trainsetloading.py

#Define transformations for the training set, flip the images randomly, crop out and apply mean and std normalization
train_transformations = transforms.Compose([
    transforms.RandomHorizontalFlip(),
    transforms.RandomCrop(32,padding=4),
    transforms.ToTensor(),
    transforms.Normalize((0.5,0.5,0.5), (0.5,0.5,0.5))
])

#Load the training set
train_set =CIFAR10(root="./data",train=True,transform=train_transformations,download=True)

#Create a loder for the training set
train_loader = DataLoader(train_set,batch_size=32,shuffle=True,num_workers=4)