From 3ad876ea697876cfa2d4ec84c79dfa80d9cf366d Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Micha=C5=82=20Koz=C5=82owski?= Date: Tue, 24 Jan 2023 18:13:25 +0100 Subject: [PATCH] fixing donut --- single_eval.py | 77 ----------------------------------------- test.image.jpg | Bin 363474 -> 0 bytes train.py | 10 ++++-- utils/donut_dataset.py | 6 ++-- 4 files changed, 11 insertions(+), 82 deletions(-) delete mode 100644 single_eval.py delete mode 100644 test.image.jpg diff --git a/single_eval.py b/single_eval.py deleted file mode 100644 index 73e986b..0000000 --- a/single_eval.py +++ /dev/null @@ -1,77 +0,0 @@ -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 -from sys import platform - -image_size = [1920, 2560] -print("Set up config") -config_vision = VisionEncoderDecoderConfig.from_pretrained("Zombely/plwiki-proto-fine-tuned-v3.2") -config_vision.encoder.image_size = image_size # (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 = image_size[::-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") -if platform == "linux": - os.system("ip r") -else: - 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 = "" - 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"} diff --git a/test.image.jpg b/test.image.jpg deleted file mode 100644 index 6732149b70755473d31729bd191d24ce93ab326c..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 363474 zcmbTdX;_k5{5JX^2xx>uR#2!I6l&#msz5=Z!70NWnkP)lVVhgPOfi$8(Fn&TH=^ZS zsSQ@T%fbf0N-%Y+sTBr_X69^ZYA^f$p5eOAr*qEY;=^-2)4HFv)^E7)-}>G0TMd}{ zdHH$)AP@k6^grNt7qAI{L!mIJAshyS85zNi5f%#(3l<>I=8H@$ESFhZSuVq1uy_Xo zcEuVT24lOyZjGaptE;OufkYuXlO0@Ko&S3X$jHbDu>gTuxDe%RgRyb`|J;5{0W-K^ zF$4gERsaTOAg~$e_bb3!-%mr(|1{wLXdnYH#1IOD8yPRqU+~ZrFaUwU1`x2JAq1ko 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