-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathutils.py
176 lines (150 loc) · 6.54 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
# -*- coding:utf-8-*-
import os
import math
import shutil #shutil则就是对os中文件操作的补充。--移动 复制 打包 压缩 解压
import logging
import numpy as np
import torch
import torch.utils.data as Data
import torchvision
import torchvision.transforms as transforms
#https://www.cnblogs.com/CJOKER/p/8295272.html
class Logger(object):
def __init__(self,log_file_name,log_level,logger_name):
#第一步，创建一个logger
self.__logger = logging.getLogger(logger_name)
self.__logger.setLevel(log_level)
#第二步，创建一个handler
file_handler = logging.FileHandler(log_file_name)
console_handler = logging.StreamHandler()
#第三步,定义handler的输出格式
formatter = logging.Formatter(
'[%(asctime)s]-[%(filename)s line:%(lineno)d]:%(message)s '
)
file_handler.setFormatter(formatter)
console_handler.setFormatter(formatter)
#第四步,将Hander添加到logger中
self.__logger.addHandler(file_handler)
self.__logger.addHandler(console_handler)
def get_log(self):
return self.__logger
def count_parameters(model):
return sum(p.numel() for p in model.parameters() if p.requires_grad) #numel()函数：返回数组中元素的个数
def data_augmentation(config,is_train=True):
aug = []
if is_train:
#random crop
if config.augmentation.random_crop:
aug.append(transforms.RandomCrop(config.input_size,padding=4))
#horizontal flip
if config.augmentation.random_horizontal_flip:
aug.append(transforms.RandomHorizontalFlip())
aug.append(transforms.ToTensor())
#normalize [-mean/std]
if config.augmentation.normalize:
if config.dataset =='cifar10':
aug.append(transforms.Normalize(
(0.4914,0.4822,0.4465),(0.2023,0.1994,0.2010)
))
else:
aug.append(transforms.Normalize(
(0.5071,0.4867,0.4408),(0.2675,0.2565,0.2761)
))
if is_train and config.augmentation.cutout:
#cutout
aug.append(Cutout(n_holes=config.augmentation.holes,
length=config.augmentation.length))
return aug
class Cutout(object):
def __init__(self,n_holes,length):
self.n_holes = n_holes
self.length = length
def __call__(self, img):
h = img.size(1)
w = img.size(2)
mask = np.ones((h,w),np.float32)
for n in range(self.n_holes):
y = np.random.randint(h)
x = np.random.randint(w)
#np.clip()函数将将数组中的元素限制在a_min, a_max之间
y1 = np.clip(y-self.length//2,0,h) #" / " 表示浮点数除法，返回浮点结果," // " 表示整数除法,返回不大于结果的一个最大的整数
y2 = np.clip(y+self.length//2,0,h)
x1 = np.clip(x-self.length//2,0,w)
x2 = np.clip(x+self.length//2,0,w)
mask[y1:y2, x1:x2] = 0
mask = torch.from_numpy(mask)
mask = mask.expand_as(img) #把一个tensor变成和函数括号内一样形状的tensor，用法与expand（）类似
img = img*mask
return img
def save_checkpoint(state,is_best,filename):
torch.save(state,filename+'.pth.tar')
if is_best:
shutil.copyfile(filename+'.pth.tar',filename+'_best.pth.tar')
def load_checkpoint(path,model,optimizer=None):
if os.path.isfile(path):
logging.info("=== loading checkpoint '{}' ===".format(path))
checkpoint = torch.load(path)
model.load_state_dict(checkpoint['state_dict'],strict=False)
if optimizer != None:
best_prec = checkpoint['best_prec']
last_epoch = checkpoint['last_epoch']
optimizer.load_state_dict(checkpoint['optimizer'])
logging.info("=== load state dict done.also load optimizer from checkpoint'{}'(epoch{})".format(
path,last_epoch+1
))
return best_prec,last_epoch
def get_data_loader(transform_train, transform_test, config):
assert config.dataset == 'cifar10' or config.dataset == 'cifar100'
if config.dataset == 'cifar10':
trainset = torchvision.datasets.CIFAR10(
root=config.data_path, train=True, transform=transform_train, download=True
)
testset = torchvision.datasets.CIFAR10(
root=config.data_path, train=False, transform=transform_test, download=True)
else:
trainset = torchvision.datasets.CIFAR100(
root=config.data_path, train=True, transform=transform_train, download=True
)
testset = torchvision.datasets.CIFAR100(
root=config.data_path, train=False, transform=transform_test, download=True
)
train_loader = Data.DataLoader(
trainset, batch_size=config.batch_size, shuffle=True, num_workers=config.workers)
test_loader = Data.DataLoader(
testset, batch_size=config.test_batch, shuffle=True, num_workers=config.workers)
return train_loader, test_loader
def mixup_data(x,y,alpha,device):
'''Returns mixed inputs,pairs of targets,and lambda'''
if alpha > 0:
lam = np.random.beta(alpha,alpha)
else:
lam = 1
batch_size = x.size()[0]
index = torch.randperm(batch_size).to(device) #返回一个0到n-1的数组
mixed_x = lam*x + (1-lam)*x[index,:]
y_a,y_b = y,y[index]
return mixed_x,y_a,y_b,lam
def mixup_criterion(criterion,pred,y_a,y_b,lam):
return lam*criterion(pred,y_a)+(1-lam)*criterion(pred,y_b)
def get_current_lr(optimizer):
for param_group in optimizer.param_groups:
return param_group['lr']
def adjust_learning_rate(optimizer,epoch,config):
lr = get_current_lr(optimizer)
if config.lr_scheduler.type == 'STEP':
if epoch in config.lr_scheduler.lr_epochs:
lr *= config.lr_scheduler.lr_mults
elif config.lr_scheduler.type == 'COSINE':
ratio = epoch / config.epochs
lr = config.lr_scheduler.min_lr + \
(config.lr_scheduler.base_lr - config.lr_scheduler.min_lr)*\
(1.0+math.cos(math.pi*ratio))/2.0
elif config.lr_scheduler.type == 'HTD':
ratio = epoch / config.epochs
lr = config.lr_schedule.min_lr + \
(config.lr_scheduler.base_lr-config.lr_scheduler.min_lr)*\
(1.0-math.tanh(config.lr_scheduler.lower_bound+
(config.lr_scheduler.upper_bound - config.lr_scheduler.lower_bound)*ratio))/2.0
for param_group in optimizer.param_groups:
param_group['lr'] = lr
return lr