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license: mit |
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# ERNIE-Layout_Pytorch |
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- **Model type:** [ERNIE-Layout](https://arxiv.org/abs/2210.06155) |
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- **Repository:** [source code](https://github.com/NormXU/ERNIE-Layout-Pytorch): an unofficial ERNIE-Layout implementation in Pytorch |
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- **Converted from:** [PaddlePaddle/ernie-layoutx-base-uncased](https://huggingface.co/PaddlePaddle/ernie-layoutx-base-uncased) |
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The ERNIE-Layout-Pytorch model is initially released by [PaddleNLP](https://github.com/PaddlePaddle/PaddleNLP). To make Pytorch users easy to use, the model has been converted into PyTorch format with the [tools/convert2torch.py](https://github.com/NormXU/ERNIE-Layout-Pytorch/blob/main/tools/convert2torch.py) script. |
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Please feel free to make any changes you need. For more details and use cases, please check the repo. |
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**A Quick Example** |
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```python |
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import torch |
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from PIL import Image |
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import torch.nn.functional as F |
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from networks import ErnieLayoutConfig, ErnieLayoutForQuestionAnswering, \ |
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ErnieLayoutProcessor, ErnieLayoutTokenizerFast |
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from transformers.models.layoutlmv3 import LayoutLMv3ImageProcessor |
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pretrain_torch_model_or_path = "Norm/ERNIE-Layout-Pytorch" |
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doc_imag_path = "./dummy_input.jpeg" |
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context = ['This is an example sequence', 'All ocr boxes are inserted into this list'] |
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layout = [[381, 91, 505, 115], [738, 96, 804, 122]] # make sure all boxes are normalized between 0 - 1000 |
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pil_image = Image.open(doc_imag_path).convert("RGB") |
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# initialize tokenizer |
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tokenizer = ErnieLayoutTokenizerFast.from_pretrained(pretrained_model_name_or_path=pretrain_torch_model_or_path) |
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# initialize feature extractor |
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feature_extractor = LayoutLMv3ImageProcessor(apply_ocr=False) |
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processor = ErnieLayoutProcessor(image_processor=feature_extractor, tokenizer=tokenizer) |
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# Tokenize context & questions |
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question = "what is it?" |
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encoding = processor(pil_image, question, context, boxes=layout, return_tensors="pt") |
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# dummy answer start && end index |
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start_positions = torch.tensor([6]) |
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end_positions = torch.tensor([12]) |
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# initialize config |
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config = ErnieLayoutConfig.from_pretrained(pretrained_model_name_or_path=pretrain_torch_model_or_path) |
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config.num_classes = 2 # start and end |
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# initialize ERNIE for VQA |
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model = ErnieLayoutForQuestionAnswering.from_pretrained( |
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pretrained_model_name_or_path=pretrain_torch_model_or_path, |
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config=config, |
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) |
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output = model(**encoding, start_positions=start_positions, end_positions=end_positions) |
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# decode output |
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start_max = torch.argmax(F.softmax(output.start_logits, dim=-1)) |
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end_max = torch.argmax(F.softmax(output.end_logits, dim=-1)) + 1 # add one ##because of python list indexing |
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answer = tokenizer.decode(encoding.input_ids[0][start_max: end_max]) |
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print(answer) |
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``` |
