---
license: mit
---

# 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. 
The model is translated from [PaddlePaddle/ernie-layoutx-base-uncased](https://huggingface.co/PaddlePaddle/ernie-layoutx-base-uncased) with [tools/convert2torch.py](https://github.com/NormXU/ERNIE-Layout-Pytorch/blob/main/tools/convert2torch.py). It is a script to translate all state dicts of ERNIE-pretrained models for PaddlePaddle into Pytorch style. Feel free to edit it if necessary.

**A Quick Example**
```python
import torch
from PIL import Image
import numpy as np
import torch.nn.functional as F
from networks.model_util import ernie_qa_processing
from networks import ErnieLayoutConfig, ErnieLayoutForQuestionAnswering, ErnieLayoutImageProcessor, \
    ERNIELayoutProcessor, ErnieLayoutTokenizerFast

pretrain_torch_model_or_path = "Norm/ERNIE-Layout-Pytorch"
doc_imag_path = "/path/to/dummy_input.jpeg"

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

# Dummy Input
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
pil_image = Image.open(doc_imag_path).convert("RGB")

# initialize tokenizer
tokenizer = ErnieLayoutTokenizerFast.from_pretrained(pretrained_model_name_or_path=pretrain_torch_model_or_path)

# initialize feature extractor
feature_extractor = ErnieLayoutImageProcessor(apply_ocr=False)
processor = ERNIELayoutProcessor(image_processor=feature_extractor, tokenizer=tokenizer)

# Tokenize context & questions
context_encodings = processor(pil_image, context)
question = "what is it?"
tokenized_res = ernie_qa_processing(tokenizer, question, layout, context_encodings)
tokenized_res['input_ids'] = torch.tensor([tokenized_res['input_ids']]).to(device)
tokenized_res['bbox'] = torch.tensor([tokenized_res['bbox']]).to(device)
tokenized_res['pixel_values'] = torch.tensor(np.array(context_encodings.data['pixel_values'])).to(device)

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

# decode output
start_max = torch.argmax(F.softmax(output.start_logits, dim=-1))
end_max = torch.argmax(F.softmax(output.end_logits, dim=-1)) + 1  # add one ##because of python list indexing
answer = tokenizer.decode(tokenized_res["input_ids"][0][start_max: end_max])
print(answer)


```