donut/single_eval.py

from transformers import DonutProcessor, VisionEncoderDecoderModel, VisionEncoderDecoderConfig
import re
import torch
from PIL import Image
import time
from fastapi import FastAPI, UploadFile, File
import io
import os   

print("Set up config")
config_vision = VisionEncoderDecoderConfig.from_pretrained("Zombely/plwiki-proto-fine-tuned-v3.2")
config_vision.encoder.image_size = [1920, 2560] # (height, width)
config_vision.decoder.max_length = 768

processor = DonutProcessor.from_pretrained("Zombely/plwiki-proto-fine-tuned-v3.2")
model = VisionEncoderDecoderModel.from_pretrained("Zombely/plwiki-proto-fine-tuned-v3.2", config=config_vision)

processor.image_processor.size = [1920, 2560][::-1] # should be (width, height)
processor.image_processor.do_align_long_axis = False

# dataset = load_dataset(config.validation_dataset_path, split=config.validation_dataset_split)
device = "cuda" if torch.cuda.is_available() else "cpu"
model.eval()
model.to(device)

print("Print ipconfig")
os.system("ipconfig")

print("Starting server")
app = FastAPI()

@app.get("/test")
async def test():
    return {"message": "Test"}

@app.post("/process")
async def process_image(file: UploadFile= File(...)):

    request_object_content = await file.read()
    input_image = Image.open(io.BytesIO(request_object_content))

    # prepare encoder inputs
    pixel_values = processor(input_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)

    print("Start processing")
    # autoregressively generate sequence
    start_time = time.time()
    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,
        )
    processing_time = (time.time() - start_time)

    # 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)
    return {"data": seq['text_sequence'], "processing_time": f"{processing_time} seconds"}