] = torch.stack([f[k] for f in features])
RuntimeError: stack expects each tensor to be equal size, but got [1, 20] at entry 0 and [1, 266] at entry 1
```

How can I solve this? Is there any documentation on how to build a simple pytorch dataset that works with huggingface transformers' models? It would be very nice if you had something like [this clear documentation on how to build a dataset for pytorch](https://pytorch.org/tutorials/beginner/data_loading_tutorial.html#dataset-class). I know there is a doc on transformers.datasets but I found it pretty confusing...
Please use the [forums](https://discuss.huggingface.co/) to debug your code as we keep the issues for bugs and feature requests only.

There is not a special Dataset that works better for the Trainer, using a standard PyTorch dataset completely works. The problem here is in your specific dataset, which doesn't have tensors of the same shapes, though can't be collated directly in a batch. Like in pure PyTorch, you either need to change your dataset to have elements of the same shape, or pass along a `data_collator` to the `Trainer` (which is the equivalent of a `collate_fn` for PyTorch DataLoaders) to process a list of samples from your dataset into a batch.<|||||>Hi, 
Sorry for that, I did not know there was a forum. 
I don't see how my inputs can be of different shapes, as they are images, and I convert them to RGB before resizing them using ```self.transforms(img)```, do you have any idea?
From what I understand, it seems that there is some kind of padding that I need to apply on the processor's output, but I just don't know how<|||||>Hi,

I see you are truncating the inputs, but the processor can take care of that for you. Just specify `truncation=True`.

```
def __getitem__(self, idx):
      image = Image.open(self.files[idx]).convert('RGB')
      label = self.labels[idx]

      # processor creates input_ids, attention_mask, token_type_ids, bbox, image
      encoding = self.processor(image, padding="max_length", truncation=True, return_tensors="pt")

      # remove batch dimension (which the processor automatically adds)
      for k,v in encoding.items():
           encoding[k] = v.squeeze()
        
      # add label
      encoding["labels"] = torch.tensor(label)

      return encoding
```

So what happens internally, is that `LayoutLMv2Processor` first uses `LayoutLMv2FeatureExtractor` to apply OCR (namely, Google's Tesseract) on the document image to get a list of words + corresponding boxes (coordinates). The feature extractor also resizes the document image to 224x224. Next, the list of words + boxes are provided to `LayoutLMv2TokenizerFast`, which convert them to token-level `input_ids`, `attention_mask`, `token_type_ids` and `bbox`. Together with the resized image and the label, you have everything you need to train the model.

<|||||>Hi, 
Thank you very much that is what I was looking for. Actually I looked for such parameters in the doc but for some reason I could not find it... 
Thank you a lot for your help. 
I have a new error, however, I don't know if you have any idea of how to solve it? 
```bash
python3.8/site-packages/detectron2/modeling/backbone/resnet.py", line 443, in forward
    assert x.dim() == 4, f"ResNet takes an input of shape (N, C, H, W). Got {x.shape} instead!"
AssertionError: ResNet takes an input of shape (N, C, H, W). Got torch.Size([8, 1, 3, 224, 224]) instead!
```

Note: I have got this error using the above code<|||||>That's because you need to remove the batch dimension which the processor automatically adds. I have updated my code snippet above.<|||||>It works ! Thank you very much for your help !