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'''
---BlockAI/T5-small Auto Generate Code---
Author : BlockAI
Project Name: T5-small
Project Link: https://blockai.kr/BlockAI/T5-small (BlockAI)
Create Date : 2024-10-05
---Requirements---
# 사용자의 환경(OS, CUDA 등)에 따라 라이브러리 버전을 맞춰주세요
pip install torch==2.0 torchvision==0.15.2 torchtext==0.15.2 torchaudio==2.0.2
pip install pytorch-lightning==2.0.4
pip install tqdm
pip install pandas
pip install scikit-learn
pip install transformers
pip install timm
---Folder Structure---
--📂 data
|--📄 train.csv
|--📄 test.csv
--📄 T5-small.py
--📄 T5-small.ipynb
--📄 requirements.txt
'''
import os
import argparse
import copy
from glob import glob
from tqdm import tqdm
import numpy as np
import pandas as pd
from sklearn import preprocessing
from sklearn.model_selection import train_test_split
import torch
import pytorch_lightning as pl
path_sep = os.sep
# https://pytorch.org/tutorials/beginner/basics/data_tutorial.html#creating-a-custom-dataset-for-your-files
class Dataset(torch.utils.data.Dataset):
def __init__(self, inputs, targets=[]):
self.inputs = inputs
self.targets = targets
# 학습 및 추론 과정에서 데이터를 1개씩 꺼내오는 곳
def __getitem__(self, idx):
# 정답이 있다면 if문을, 없다면 else문을 수행합니다
if len(self.targets) == 0:
return torch.tensor(self.inputs[idx])
else:
return torch.tensor(self.inputs[idx]), torch.tensor(self.targets[idx])
# 입력하는 개수만큼 데이터를 사용합니다
# 'return 100'이면 1에폭에 100개의 데이터만 사용합니다
def __len__(self):
return len(self.inputs)
# https://pytorch-lightning.readthedocs.io/en/stable/extensions/datamodules.html
class Dataloader(pl.LightningDataModule):
# 데이터의 종류에 따라 코드 수정이 필요할 수 있습니다
def __init__(self, data_folder, batch_size, train_ratio, shuffle):
super().__init__()
self.data_folder = data_folder
self.batch_size = batch_size
self.train_ratio = train_ratio
self.shuffle = shuffle
self.train_dataset = None
self.test_dataset = None
self.predict_dataset = None
def preprocessing(self, data):
# 타겟 데이터가 없으면 빈 배열을 리턴합니다.
try:
targets = data[self.target_columns].values.tolist()
inputs = data.drop(self.target_columns, axis=1).values.tolist()
except:
targets = []
inputs = data.values.tolist()
return inputs, targets
def setup(self, stage='fit'):
if stage == 'fit':
train_data = pd.read_csv(os.path.join(self.data_folder, 'train.csv'), sep=',', header=None, index_col=None, encoding='utf-8')
# 학습데이터 준비
train_inputs, train_targets = self.preprocessing(train_data)
# train 데이터만 shuffle을 적용해줍니다, 필요하다면 val, test 데이터에도 shuffle을 적용할 수 있습니다
self.train_dataset = Dataset(train_inputs, train_targets)
else:
# 평가데이터 준비
test_data = pd.read_csv(os.path.join(self.data_folder, 'test.csv'), sep=',', header=None, index_col=None, encoding='utf-8')
test_inputs, test_targets = self.preprocessing(test_data)
self.test_dataset = Dataset(test_inputs, test_targets)
self.predict_dataset = Dataset(test_inputs, [])
def train_dataloader(self):
return torch.utils.data.DataLoader(self.train_dataset, batch_size=self.batch_size, shuffle=args.shuffle)
def test_dataloader(self):
return torch.utils.data.DataLoader(self.test_dataset, batch_size=self.batch_size)
def predict_dataloader(self):
return torch.utils.data.DataLoader(self.predict_dataset, batch_size=self.batch_size)
# https://pytorch-lightning.readthedocs.io/en/stable/common/lightning_module.html
class Model(pl.LightningModule):
def __init__(self):
super().__init__()
self.save_hyperparameters()
self.transformer_1 = torch.nn.Transformer()
self.linear_1 = torch.nn.Linear(in_features=512, out_features=32128)
self.embedding_1 = torch.nn.Embedding(num_embeddings=32128, embedding_dim=512)
self.softmax_1 = torch.nn.Softmax()
def forward(self, x_emptycsv):
x_0 = self.embedding_1(x_emptycsv)
x_0 = self.transformer_1(x_0)
x_0 = self.linear_1(x_0)
x_0 = self.softmax_1(x_0)
return
def training_step(self, batch, batch_idx):
x_emptycsv, y = batch
logits = self(x_emptycsv)
self.log("train_loss", loss)
return loss
def validation_step(self, batch, batch_idx):
x_emptycsv, y = batch
logits = self(x_emptycsv)
self.log("val_loss", loss)
return loss
def test_step(self, batch, batch_idx):
x_emptycsv, y = batch
logits = self(x_emptycsv)
self.log("test_loss", loss)
return loss
def predict_step(self, batch, batch_idx):
x_emptycsv = batch
logits = self(x_emptycsv)
return logits
def configure_optimizers(self):
pass
if __name__ == '__main__':
# https://docs.python.org/ko/3/library/argparse.html
# 하이퍼 파라미터 등 각종 설정값을 입력받습니다
# 터미널 실행 예시 : python3 run.py --batch_size=64 ...
# 실행 시 '--batch_size=64' 같은 인자를 입력하지 않으면 default 값이 기본으로 실행됩니다
parser = argparse.ArgumentParser()
parser.add_argument('--data_folder', default='./data')
parser.add_argument('--batch_size', default=0)
parser.add_argument('--max_epoch', default=0)
parser.add_argument('--shuffle', default=False)
parser.add_argument('--train_ratio', default=1.0)
args = parser.parse_args()
dataloader = Dataloader(args.data_folder, args.batch_size, args.train_ratio, args.shuffle)
model = Model()
# https://pytorch-lightning.readthedocs.io/en/stable/common/trainer.html
# 학습 및 추론을 위한 Trainer 설정
trainer = pl.Trainer(accelerator='gpu', devices=1, max_epochs=args.max_epoch)
trainer.fit(model=model, datamodule=dataloader)
# trainer.test(model=model, datamodule=dataloader)
# predictions = trainer.predict(model=model, datamodule=dataloader)
