shuffled-block-sparse-training/preprocess.py at master · HPC-Research-Lab/shuffled-block-sparse-training · GitHub

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
import os

import torch
import torchvision
import torchvision.transforms as transforms
from torchvision.datasets import ImageFolder
from torch.utils.data import DataLoader, Subset
import numpy as np


def data_loader(root, batch_size=128):
    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 = torchvision.datasets.CIFAR10(root=root, train=True, download=True, transform=transform_train)
    trainloader = torch.utils.data.DataLoader(trainset, batch_size, shuffle=True, num_workers=2)
    testset = torchvision.datasets.CIFAR10(root=root, train=False, download=True, transform=transform_test)
    testloader = torch.utils.data.DataLoader(testset, batch_size=100, shuffle=False, num_workers=2)

    return trainloader, testloader


def data_loader_cifar100(root, batch_size=128):
    cifar_mean = (0.5070751592371323, 0.48654887331495095, 0.4409178433670343)
    cifar_std = (0.2673342858792401, 0.2564384629170883, 0.27615047132568404)

    transform_train = transforms.Compose([
        transforms.RandomCrop(32, padding=4),
        transforms.RandomHorizontalFlip(),
        transforms.ToTensor(),
        transforms.Normalize(cifar_mean, cifar_std),
    ])

    transform_test = transforms.Compose([
        transforms.ToTensor(),
        transforms.Normalize(cifar_mean, cifar_std),
    ])

    trainset = torchvision.datasets.CIFAR100(root=root, train=True, download=True,
                                             transform=transform_train)
    train_loader = torch.utils.data.DataLoader(trainset, batch_size, shuffle=True, num_workers=2)

    testset = torchvision.datasets.CIFAR100(root=root, train=False, download=True, transform=transform_test)
    test_loader = torch.utils.data.DataLoader(testset, batch_size, shuffle=False, num_workers=2)

    return train_loader, test_loader

def imagenet_dataloaders(root, batch_size, workers=4):
    traindir = os.path.join(root, 'train')
    valdir = os.path.join(root, 'val')
    normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
                                     std=[0.229, 0.224, 0.225])

    train_dataset = ImageFolder(
        traindir,
        transforms.Compose([
            transforms.RandomResizedCrop(224),
            transforms.RandomHorizontalFlip(),
            transforms.ToTensor(),
            normalize
        ])
    )
    val_dataset = ImageFolder(
        valdir,
        transforms.Compose([
            transforms.Resize(256),
            transforms.CenterCrop(224),
            transforms.ToTensor(),
            normalize
        ])
    )

    train_sampler = torch.utils.data.distributed.DistributedSampler(train_dataset)
    train_loader = torch.utils.data.DataLoader(
        train_dataset,
        batch_size=batch_size,
        shuffle=(train_sampler is None),
        num_workers=workers,
        sampler=train_sampler,
        pin_memory = False
    )
    val_sampler = torch.utils.data.distributed.DistributedSampler(val_dataset)
    val_loader = torch.utils.data.DataLoader(
        val_dataset,
        batch_size=batch_size,
        shuffle=(val_sampler is None),
        num_workers=workers,
        sampler = val_sampler,
        pin_memory = False
    )
    return train_loader, val_loader

def tiny_imagenet_dataloaders(data_dir,batch_size=64,  dataset=False, split_file=None):

    train_transform = transforms.Compose([
        transforms.RandomRotation(20),
        #transforms.RandomCrop(64, padding=4),
        transforms.RandomHorizontalFlip(),
        transforms.ToTensor(),
    ])

    test_transform = transforms.Compose([
        transforms.ToTensor(),
    ])

    train_path = os.path.join(data_dir, 'train')
    val_path = os.path.join(data_dir, 'val')

    if not split_file:
        split_file = 'npy_files/tiny-imagenet-train-val.npy'
    split_permutation = list(np.load(split_file))

    train_set = Subset(ImageFolder(train_path, transform=train_transform), split_permutation[:90000])
    val_set = Subset(ImageFolder(train_path, transform=test_transform), split_permutation[90000:])
    test_set = ImageFolder(val_path, transform=test_transform)

    train_loader = DataLoader(train_set, batch_size=batch_size, shuffle=True, num_workers=8, pin_memory=True)
    val_loader = DataLoader(val_set, batch_size=batch_size, shuffle=False, num_workers=2, pin_memory=True)
    test_loader = DataLoader(test_set, batch_size=batch_size, shuffle=False, num_workers=2, pin_memory=True)

    if dataset:
        print('return train dataset')
        train_dataset = ImageFolder(train_path, transform=train_transform)
        return train_dataset, val_loader, test_loader
    else:
        return train_loader, val_loader, test_loader