| torch==2.0.1 | |
| torchvision==0.15.2 | |
| transformers==4.37.2 | |
| tiktoken==0.6.0 | |
| verovio==4.3.1 | |
| accelerate==0.28.0 | |
| from transformers import AutoModel, AutoTokenizer | |
| tokenizer = AutoTokenizer.from_pretrained('ucaslcl/GOT-OCR2_0', trust_remote_code=True) | |
| model = AutoModel.from_pretrained('ucaslcl/GOT-OCR2_0', trust_remote_code=True, low_cpu_mem_usage=True, device_map='cuda', use_safetensors=True, pad_token_id=tokenizer.eos_token_id) | |
| model = model.eval().cuda() | |
| # input your test image | |
| image_file = 'radio.jpeg' | |
| # plain texts OCR | |
| res = model.chat(tokenizer, image_file, ocr_type='ocr') | |
| # format texts OCR: | |
| # res = model.chat(tokenizer, image_file, ocr_type='format') | |
| # fine-grained OCR: | |
| # res = model.chat(tokenizer, image_file, ocr_type='ocr', ocr_box='') | |
| # res = model.chat(tokenizer, image_file, ocr_type='format', ocr_box='') | |
| # res = model.chat(tokenizer, image_file, ocr_type='ocr', ocr_color='') | |
| # res = model.chat(tokenizer, image_file, ocr_type='format', ocr_color='') | |
| # multi-crop OCR: | |
| # res = model.chat_crop(tokenizer, image_file, ocr_type='ocr') | |
| # res = model.chat_crop(tokenizer, image_file, ocr_type='format') | |
| # render the formatted OCR results: | |
| # res = model.chat(tokenizer, image_file, ocr_type='format', render=True, save_render_file = './demo.html') | |
| print(res) | |