README.md

---
license: afl-3.0
---

# ERNIE-Layout_Pytorch

[This repo](https://github.com/NormXU/ERNIE-Layout-Pytorch) is an unofficial Pytorch implementation of [ERNIE-Layout](http://arxiv.org/abs/2210.06155) which is originally released through PaddleNLP. 


**A Quick Example**
```python
import torch
from networks.modeling_erine_layout import ErnieLayoutConfig, ErnieLayoutForQuestionAnswering
from networks.feature_extractor import ErnieFeatureExtractor
from networks.tokenizer import ErnieLayoutTokenizer
from networks.model_util import ernie_qa_tokenize, prepare_context_info
from PIL import Image


pretrain_torch_model_or_path = "path/to/pretrained/mode"
doc_imag_path = "path/to/doc/image"

device = torch.device("cuda:0")

# initialize tokenizer
tokenizer = ErnieLayoutTokenizer.from_pretrained(pretrained_model_name_or_path=pretrain_torch_model_or_path)
context = ['This is an example document', 'All ocr boxes are inserted into this list']
layout = [[381, 91, 505, 115], [738, 96, 804, 122]]  # all boxes are resized between 0 - 1000

# initialize feature extractor
feature_extractor = ErnieFeatureExtractor()

# Tokenize context & questions
context_encodings = prepare_context_info(tokenizer, context, layout, add_special_tokens=False)
question = "what is it?"
tokenized_res = ernie_qa_tokenize(tokenizer, question, context_encodings)
tokenized_res['input_ids'] = torch.tensor([tokenized_res['input_ids']]).to(device)
tokenized_res['bbox'] = torch.tensor([tokenized_res['bbox']]).to(device)

# answer start && end index
tokenized_res['start_positions'] = torch.tensor([6]).to(device)
tokenized_res['end_positions'] = torch.tensor([12]).to(device)


# open the image of the document and process image
tokenized_res['pixel_values'] = feature_extractor(Image.open(doc_imag_path).convert("RGB")).unsqueeze(0).to(device)


# initialize config
config = ErnieLayoutConfig.from_pretrained(pretrained_model_name_or_path=pretrain_torch_model_or_path)
config.num_classes = 2 # start and end

# initialize ERNIE for VQA
model = ErnieLayoutForQuestionAnswering.from_pretrained(
    pretrained_model_name_or_path=pretrain_torch_model_or_path,
    config=config,
)
model.to(device)

output = model(**tokenized_res)


```