-
-
Notifications
You must be signed in to change notification settings - Fork 1
/
pr6130.test.py
98 lines (80 loc) · 3.23 KB
/
pr6130.test.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
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
-------------------------------------------------
Description : PR 6130 test
1. https://github.com/pytorch/pytorch/pull/6130#issuecomment-378699147
Email : autuanliu@163.com
Date：2018/04/01
"""
from models.utils.utils_imports import *
from models.vislib.line_plot import line
class Net(nn.Module):
def __init__(self):
super().__init__()
self.conv1 = nn.Conv2d(1, 1, 1)
self.conv2 = nn.Conv2d(1, 1, 1)
def forward(self, x):
return self.conv2(F.relu(self.conv1(x)))
class Cos(lr_scheduler._LRScheduler):
def __init__(self, optimizer, T_max, eta_min=0, T_mult=2, last_epoch=-1):
self.T_max = T_max
self.Ti = T_max
self.eta_min = eta_min
self.T_mult = T_mult
self.cycle = 0
super().__init__(optimizer, last_epoch)
def step(self, epoch=None):
if epoch is None:
epoch = self.last_epoch + 1
if epoch == self.Ti:
epoch = 0
self.cycle += 1
else:
self.cycle = int(math.floor(math.log(epoch / self.T_max * (self.T_mult - 1) + 1, self.T_mult)))
epoch -= sum([self.T_max * self.T_mult**x for x in range(self.cycle)])
self.last_epoch = epoch
self.Ti = self.T_max * self.T_mult**self.cycle
for param_group, lr in zip(self.optimizer.param_groups, self.get_lr()):
param_group['lr'] = lr
def get_lr(self):
return [self.eta_min + (base_lr - self.eta_min) * (1 + math.cos(math.pi * self.last_epoch / self.Ti)) / 2 for base_lr in self.base_lrs]
net = Net()
opt = optim.SGD([{'params': net.conv1.parameters()}, {'params': net.conv2.parameters(), 'lr': 0.5}], lr=0.05)
epochs = 10
eta_min = 1e-10
T_mult = 3
T_max = 1
T_cur = list(range(T_max)) + list(range(T_max * T_mult)) + list(range(T_max * T_mult * T_mult))
T_i = [T_max] * T_max + [T_max * T_mult] * T_max * T_mult + [T_max * T_mult * T_mult] * T_max * T_mult * T_mult
single_targets = [eta_min + (0.05 - eta_min) * (1 + math.cos(math.pi * x / y)) / 2 for x, y in zip(T_cur, T_i)]
targets = [single_targets, list(map(lambda x: x * 10, single_targets))]
scheduler = Cos(opt, T_max=T_max, eta_min=1e-10, T_mult=T_mult)
# without epoch args
# epochs = 10
# eta_min = 1e-10
# single_targets = [eta_min + (0.05 - eta_min) * (1 + math.cos(math.pi * x / epochs)) / 2
# for x in range(epochs)]
# targets = [single_targets, list(map(lambda x: x * 10, single_targets))]
# scheduler = Cos(opt, T_max=epochs, eta_min=eta_min)
# print(targets, '\n\n')
# vis_data = []
# vis_data1 = []
# for epoch in range(10):
# scheduler.step()
# print(scheduler.get_lr())
# vis_data.append(scheduler.get_lr()[0])
# vis_data1.append(scheduler.get_lr()[1])
# opt.step()
# line(vis_data)
# line(vis_data1)
# plt.show()
def test(scheduler, targets, epochs=10):
for epoch in range(epochs):
scheduler.step(epoch)
print('epoch: ', epoch, '\n')
print('post: ', scheduler.last_epoch, scheduler.Ti, '\n')
for param_group, target in zip(opt.param_groups, targets):
print("target: ", target[epoch], '\n')
print('ac lr: ', param_group['lr'], '\n')
test(scheduler, targets, epochs=10)