AttributeError: 'LayoutLMForTokenClassification' object has no attribute 'config'
Author: blueprintparadise
Pull request: no
Labels: name: bug

### System Info

Adding image embeddings to layoutLM makes the model unconvertable
After following the  - https://colab.research.google.com/github/NielsRogge/Transformers-Tutorials/blob/master/LayoutLM/Add_image_embeddings_to_LayoutLM.ipynb
I wanted to convert the .pt model to onnx. The issue is that the changes made in the notebook do not allow for the model conversion to work.

New model - ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
import torch.nn as nn
from transformers.models.layoutlm import LayoutLMModel, LayoutLMConfig
from transformers.modeling_outputs import TokenClassifierOutput
import torchvision
from torchvision.ops import RoIAlign
class LayoutLMForTokenClassification(nn.Module):
    def __init__(self, output_size=(3,3), 
                 spatial_scale=14/224, 
                 sampling_ratio=2
        ): 
        super().__init__()
        
        # LayoutLM base model + token classifier
        self.num_labels = len(label2idx)
        self.layoutlm = LayoutLMModel.from_pretrained("microsoft/layoutlm-base-uncased", num_labels=self.num_labels)
        self.dropout = nn.Dropout(self.layoutlm.config.hidden_dropout_prob)
        self.classifier = nn.Linear(self.layoutlm.config.hidden_size, self.num_labels)

        # backbone + roi-align + projection layer
        model = torchvision.models.resnet101(pretrained=True)
        self.backbone = nn.Sequential(*(list(model.children())[:-3]))
        self.roi_align = RoIAlign(output_size, spatial_scale=spatial_scale, sampling_ratio=sampling_ratio)
        self.projection = nn.Linear(in_features=1024*3*3, out_features=self.layoutlm.config.hidden_size)

    def forward(
        self,
        input_ids,
        bbox,
        attention_mask,
        token_type_ids,
        position_ids=None,
        head_mask=None,
        inputs_embeds=None,
        labels=None,
        resized_images=None, # shape (N, C, H, W), with H = W = 224
        resized_and_aligned_bounding_boxes=None, # single torch tensor that also contains the batch index for every bbox at image size 224
        output_attentions=None,
        output_hidden_states=None,
        return_dict=None,
    ):
        r"""
        labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
            Labels for computing the token classification loss. Indices should be in ``[0, ..., config.num_labels -
            1]``.

        """
        return_dict = return_dict if return_dict is not None else self.layoutlm.config.use_return_dict

        # first, forward pass on LayoutLM
        outputs = self.layoutlm(
            input_ids=input_ids,
            bbox=bbox,
            attention_mask=attention_mask,
            token_type_ids=token_type_ids,
            position_ids=position_ids,
            head_mask=head_mask,
            inputs_embeds=inputs_embeds,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )

        sequence_output = outputs[0]

        # next, send resized images of shape (batch_size, 3, 224, 224) through backbone to get feature maps of images 
        # shape (batch_size, 1024, 14, 14)
        feature_maps = self.backbone(resized_images)
        
        # next, use roi align to get feature maps of individual (resized and aligned) bounding boxes
        # shape (batch_size*seq_len, 1024, 3, 3)
        device = input_ids.device
        resized_bounding_boxes_list = []
        for i in resized_and_aligned_bounding_boxes:
          resized_bounding_boxes_list.append(i.float().to(device))
                
        feat_maps_bboxes = self.roi_align(input=feature_maps, 
                                        # we pass in a list of tensors
                                        # We have also added -0.5 for the first two coordinates and +0.5 for the last two coordinates,
                                        # see https://stackoverflow.com/questions/60060016/why-does-roi-align-not-seem-to-work-in-pytorch
                                        rois=resized_bounding_boxes_list
                           )  
      
        # next, reshape  + project to same dimension as LayoutLM. 
        batch_size = input_ids.shape[0]
        seq_len = input_ids.shape[1]
        feat_maps_bboxes = feat_maps_bboxes.view(batch_size, seq_len, -1) # Shape (batch_size, seq_len, 1024*3*3)
        projected_feat_maps_bboxes = self.projection(feat_maps_bboxes) # Shape (batch_size, seq_len, hidden_size)

        # add those to the sequence_output - shape (batch_size, seq_len, hidden_size)
        sequence_output += projected_feat_maps_bboxes

        sequence_output = self.dropout(sequence_output)
        logits = self.classifier(sequence_output)

        loss = None
        if labels is not None:
            loss_fct = nn.CrossEntropyLoss()

            if attention_mask is not None:
                active_loss = attention_mask.view(-1) == 1
                active_logits = logits.view(-1, self.num_labels)[active_loss]
                active_labels = labels.view(-1)[active_loss]
                loss = loss_fct(active_logits, active_labels)
            else:
                loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))

        if not return_dict:
            output = (logits,) + outputs[2:]
            return ((loss,) + output) if loss is not None else output

        return TokenClassifierOutput(
            loss=loss,
            logits=logits,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
        )

ERROR ------------------------------------------------------------------------------------------------------------------------------------
---------------------------------------------------------------------------------------------------------------------------------------------
      3 from transformers.onnx import export
      4 def save_onnx(save_path):
      5         onnx_config = LayoutLMOnnxConfig(model.config)
      6         export(preprocessor=tokenizer, model=model.cpu(), config=onnx_config, output=Path(save_path),opset=11)

                 [/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py](https://localhost:8080/#) in __getattr__(self, name)
   1206                 return modules[name]
   1207         raise AttributeError("'{}' object has no attribute '{}'".format(
   1208             type(self).__name__, name))
   1209 
   1210     def __setattr__(self, name: str, value: Union[Tensor, 'Module']) -> None:

AttributeError: 'LayoutLMForTokenClassification' object has no attribute 'config'

Please help.@NielsRogge

### Who can help?

@NielsRogge
@SaulLu

### Information

- [ ] The official example scripts
- [X] My own modified scripts

### Tasks

- [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)
- [X] My own task or dataset (give details below)

### Reproduction

Step 1 . Run this notebook - 
https://colab.research.google.com/github/NielsRogge/Transformers-Tutorials/blob/master/LayoutLM/Add_image_embeddings_to_LayoutLM.ipynb?authuser=4#scrollTo=Vr4sG80hu6rC

Step   2 - Run the model conversion code -
from pathlib import Path
from transformers.models.layoutlm import LayoutLMOnnxConfig
from transformers.onnx import export
def save_onnx(save_path):
        onnx_config = LayoutLMOnnxConfig(model.config)
        export(preprocessor=tokenizer, model=model.cpu(), config=onnx_config, output=Path(save_path),opset=11)

print("Save model as ONNX")
save_onnx('/content/data/model/model.onnx')



I have also tried this method, but the output is blank.-------------------------------------------------------

def save_onnx(save_path):
        configuration = LayoutLMConfig()
        onnx_config = LayoutLMOnnxConfig(configuration)
        export(preprocessor=tokenizer, model=model.cpu(), config=onnx_config, output=Path(save_path),opset=11)

Please let me know if you will need anything else.
        




### Expected behavior

The converted onnx model is produced in the instructed directory

Comments:
Hi,

This question seems better suited for our [forum](https://discuss.huggingface.co/). Would you be able to post your question there?

Thanks!

ok sir