starting with all the libraries to import

imports.py

# visualization library
import cv2
from matplotlib import pyplot as plt
# data storing library
import numpy as np
import pandas as pd
# torch libraries
from torch.optim.lr_scheduler import ReduceLROnPlateau
import torch
import torch.nn as nn
from torch.nn import functional as F
import torch.optim as optim
import torch.backends.cudnn as cudnn
from torch.utils.data import DataLoader, Dataset, sampler
# architecture and data split library
from sklearn.model_selection import train_test_split
import segmentation_models_pytorch as smp
# augmenation library
from albumentations import (HorizontalFlip, ShiftScaleRotate, Normalize, Resize, Compose, GaussNoise)
from albumentations.pytorch import ToTensor
# others
import os
import pdb
import time
import warnings
import random
from tqdm import tqdm_notebook as tqdm
import concurrent.futures
# warning print supression
warnings.filterwarnings("ignore")

# *****************to reproduce same results fixing the seed and hash*******************
seed = 42
random.seed(seed)
os.environ["PYTHONHASHSEED"] = str(seed)
np.random.seed(seed)
torch.cuda.manual_seed(seed)
torch.manual_seed(seed)
torch.backends.cudnn.deterministic = True