# AttributeError: LayoutLMTokenClassification object has no attribute 'config' **URL:** \ **Category:** Intermediate\ **Created:** [August 10, 2022, 10:49am UTC](https://discuss.huggingface.co/t/attributeerror-layoutlmtokenclassification-object-has-no-attribute-config/21473 "2022-08-10T10:49:05Z")\ **Posts on this page:** 4\ **Page:** 1
**Author:** ![Rushikesh](https://avatars.discourse-cdn.com/v4/letter/r/6f9a4e/32.png) [@Rushikesh](https://discuss.huggingface.co/u/Rushikesh)\ **Post date:** [August 10, 2022, 10:49am UTC](https://discuss.huggingface.co/t/attributeerror-layoutlmtokenclassification-object-has-no-attribute-config/21473/1 "2022-08-10T10:49:05Z")
Adding image embeddings to layoutLM makes the model unconvertable After following the - [Google Colab](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 ------------------------------------ ```auto 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 ----- ```auto 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) 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' ``` # REPRODUCTION ### 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](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 - ```auto 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.– ```auto 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 ---
**Author:** ![nielsr](https://sea2.discourse-cdn.com/hellohellohello/user_avatar/discuss.huggingface.co/nielsr/32/39617_2.png) [@nielsr](https://discuss.huggingface.co/u/nielsr)\ **Post date:** [August 10, 2022, 11:43am UTC](https://discuss.huggingface.co/t/attributeerror-layoutlmtokenclassification-object-has-no-attribute-config/21473/2 "2022-08-10T11:43:48Z")
Could you please format your question, to make it easily readable. e.g. you can use ``` to format your code. ---
**Author:** ![Rushikesh](https://avatars.discourse-cdn.com/v4/letter/r/6f9a4e/32.png) [@Rushikesh](https://discuss.huggingface.co/u/Rushikesh)\ **Post date:** [August 10, 2022, 1:02pm UTC](https://discuss.huggingface.co/t/attributeerror-layoutlmtokenclassification-object-has-no-attribute-config/21473/3 "2022-08-10T13:02:59Z")
Will this be okay? ---
**Author:** ![Rushikesh](https://avatars.discourse-cdn.com/v4/letter/r/6f9a4e/32.png) [@Rushikesh](https://discuss.huggingface.co/u/Rushikesh)\ **Post date:** [August 13, 2022, 7:07am UTC](https://discuss.huggingface.co/t/attributeerror-layoutlmtokenclassification-object-has-no-attribute-config/21473/4 "2022-08-13T07:07:14Z")
Even if you cannot provide the answer, if someone could just point me in the correct direction, it will be much appreciated.