/
utils.py
179 lines (147 loc) · 6.68 KB
/
utils.py
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
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
# -*- coding: utf-8 -*-
import copy
import torch
import torch.nn.functional as F
import torch.nn.parallel
import torch.backends.cudnn as cudnn
import torch.optim
import torch.utils.data
import torchvision.transforms as transforms
import random
import numpy as np
from resnet import ResNet32
from load_corrupted_data import CIFAR10, CIFAR100
def make_onehot_label(targets, num_class):
return torch.zeros(targets.shape[0], num_class, device=targets.device).scatter_(1, targets.reshape(-1, 1), 1)
def cross_entropy_with_soft_label(logits, targets):
return -torch.mean(torch.sum(F.log_softmax(logits, dim=1) * targets, dim=1))
def set_seed(seed):
torch.cuda.manual_seed(seed)
torch.manual_seed(seed)
np.random.seed(seed)
random.seed(seed)
cudnn.deterministic = True
cudnn.benchmark = False
def build_dataset(args):
normalize = transforms.Normalize(mean=[x / 255.0 for x in [125.3, 123.0, 113.9]],
std=[x / 255.0 for x in [63.0, 62.1, 66.7]])
if args.augment:
train_transform = transforms.Compose([
transforms.ToTensor(),
transforms.Lambda(lambda x: F.pad(x.unsqueeze(0),
(4, 4, 4, 4), mode='reflect').squeeze()),
transforms.ToPILImage(),
transforms.RandomCrop(32),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
normalize,
])
else:
train_transform = transforms.Compose([
transforms.ToTensor(),
normalize,
])
test_transform = transforms.Compose([
transforms.ToTensor(),
normalize
])
if args.dataset == 'cifar10':
train_data_meta = CIFAR10(
root='./data', train=True, meta=True, num_meta=args.num_meta, corruption_prob=args.corruption_prob,
corruption_type=args.corruption_type, transform=train_transform, download=True)
train_data = CIFAR10(
root='./data', train=True, meta=False, num_meta=args.num_meta, corruption_prob=args.corruption_prob,
corruption_type=args.corruption_type, transform=train_transform, download=True, seed=args.seed)
test_data = CIFAR10(root='./data', train=False, transform=test_transform, download=True)
elif args.dataset == 'cifar100':
train_data_meta = CIFAR100(
root='./data', train=True, meta=True, num_meta=args.num_meta, corruption_prob=args.corruption_prob,
corruption_type=args.corruption_type, transform=train_transform, download=True)
train_data = CIFAR100(
root='./data', train=True, meta=False, num_meta=args.num_meta, corruption_prob=args.corruption_prob,
corruption_type=args.corruption_type, transform=train_transform, download=True, seed=args.seed)
test_data = CIFAR100(root='./data', train=False, transform=test_transform, download=True)
train_loader = torch.utils.data.DataLoader(
train_data, batch_size=args.batch_size, shuffle=True,
num_workers=args.prefetch, pin_memory=True)
train_meta_loader = torch.utils.data.DataLoader(
train_data_meta, batch_size=args.batch_size, shuffle=True,
num_workers=args.prefetch, pin_memory=True)
test_loader = torch.utils.data.DataLoader(test_data, batch_size=args.batch_size, shuffle=False,
num_workers=args.prefetch, pin_memory=True)
if args.dataset == 'cifar10':
num_classes = 10
elif args.dataset == 'cifar100':
num_classes = 100
data_list = {}
for j in range(num_classes):
data_list[j] = [i for i, label in enumerate(train_loader.dataset.train_labels) if label == j]
img_num_list = get_img_num_per_cls(args.dataset, args.imb_factor, args.num_meta)# * num_classes)
print(img_num_list)
print(sum(img_num_list))
im_data = {}
idx_to_del = []
for cls_idx, img_id_list in data_list.items():
random.shuffle(img_id_list)
img_num = img_num_list[int(cls_idx)]
im_data[cls_idx] = img_id_list[img_num:]
idx_to_del.extend(img_id_list[img_num:])
print(len(idx_to_del))
imbalanced_train_dataset = copy.deepcopy(train_data)
imbalanced_train_dataset.train_labels = np.delete(train_loader.dataset.train_labels, idx_to_del, axis=0)
imbalanced_train_dataset.train_data = np.delete(train_loader.dataset.train_data, idx_to_del, axis=0)
imbalanced_train_dataset.clabels = np.delete(train_loader.dataset.clabels, idx_to_del, axis=0)
imbalanced_train_loader = torch.utils.data.DataLoader(
imbalanced_train_dataset, batch_size=args.batch_size, shuffle=True, num_workers=args.prefetch, pin_memory=True)
return imbalanced_train_loader, train_meta_loader, test_loader
def build_model(args):
model = ResNet32(args.dataset == 'cifar10' and 10 or 100)
if torch.cuda.is_available():
model.cuda()
torch.backends.cudnn.benchmark = True
return model
def accuracy(output, target, topk=(1,)):
"""Computes the precision@k for the specified values of k"""
maxk = max(topk)
batch_size = target.size(0)
_, pred = output.topk(maxk, 1, True, True)
pred = pred.t()
correct = pred.eq(target.view(1, -1).expand_as(pred))
res = []
for k in topk:
correct_k = correct[:k].view(-1).float().sum(0)
res.append(correct_k.mul_(100.0 / batch_size))
return res
def adjust_learning_rate(args, optimizer, epochs):
lr = args.lr * ((0.1 ** int(epochs >= 80)) * (0.1 ** int(epochs >= 90))) # For WRN-28-10
lr = args.lr * (1 + np.cos(np.pi * epochs * 1.0 / (args.epochs * 1.0))) / 2.0
print("lr {}".format(lr))
for param_group in optimizer.param_groups:
param_group['lr'] = lr
def get_img_num_per_cls(dataset,imb_factor=None,num_meta=None):
"""
Get a list of image numbers for each class, given cifar version
Num of imgs follows emponential distribution
img max: 5000 / 500 * e^(-lambda * 0);
img min: 5000 / 500 * e^(-lambda * int(cifar_version - 1))
exp(-lambda * (int(cifar_version) - 1)) = img_max / img_min
args:
cifar_version: str, '10', '100', '20'
imb_factor: float, imbalance factor: img_min/img_max,
None if geting default cifar data number
output:
img_num_per_cls: a list of number of images per class
"""
if dataset == 'cifar10':
img_max = (50000-num_meta)/10
cls_num = 10
if dataset == 'cifar100':
img_max = (50000-num_meta)/100
cls_num = 100
if imb_factor is None:
return [img_max] * cls_num
img_num_per_cls = []
for cls_idx in range(cls_num):
num = img_max * (imb_factor**(cls_idx / (cls_num - 1.0)))
img_num_per_cls.append(int(num))
return img_num_per_cls