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lower params

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This commit is contained in:
mkozlowskiAzimuthe 2023-01-22 23:46:09 +01:00
parent 6ba533aeda
commit 28104dd686
1 changed files with 46 additions and 45 deletions
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single_eval.py
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@ -7,21 +7,22 @@ from fastapi import FastAPI, UploadFile, File
import io import io
import os import os
# print("Set up config") print("Set up config")
# config_vision = VisionEncoderDecoderConfig.from_pretrained("Zombely/plwiki-proto-fine-tuned-v3.2") image_size = [768, 1280]
# config_vision.encoder.image_size = [1920, 2560] # (height, width) config_vision = VisionEncoderDecoderConfig.from_pretrained("Zombely/plwiki-proto-fine-tuned-v3.2")
# config_vision.decoder.max_length = 768 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") 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) 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.size = image_size[::-1] # should be (width, height)
# processor.image_processor.do_align_long_axis = False processor.image_processor.do_align_long_axis = False
# # dataset = load_dataset(config.validation_dataset_path, split=config.validation_dataset_split) # dataset = load_dataset(config.validation_dataset_path, split=config.validation_dataset_split)
# device = "cuda" if torch.cuda.is_available() else "cpu" device = "cuda" if torch.cuda.is_available() else "cpu"
# model.eval() model.eval()
# model.to(device) model.to(device)
print("Print ipconfig") print("Print ipconfig")
os.system("ipconfig") os.system("ipconfig")
@ -33,40 +34,40 @@ app = FastAPI()
async def test(): async def test():
return {"message": "Test"} return {"message": "Test"}
# @app.post("/process") @app.post("/process")
# async def process_image(file: UploadFile= File(...)): async def process_image(file: UploadFile= File(...)):
# request_object_content = await file.read() request_object_content = await file.read()
# input_image = Image.open(io.BytesIO(request_object_content)) input_image = Image.open(io.BytesIO(request_object_content))
# # prepare encoder inputs # prepare encoder inputs
# pixel_values = processor(input_image.convert("RGB"), return_tensors="pt").pixel_values pixel_values = processor(input_image.convert("RGB"), return_tensors="pt").pixel_values
# pixel_values = pixel_values.to(device) pixel_values = pixel_values.to(device)
# # prepare decoder inputs # prepare decoder inputs
# task_prompt = "<s_cord-v2>" task_prompt = "<s_cord-v2>"
# decoder_input_ids = processor.tokenizer(task_prompt, add_special_tokens=False, return_tensors="pt").input_ids decoder_input_ids = processor.tokenizer(task_prompt, add_special_tokens=False, return_tensors="pt").input_ids
# decoder_input_ids = decoder_input_ids.to(device) decoder_input_ids = decoder_input_ids.to(device)
# print("Start processing") print("Start processing")
# # autoregressively generate sequence # autoregressively generate sequence
# start_time = time.time() start_time = time.time()
# outputs = model.generate( outputs = model.generate(
# pixel_values, pixel_values,
# decoder_input_ids=decoder_input_ids, decoder_input_ids=decoder_input_ids,
# max_length=model.decoder.config.max_position_embeddings, max_length=model.decoder.config.max_position_embeddings,
# early_stopping=True, early_stopping=True,
# pad_token_id=processor.tokenizer.pad_token_id, pad_token_id=processor.tokenizer.pad_token_id,
# eos_token_id=processor.tokenizer.eos_token_id, eos_token_id=processor.tokenizer.eos_token_id,
# use_cache=True, use_cache=True,
# num_beams=1, num_beams=1,
# bad_words_ids=[[processor.tokenizer.unk_token_id]], bad_words_ids=[[processor.tokenizer.unk_token_id]],
# return_dict_in_generate=True, return_dict_in_generate=True,
# ) )
# processing_time = (time.time() - start_time) processing_time = (time.time() - start_time)
# # turn into JSON # turn into JSON
# seq = processor.batch_decode(outputs.sequences)[0] seq = processor.batch_decode(outputs.sequences)[0]
# seq = seq.replace(processor.tokenizer.eos_token, "").replace(processor.tokenizer.pad_token, "") 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 = re.sub(r"<.*?>", "", seq, count=1).strip() # remove first task start token
# seq = processor.token2json(seq) seq = processor.token2json(seq)
# return {"data": seq['text_sequence'], "processing_time": f"{processing_time} seconds"} return {"data": seq['text_sequence'], "processing_time": f"{processing_time} seconds"}