-
Notifications
You must be signed in to change notification settings - Fork 48
/
utils.py
executable file
·99 lines (72 loc) · 2.52 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
import logging
import os
import shutil
import numpy as np
import torch
import torch.nn as nn
from torch.autograd import Variable
class AvgrageMeter(object):
def __init__(self):
self.reset()
def reset(self):
self.avg = 0
self.sum = 0
self.cnt = 0
def update(self, val, n=1):
self.cur = val
self.sum += val * n
self.cnt += n
self.avg = self.sum / self.cnt
def accuracy(output, target, topk=(1, 5)):
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 count_parameters_in_MB(model):
return np.sum(np.prod(v.size()) for name, v in model.named_parameters() if "aux" not in name)/1e6
def save_checkpoint(state, is_best, save):
filename = os.path.join(save, 'checkpoint.pth.tar')
torch.save(state, filename)
if is_best:
best_filename = os.path.join(save, 'model_best.pth.tar')
shutil.copyfile(filename, best_filename)
def save(model, model_path):
torch.save(model.state_dict(), model_path)
def load_net_config(path):
with open(path, 'r') as f:
return f.readline()
def load_model(model, model_path):
logging.info('Start loading the model from ' + model_path)
model.load_state_dict(torch.load(model_path))
logging.info('Loading the model finished!')
def drop_path(x, drop_prob):
if drop_prob > 0.:
keep_prob = 1.-drop_prob
mask = Variable(torch.cuda.FloatTensor(x.size(0), 1, 1, 1).bernoulli_(keep_prob))
x.div_(keep_prob)
x.mul_(mask)
return x
def create_exp_dir(path):
if not os.path.exists(path):
os.mkdir(path)
print('Experiment dir : {}'.format(path))
def cross_entropy_with_label_smoothing(pred, target, label_smoothing=0.):
"""
Label smoothing implementation.
This function is taken from https://github.com/MIT-HAN-LAB/ProxylessNAS/blob/master/proxyless_nas/utils.py
"""
logsoftmax = nn.LogSoftmax().cuda()
n_classes = pred.size(1)
# convert to one-hot
target = torch.unsqueeze(target, 1)
soft_target = torch.zeros_like(pred)
soft_target.scatter_(1, target, 1)
# label smoothing
soft_target = soft_target * (1 - label_smoothing) + label_smoothing / n_classes
return torch.mean(torch.sum(- soft_target * logsoftmax(pred), 1))