-
Notifications
You must be signed in to change notification settings - Fork 2
/
util.py
70 lines (53 loc) · 1.7 KB
/
util.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
import torch
import torch.nn.functional as F
class Surrogate_BP_Function(torch.autograd.Function):
@staticmethod
def forward(ctx, input):
ctx.save_for_backward(input)
out = torch.zeros_like(input).cuda()
out[input > 0] = 1.0
return out
@staticmethod
def backward(ctx, grad_output):
(input,) = ctx.saved_tensors
grad_input = grad_output.clone()
grad = grad_input * 0.3 * F.threshold(1.0 - torch.abs(input), 0, 0)
return grad
import torch
def adjust_learning_rate(optimizer, cur_epoch, max_epoch):
if (
cur_epoch == (max_epoch * 0.5)
or cur_epoch == (max_epoch * 0.7)
or cur_epoch == (max_epoch * 0.9)
):
for param_group in optimizer.param_groups:
param_group["lr"] /= 10
def accuracy(outp, target, topk=(1,)):
"""Computes the precision@k for the specified values of k"""
with torch.no_grad():
maxk = max(topk)
batch_size = target.size(0)
_, pred = outp.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].reshape(-1).float().sum(0, keepdim=True)
res.append(correct_k.mul_(100.0 / batch_size))
return res
class AverageMeter(object):
"""
Computes and stores the average and current value
"""
def __init__(self):
self.reset()
def reset(self):
self.val = 0
self.avg = 0
self.sum = 0
self.count = 0
def update(self, val, n=1):
self.val = val
self.sum += val * n
self.count += n
self.avg = self.sum / self.count