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| """Convert document images to markdown using DeepSeek-OCR-2 via transformers.""" |
|
|
| import argparse |
| import json |
| import os |
| import sys |
| import tempfile |
| from datetime import datetime |
|
|
| import torch |
| from datasets import load_dataset, Dataset |
| from huggingface_hub import login |
| from PIL import Image |
| from tqdm.auto import tqdm |
| from transformers import AutoModel, AutoTokenizer |
|
|
| PROMPT = "<image>\n<|grounding|>Convert the document to markdown. " |
|
|
|
|
| def main(input_dataset: str, output_dataset: str, split: str = "train", |
| max_samples: int | None = None, image_column: str = "image"): |
|
|
| if not torch.cuda.is_available(): |
| print("ERROR: CUDA not available. GPU required.") |
| sys.exit(1) |
| print(f"GPU: {torch.cuda.get_device_name(0)}") |
|
|
| token = os.environ.get("HF_TOKEN") |
| if token: |
| login(token=token) |
|
|
| print("Loading model deepseek-ai/DeepSeek-OCR-2...") |
| tokenizer = AutoTokenizer.from_pretrained( |
| "deepseek-ai/DeepSeek-OCR-2", trust_remote_code=True |
| ) |
| model = AutoModel.from_pretrained( |
| "deepseek-ai/DeepSeek-OCR-2", |
| trust_remote_code=True, |
| use_safetensors=True, |
| torch_dtype=torch.bfloat16, |
| ).cuda() |
|
|
| print(f"Loading dataset {input_dataset}...") |
| ds = load_dataset(input_dataset, split=split) |
| if max_samples: |
| ds = ds.select(range(min(max_samples, len(ds)))) |
| print(f"Processing {len(ds)} samples...") |
|
|
| results = [] |
| with tempfile.TemporaryDirectory() as tmpdir: |
| for i, row in enumerate(tqdm(ds)): |
| img_path = os.path.join(tmpdir, f"img_{i}.jpg") |
| img = row[image_column] |
| if isinstance(img, dict): |
| img = Image.open(__import__("io").BytesIO(img["bytes"])) |
| img.save(img_path, format="JPEG", quality=95) |
|
|
| try: |
| out = model.infer( |
| tokenizer, |
| prompt=PROMPT, |
| image_file=img_path, |
| output_path=tmpdir, |
| base_size=1024, |
| image_size=768, |
| crop_mode=True, |
| save_results=False, |
| ) |
| if i == 0: |
| print(f"[DEBUG] out type={type(out)}, value={repr(out)[:200]}") |
| markdown = out if isinstance(out, str) else str(out) |
| except Exception as e: |
| print(f"Error on sample {i}: {e}") |
| markdown = "" |
|
|
| results.append({ |
| "image": row[image_column], |
| "gt_json": row.get("gt_json", ""), |
| "markdown": markdown, |
| "inference_info": json.dumps([{ |
| "column_name": "markdown", |
| "model_id": "deepseek-ai/DeepSeek-OCR-2", |
| "processing_date": datetime.now().strftime("%Y-%m-%d"), |
| "backend": "transformers", |
| }]), |
| }) |
|
|
| print(f"Pushing to {output_dataset}...") |
| Dataset.from_list(results).push_to_hub(output_dataset, private=False) |
| print(f"Done → https://huggingface.co/datasets/{output_dataset}") |
|
|
|
|
| if __name__ == "__main__": |
| parser = argparse.ArgumentParser() |
| parser.add_argument("input_dataset") |
| parser.add_argument("output_dataset") |
| parser.add_argument("--split", default="train") |
| parser.add_argument("--max-samples", type=int, default=None) |
| parser.add_argument("--image-column", default="image") |
| args = parser.parse_args() |
| main(args.input_dataset, args.output_dataset, args.split, |
| args.max_samples, args.image_column) |
|
|
