From 28104dd6861f455749d3594355271d5a94f8e5c9 Mon Sep 17 00:00:00 2001 From: mkozlowskiAzimuthe Date: Sun, 22 Jan 2023 23:46:09 +0100 Subject: [PATCH] lower params --- single_eval.py | 91 +++++++++++++++++++++++++------------------------- 1 file changed, 46 insertions(+), 45 deletions(-) diff --git a/single_eval.py b/single_eval.py index 34f73e6..30c368e 100644 --- a/single_eval.py +++ b/single_eval.py @@ -7,21 +7,22 @@ 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 +print("Set up config") +image_size = [768, 1280] +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 = 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 +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) +# 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") @@ -33,40 +34,40 @@ app = FastAPI() async def test(): return {"message": "Test"} -# @app.post("/process") -# async def process_image(file: UploadFile= File(...)): +@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)) + 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) + # 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) + 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"} + # 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"}