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from transformers import DonutProcessor, VisionEncoderDecoderModel
import locale
import re
import json
import torch
from tqdm.auto import tqdm
import numpy as np
from donut import JSONParseEvaluator
from datasets import load_dataset
from functools import lru_cache
import os
import time
from config import settings
locale.getpreferredencoding = lambda: "UTF-8"
@lru_cache(maxsize=1)
def prepare_model():
processor = DonutProcessor.from_pretrained(settings.processor)
model = VisionEncoderDecoderModel.from_pretrained(settings.model)
device = "cuda" if torch.cuda.is_available() else "cpu"
model.eval()
model.to(device)
dataset = load_dataset(settings.dataset, split="test")
return processor, model, device, dataset
def run_evaluate_donut():
worker_pid = os.getpid()
print(f"Handling evaluation request with worker PID: {worker_pid}")
start_time = time.time()
output_list = []
accs = []
processor, model, device, dataset = prepare_model()
for idx, sample in tqdm(enumerate(dataset), total=len(dataset)):
# prepare encoder inputs
pixel_values = processor(sample["image"].convert("RGB"), return_tensors="pt").pixel_values
pixel_values = pixel_values.to(device)
# prepare decoder inputs
task_prompt = "<s_cord-v2>"
decoder_input_ids = processor.tokenizer(task_prompt, add_special_tokens=False, return_tensors="pt").input_ids
decoder_input_ids = decoder_input_ids.to(device)
# autoregressively generate sequence
outputs = model.generate(
pixel_values,
decoder_input_ids=decoder_input_ids,
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,
)
# turn into JSON
seq = processor.batch_decode(outputs.sequences)[0]
seq = seq.replace(processor.tokenizer.eos_token, "").replace(processor.tokenizer.pad_token, "")
seq = re.sub(r"<.*?>", "", seq, count=1).strip() # remove first task start token
seq = processor.token2json(seq)
ground_truth = json.loads(sample["ground_truth"])
ground_truth = ground_truth["gt_parse"]
evaluator = JSONParseEvaluator()
score = evaluator.cal_acc(seq, ground_truth)
accs.append(score)
output_list.append(seq)
end_time = time.time()
processing_time = end_time - start_time
scores = {"accuracies": accs, "mean_accuracy": np.mean(accs)}
print(scores, f"length : {len(accs)}")
print("Mean accuracy:", np.mean(accs))
print(f"Evaluation done, worker PID: {worker_pid}")
return scores, np.mean(accs), processing_time
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