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| import re | |
| import time | |
| import torch | |
| from transformers import DonutProcessor, VisionEncoderDecoderModel | |
| from config import settings | |
| from functools import lru_cache | |
| import os | |
| def load_model(): | |
| processor = DonutProcessor.from_pretrained(settings.processor) | |
| model = VisionEncoderDecoderModel.from_pretrained(settings.model) | |
| device = "cuda" if torch.cuda.is_available() else "cpu" | |
| model.to(device) | |
| return processor, model, device | |
| def process_document_donut(image): | |
| worker_pid = os.getpid() | |
| print(f"Handling inference request with worker PID: {worker_pid}") | |
| start_time = time.time() | |
| processor, model, device = load_model() | |
| # prepare encoder inputs | |
| pixel_values = processor(image, return_tensors="pt").pixel_values | |
| # prepare decoder inputs | |
| task_prompt = "<s_cord-v2>" | |
| decoder_input_ids = processor.tokenizer(task_prompt, add_special_tokens=False, return_tensors="pt").input_ids | |
| # generate answer | |
| 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=1, | |
| bad_words_ids=[[processor.tokenizer.unk_token_id]], | |
| return_dict_in_generate=True, | |
| ) | |
| # postprocess | |
| sequence = processor.batch_decode(outputs.sequences)[0] | |
| sequence = sequence.replace(processor.tokenizer.eos_token, "").replace(processor.tokenizer.pad_token, "") | |
| sequence = re.sub(r"<.*?>", "", sequence, count=1).strip() # remove first task start token | |
| end_time = time.time() | |
| processing_time = end_time - start_time | |
| print(f"Inference done, worker PID: {worker_pid}") | |
| return processor.token2json(sequence), processing_time |