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97 lines
3.0 KiB
Python
97 lines
3.0 KiB
Python
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"""Image Reader.
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A parser for image files.
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"""
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import re
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from pathlib import Path
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from typing import Dict, Optional, List
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from gpt_index.readers.base import BaseReader
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from gpt_index.readers.schema.base import Document
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class ImageReader(BaseReader):
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"""Image parser.
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Extract text from images using DONUT.
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"""
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def __init__(self) -> None:
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"""Init parser."""
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try:
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import torch # noqa: F401
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except ImportError:
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raise ValueError("install pytorch to use the model")
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try:
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from transformers import DonutProcessor, VisionEncoderDecoderModel
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except ImportError:
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raise ValueError("transformers is required for using DONUT model.")
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try:
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import sentencepiece # noqa: F401
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except ImportError:
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raise ValueError("sentencepiece is required for using DONUT model.")
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try:
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from PIL import Image # noqa: F401
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except ImportError:
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raise ValueError(
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"PIL is required to read image files." "Please run `pip install Pillow`"
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)
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processor = DonutProcessor.from_pretrained(
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"naver-clova-ix/donut-base-finetuned-cord-v2"
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)
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model = VisionEncoderDecoderModel.from_pretrained(
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"naver-clova-ix/donut-base-finetuned-cord-v2"
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)
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self.parser_config = {"processor": processor, "model": model}
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def load_data(
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self, file: Path, extra_info: Optional[Dict] = None
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) -> List[Document]:
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"""Parse file."""
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import torch
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from PIL import Image
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model = self.parser_config["model"]
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processor = self.parser_config["processor"]
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device = "cuda" if torch.cuda.is_available() else "cpu"
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model.to(device)
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# load document image
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image = Image.open(file)
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if image.mode != "RGB":
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image = image.convert("RGB")
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# prepare decoder inputs
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task_prompt = "<s_cord-v2>"
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decoder_input_ids = processor.tokenizer(
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task_prompt, add_special_tokens=False, return_tensors="pt"
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).input_ids
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pixel_values = processor(image, return_tensors="pt").pixel_values
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outputs = model.generate(
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pixel_values.to(device),
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decoder_input_ids=decoder_input_ids.to(device),
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max_length=model.decoder.config.max_position_embeddings,
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early_stopping=True,
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pad_token_id=processor.tokenizer.pad_token_id,
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eos_token_id=processor.tokenizer.eos_token_id,
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use_cache=True,
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num_beams=1,
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bad_words_ids=[[processor.tokenizer.unk_token_id]],
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return_dict_in_generate=True,
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)
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sequence = processor.batch_decode(outputs.sequences)[0]
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sequence = sequence.replace(processor.tokenizer.eos_token, "").replace(
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processor.tokenizer.pad_token, ""
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)
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# remove first task start token
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sequence = re.sub(r"<.*?>", "", sequence, count=1).strip()
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return [Document(sequence, extra_info=extra_info)]
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