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| """Image parser. | |
| Contains parsers for image files. | |
| """ | |
| import re | |
| from pathlib import Path | |
| from typing import Dict | |
| from gpt_index.readers.file.base_parser import BaseParser | |
| class ImageParser(BaseParser): | |
| """Image parser. | |
| Extract text from images using DONUT. | |
| """ | |
| def _init_parser(self) -> Dict: | |
| """Init parser.""" | |
| try: | |
| import torch # noqa: F401 | |
| except ImportError: | |
| raise ImportError( | |
| "install pytorch to use the model: " "`pip install torch`" | |
| ) | |
| try: | |
| from transformers import DonutProcessor, VisionEncoderDecoderModel | |
| except ImportError: | |
| raise ImportError( | |
| "transformers is required for using DONUT model: " | |
| "`pip install transformers`" | |
| ) | |
| try: | |
| import sentencepiece # noqa: F401 | |
| except ImportError: | |
| raise ImportError( | |
| "sentencepiece is required for using DONUT model: " | |
| "`pip install sentencepiece`" | |
| ) | |
| try: | |
| from PIL import Image # noqa: F401 | |
| except ImportError: | |
| raise ImportError( | |
| "PIL is required to read image files: " "`pip install Pillow`" | |
| ) | |
| processor = DonutProcessor.from_pretrained( | |
| "naver-clova-ix/donut-base-finetuned-cord-v2" | |
| ) | |
| model = VisionEncoderDecoderModel.from_pretrained( | |
| "naver-clova-ix/donut-base-finetuned-cord-v2" | |
| ) | |
| return {"processor": processor, "model": model} | |
| def parse_file(self, file: Path, errors: str = "ignore") -> str: | |
| """Parse file.""" | |
| import torch | |
| from PIL import Image | |
| model = self.parser_config["model"] | |
| processor = self.parser_config["processor"] | |
| device = "cuda" if torch.cuda.is_available() else "cpu" | |
| model.to(device) | |
| # load document image | |
| image = Image.open(file) | |
| if image.mode != "RGB": | |
| image = image.convert("RGB") | |
| # prepare decoder inputs | |
| task_prompt = "<s_cord-v2>" | |
| decoder_input_ids = processor.tokenizer( | |
| task_prompt, add_special_tokens=False, return_tensors="pt" | |
| ).input_ids | |
| pixel_values = processor(image, return_tensors="pt").pixel_values | |
| outputs = model.generate( | |
| pixel_values.to(device), | |
| decoder_input_ids=decoder_input_ids.to(device), | |
| max_length=model.decoder.config.max_position_embeddings, | |
| early_stopping=True, | |
| pad_token_id=processor.tokenizer.pad_token_id, | |
| eos_token_id=processor.tokenizer.eos_token_id, | |
| use_cache=True, | |
| num_beams=3, | |
| bad_words_ids=[[processor.tokenizer.unk_token_id]], | |
| return_dict_in_generate=True, | |
| ) | |
| sequence = processor.batch_decode(outputs.sequences)[0] | |
| sequence = sequence.replace(processor.tokenizer.eos_token, "").replace( | |
| processor.tokenizer.pad_token, "" | |
| ) | |
| # remove first task start token | |
| sequence = re.sub(r"<.*?>", "", sequence, count=1).strip() | |
| return sequence | |