70 lines
2.6 KiB
Python
70 lines
2.6 KiB
Python
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from transformers import DonutProcessor, VisionEncoderDecoderModel, VisionEncoderDecoderConfig
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import re
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import torch
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from PIL import Image
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import time
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from fastapi import FastAPI, UploadFile, File
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import io
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import os
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print("Set up config")
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config_vision = VisionEncoderDecoderConfig.from_pretrained("Zombely/plwiki-proto-fine-tuned-v3.2")
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config_vision.encoder.image_size = [1920, 2560] # (height, width)
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config_vision.decoder.max_length = 768
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processor = DonutProcessor.from_pretrained("Zombely/plwiki-proto-fine-tuned-v3.2")
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model = VisionEncoderDecoderModel.from_pretrained("Zombely/plwiki-proto-fine-tuned-v3.2", config=config_vision)
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processor.image_processor.size = [1920, 2560][::-1] # should be (width, height)
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processor.image_processor.do_align_long_axis = False
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# dataset = load_dataset(config.validation_dataset_path, split=config.validation_dataset_split)
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device = "cuda" if torch.cuda.is_available() else "cpu"
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model.eval()
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model.to(device)
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print("Print ipconfig")
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os.system("ipconfig")
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print("Starting server")
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app = FastAPI()
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@app.post("/process")
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async def process_image(file: UploadFile= File(...)):
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request_object_content = await file.read()
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input_image = Image.open(io.BytesIO(request_object_content))
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# prepare encoder inputs
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pixel_values = processor(input_image.convert("RGB"), return_tensors="pt").pixel_values
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pixel_values = pixel_values.to(device)
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# prepare decoder inputs
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task_prompt = "<s_cord-v2>"
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decoder_input_ids = processor.tokenizer(task_prompt, add_special_tokens=False, return_tensors="pt").input_ids
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decoder_input_ids = decoder_input_ids.to(device)
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print("Start processing")
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# autoregressively generate sequence
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start_time = time.time()
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outputs = model.generate(
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pixel_values,
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decoder_input_ids=decoder_input_ids,
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max_length=model.decoder.config.max_position_embeddings,
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early_stopping=True,
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pad_token_id=processor.tokenizer.pad_token_id,
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eos_token_id=processor.tokenizer.eos_token_id,
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use_cache=True,
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num_beams=1,
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bad_words_ids=[[processor.tokenizer.unk_token_id]],
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return_dict_in_generate=True,
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)
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processing_time = (time.time() - start_time)
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# turn into JSON
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seq = processor.batch_decode(outputs.sequences)[0]
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seq = seq.replace(processor.tokenizer.eos_token, "").replace(processor.tokenizer.pad_token, "")
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seq = re.sub(r"<.*?>", "", seq, count=1).strip() # remove first task start token
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seq = processor.token2json(seq)
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return {"data": seq['text_sequence'], "processing_time": f"{processing_time} seconds"}
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