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# init git
cd existing_folder
git init --initial-branch=main
git remote add origin git@gitlab-XXXX.com:yanwei.liu/XXXX-XXXX.git

# git commit
git add .
git commit -m "Initial commit"
git push -u origin main

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import torch
import torch.nn.functional as F

def pad_image_to_nearest_multiple(image, multiple=32):
    image = torch.from_numpy(image.astype(np.float32)).unsqueeze(0)

    channels, height, width = image.shape
    padded_height = ((height + multiple - 1) // multiple) * multiple
    padded_width = ((width + multiple - 1) // multiple) * multiple
    

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import warnings
warnings.simplefilter(action='ignore', category=FutureWarning)

import datetime
import pandas as pd
from pycoingecko import CoinGeckoAPI
import yfinance as yf

def get_monthly_prices_stock(symbol, date):
    today = datetime.date.today()

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import cv2
import imageio
import rawpy

class RawImage:
    """
    A class for working with raw image files.

    Attributes:
        file_path (str): The path to the raw image file.

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import os
import random
from shutil import copyfile
import numpy as np
from PIL import Image
import multiprocessing as mp

class DarkFace2YOLOv5:
    def __init__(self, data_dir, class_list, train_ratio=0.8, random_seed=None):
        self.data_dir = data_dir

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import numpy as np
import cv2
import imageio

def simplest_color_balance(img, percent):
    out_channels = []
    channels = cv2.split(img)
    for channel in channels:
        total_pixels = img.shape[0] * img.shape[1]
        low_val, high_val = np.percentile(channel, [percent, 100 - percent])

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import os
import xml.etree.ElementTree as ET
import csv

# Set the paths for the input and output directories
voc_path = '/home/Yanwei_Liu/Datasets/PASCALRAW/annotations/'
train_img_path = '/home/Yanwei_Liu/Datasets/PASCALRAW/images/train/'
val_img_path = '/home/Yanwei_Liu/Datasets/PASCALRAW/images/val/'

train_file_path = '/home/Yanwei_Liu/Datasets/PASCALRAW/trainval/train.txt'

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(Pdb) pdb.set_trace = lambda: None  # This replaces the set_trace() function!
(Pdb) continue

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"""
Implementation of IQA metrics in PyTorch, including PSNR, SSIM, LPIPS, NIQE, and LOE.
"""
import torch
import torch.nn as nn
import torchvision.models as models
import torchvision.transforms.functional as F
from torch.nn.functional import conv2d
from IQA_pytorch import SSIM, LPIPSvgg

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import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_absolute_error, mean_squared_error
from FinMind.data import DataLoader

dl = DataLoader()
stock_data = dl.taiwan_stock_daily(
    stock_id='2330', start_date='2010-01-01', end_date='2022-12-20'