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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
@lru_cache(maxsize=1)
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 |