data.py

from torchvision.datasets import CIFAR100
from torchvision import transforms
import torch
import config as cf

transform_train = transforms.Compose([
    transforms.RandomCrop(32, padding = 4),
    transforms.RandomHorizontalFlip(),
    transforms.ToTensor(),
    transforms.Normalize(cf.mean['cifar100'], cf.std['cifar100'])
])
transform_test = transforms.Compose([
    transforms.ToTensor(),
    transforms.Normalize(cf.mean['cifar100'], cf.std['cifar100'])
])
trainset = CIFAR100(root=cf.root, train=True, download=True, transform=transform_train)
testset = CIFAR100(root=cf.root, train=False, download=True, transform=transform_test)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=cf.batch_size, shuffle=True, num_workers=8)
testloader = torch.utils.data.DataLoader(testset, batch_size=100, shuffle=False, num_workers=8)