import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.utils.data import DataLoader, random_split, SubsetRandomSampler, Subset, WeightedRandomSampler
import torchvision
from torch.autograd import Variable
from torchvision.datasets import ImageFolder, DatasetFolder
import torchvision.transforms as transforms
from sklearn.metrics import confusion_matrix, classification_report
import os
import seaborn as sns

import PIL
from PIL import Image
import warnings
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
device

image_transforms = transforms.Compose(
                   [transforms.Resize((32,32)),
                    transforms.ToTensor(),
                    transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])

MAIN = '../input/face-mask-dataset/data'

dataset = ImageFolder(
                      root = MAIN,
                      transform = image_transforms
                       )
dataset