utils.py

#*
# @file Different utility functions 
# Copyright (c) Zhewei Yao, Amir Gholami
# All rights reserved.
# This file is part of HessianFlow library.
#
# HessianFlow is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# HessianFlow is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with HessianFlow.  If not, see <http://www.gnu.org/licenses/>.
#*

import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torchvision import datasets, transforms
from torch.autograd import Variable

def getData(name = 'cifar10', train_bs = 128, test_bs = 1000):    
    if name == 'svhn':
        train_loader = torch.utils.data.DataLoader(
    datasets.SVHN('../data', split = 'extra', download = True,
                   transform = transforms.Compose([
                       transforms.ToTensor()
                   ])),
    batch_size = train_bs, shuffle = True)
        test_loader = torch.utils.data.DataLoader(
    datasets.SVHN('../data', split = 'test', download = True, transform = transforms.Compose([
                       transforms.ToTensor()
                   ])),
    batch_size = test_bs, shuffle=False)
   
    if name == 'mnist':

        train_loader = torch.utils.data.DataLoader(
            datasets.MNIST('../data', train = True, download = True,
                           transform = transforms.Compose([
                               transforms.ToTensor(),
                               transforms.Normalize((0.1307,), (0.3081,))
                           ])),
            batch_size = train_bs, shuffle = True)
        test_loader = torch.utils.data.DataLoader(
            datasets.MNIST('../data', train = False, transform = transforms.Compose([
                               transforms.ToTensor(),
                               transforms.Normalize((0.1307,), (0.3081,))
                           ])),
            batch_size = test_bs, shuffle = False)


    if name == 'cifar10':
        transform_train = transforms.Compose([
        #transforms.RandomCrop(32, padding = 4),
        #transforms.RandomHorizontalFlip(),
        transforms.ToTensor(),
        #transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),
    ])

        transform_test = transforms.Compose([
        transforms.ToTensor(),
        #transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),
    ])

        trainset = datasets.CIFAR10(root='../data', train = True, download = True, transform = transform_train)
        train_loader = torch.utils.data.DataLoader(trainset, batch_size = train_bs, shuffle = True)

        testset = datasets.CIFAR10(root='../data', train = False, download = False, transform = transform_test)
        test_loader = torch.utils.data.DataLoader(testset, batch_size = test_bs, shuffle = False)
    
    if name == 'cifar10da': # with data augumentation
        transform_train = transforms.Compose([
        transforms.RandomCrop(32, padding = 4),
        transforms.RandomHorizontalFlip(),
        transforms.ToTensor(),
        transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),
    ])

        transform_test = transforms.Compose([
        transforms.ToTensor(),
        transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),
    ])

        trainset = datasets.CIFAR10(root='../data', train = True, download = True, transform = transform_train)
        train_loader = torch.utils.data.DataLoader(trainset, batch_size = train_bs, shuffle = True)

        testset = datasets.CIFAR10(root='../data', train = False, download = False, transform = transform_test)
        test_loader = torch.utils.data.DataLoader(testset, batch_size = test_bs, shuffle = False)
    
    if name == 'cifar100':
        
        transform_train = transforms.Compose([
        transforms.RandomCrop(32, padding = 4),
        transforms.RandomHorizontalFlip(),
        transforms.ToTensor(),
        transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),
    ])

        transform_test = transforms.Compose([
        transforms.ToTensor(),
        transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),
    ])

        trainset = datasets.CIFAR100(root='../data', train = True, download = True, transform = transform_train)
        train_loader = torch.utils.data.DataLoader(trainset, batch_size = train_bs, shuffle = True)

        testset = datasets.CIFAR100(root='../data', train = False, download = False, transform = transform_test)
        test_loader = torch.utils.data.DataLoader(testset, batch_size = test_bs, shuffle = False)
    
    if name == 'tinyimagenet':      
        normalize = transforms.Normalize(mean = [0.44785526394844055, 0.41693055629730225, 0.36942949891090393],
                                     std = [0.2928885519504547, 0.28230994939804077, 0.2889912724494934])
        train_dataset = datasets.ImageFolder(
        '../data/tiny-imagenet-200/train',
        transforms.Compose([
            transforms.RandomCrop(64, padding = 4),
            transforms.RandomHorizontalFlip(),
            transforms.ToTensor(),
            normalize,
        ]))
        
        train_loader = torch.utils.data.DataLoader(train_dataset, batch_size = train_bs, shuffle = True, num_workers = 4, pin_memory = False)
        
        test_dataset = datasets.ImageFolder(
        '../data/tiny-imagenet-200/val',
        transforms.Compose([
            transforms.ToTensor(),
            normalize,
        ]))
        
        test_loader = torch.utils.data.DataLoader(test_dataset, batch_size = test_bs, shuffle = False)
        
    return train_loader, test_loader