Getting_the_Data.py

from __future__ import print_function
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torchvision import datasets, transforms
from tqdm import tqdm
from torch.optim.lr_scheduler import StepLR
from torch.optim.lr_scheduler import OneCycleLR
import matplotlib.pyplot as plt
import numpy as np
import torchvision
import torchsummary
from torchsummary import summary


def get_and_transform_the_data():
	use_cuda = torch.cuda.is_available()

	cuda = torch.cuda.is_available()
	print("CUDA Available?", cuda)
	
	SEED=1
	
	torch.manual_seed(SEED)

	if cuda:
		torch.cuda.manual_seed(SEED)

	transform = transforms.Compose(
		[transforms.RandomCrop(32, padding=4),
		 transforms.RandomHorizontalFlip(),
		 transforms.ToTensor(),
		 transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])

	trainset = torchvision.datasets.CIFAR10(root='./data', train=True,
											download=True, transform=transform)
	train_loader = torch.utils.data.DataLoader(trainset, batch_size=128,
											  shuffle=True, num_workers=4)

	testset = torchvision.datasets.CIFAR10(root='./data', train=False,
										   download=True, transform=transform)
	test_loader = torch.utils.data.DataLoader(testset, batch_size=128,
											 shuffle=False, num_workers=4)

	classes = ('plane', 'car', 'bird', 'cat',
			   'deer', 'dog', 'frog', 'horse', 'ship', 'truck')
	
	return trainset, testset, train_loader, test_loader, classes