mirror of
https://github.com/allenai/olmocr.git
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87 lines
2.4 KiB
Python
87 lines
2.4 KiB
Python
import os
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import json
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import base64
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import logging
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import time
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from io import BytesIO
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from PIL import Image
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from functools import partial
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from logging import Logger
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from pathlib import Path
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from tempfile import TemporaryDirectory
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from typing import Optional
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from tqdm import tqdm
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import accelerate
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import torch
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import torch.distributed
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from datasets.utils import disable_progress_bars
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from datasets.utils.logging import set_verbosity
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from peft import LoraConfig, get_peft_model # pyright: ignore
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from transformers import (
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AutoModelForCausalLM,
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Trainer,
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TrainerCallback,
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TrainingArguments,
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Qwen2VLForConditionalGeneration,
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AutoProcessor,
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Qwen2VLConfig
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)
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from pdelfin.data.renderpdf import render_pdf_to_base64png
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from pdelfin.prompts.anchor import get_anchor_text
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from pdelfin.prompts.prompts import build_finetuning_prompt
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from pdelfin.train.dataprep import prepare_data_for_qwen2_inference
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def build_page_query(local_pdf_path: str, page: int) -> dict:
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image_base64 = render_pdf_to_base64png(local_pdf_path, page, 1024)
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anchor_text = get_anchor_text(local_pdf_path, page, pdf_engine="pdfreport")
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return {
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"input_prompt_text": build_finetuning_prompt(anchor_text),
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"input_prompt_image_base64": image_base64
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}
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@torch.no_grad()
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def run_inference(model_name: str):
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config = Qwen2VLConfig.from_pretrained(model_name)
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processor = AutoProcessor.from_pretrained(model_name)
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# If it doesn't load, change the type:mrope key to "default"
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model = Qwen2VLForConditionalGeneration.from_pretrained(model_name, device_map="auto", config=config)
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model.eval()
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query = build_page_query(os.path.join(os.path.dirname(__file__), "..", "..", "tests", "gnarly_pdfs", "overrun_on_pg8.pdf"), 8)
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inputs = prepare_data_for_qwen2_inference(query, processor)
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print(inputs)
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inputs = {
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x: torch.from_numpy(y).unsqueeze(0).to("cuda")
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for (x,y) in inputs.items()
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}
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output_ids = model.generate(**inputs, temperature=0.8, do_sample=True, max_new_tokens=1500)
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generated_ids = [
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output_ids[len(input_ids) :]
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for input_ids, output_ids in zip(inputs["input_ids"], output_ids)
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]
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output_text = processor.batch_decode(
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generated_ids, skip_special_tokens=True, clean_up_tokenization_spaces=True
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)
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print(output_text)
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def main():
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run_inference(model_name="/root/model")
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if __name__ == "__main__":
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main() |