-
Notifications
You must be signed in to change notification settings - Fork 3
/
Copy pathutils.py
203 lines (174 loc) · 7.02 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
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
import os
import re
import torch
import torch.nn as nn
import torchvision.transforms as transforms
import torchvision.datasets as torchdata
import agent_net
from spottune_models import *
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
def adjust_learning_rate_net(optimizer, epoch, args):
"""Sets the learning rate to the initial LR decayed by 10 every X epochs"""
if epoch >= args.step3:
lr = args.lr * 0.001
elif epoch >= args.step2:
lr = args.lr * 0.01
elif epoch >= args.step1:
lr = args.lr * 0.1
else:
lr = args.lr
for param_group in optimizer.param_groups:
param_group['lr'] = lr
def adjust_learning_rate_agent(optimizer, epoch, args):
"""Sets the learning rate to the initial LR decayed by 10 every X epochs"""
if epoch >= args.step3:
lr = args.lr_agent * 0.001
elif epoch >= args.step2:
lr = args.lr_agent * 0.01
elif epoch >= args.step1:
lr = args.lr_agent * 0.1
else:
lr = args.lr_agent
for param_group in optimizer.param_groups:
param_group['lr'] = lr
'''
def adjust_learning_rate(optimizer, epoch, lr_decay, lr_decay1, decay_value):
"""Sets the learning rate to the initial LR decayed by 10 every 30 epochs"""
for i, param_group in enumerate(optimizer.param_groups):
# print(param_group['params'])
lr = param_group['lr']
# name = param_group['name']
#print(i)
#print(lr)
#print(lr_decay[0])
#print(lr_decay[1])
#print(lr_decay[2])
# if((not(name.split('.')[1].isdigit())) or (int(name.split('.')[1])>=(21-high_lr_layers))):
#print(name)
#print('hi')
if(epoch>(lr_decay[0] + lr_decay[1] + 1)):
#print('hi1')
#print(int(round((epoch-(lr_decay[0] + lr_decay[1]))%lr_decay[2])))
if(int(round(((epoch-(lr_decay[0] + lr_decay[1]))%lr_decay[2]))) == 1):
#print('epoch: ' + str(epoch))
#print(lr)
#print(decay_value[2])
lr = param_group['lr']*decay_value[2]
# print(name)
print(lr)
elif(epoch>(lr_decay[0]+1)):
#print('hi2')
#print(int(round((epoch-(lr_decay[0]))%lr_decay[1])))
if(int(round((epoch-(lr_decay[0]))%lr_decay[1])) == 1):
#print('epoch: ' + str(epoch))
lr = param_group['lr']*decay_value[1]
# print(name)
print(lr)
else:
#print('hi3')
#print(int(round(epoch%lr_decay[0])))
if(int(round(epoch%lr_decay[0])) == 1):
lr = param_group['lr']*decay_value[0]
# print(name)
print(lr)
## else:
# if(epoch>(lr_decay[0] + lr_decay[1] + 1)):
# if(int(round(((epoch-(lr_decay[0] + lr_decay[1]))%lr_decay[2])) == 1)):
# #print('epoch: ' + str(epoch))
# lr = param_group['lr']*decay_value[2]
## print(name)
# print(lr)
# elif(epoch>(lr_decay[0]+1)):
# if((int(round((epoch-(lr_decay[0]))%lr_decay[1])) == 1)):
# #print('epoch: ' + str(epoch))
# lr = param_group['lr']*decay_value[1]
## print(name)
# print(lr)
param_group['lr'] = lr
#'''
def load_weights_to_flatresnet(source, net, agent, dataset):
checkpoint = torch.load(source)
net_old = checkpoint['net']
store_data = []
for name, m in net_old.named_modules():
if isinstance(m, nn.Conv2d):
store_data.append(m.weight.data)
element = 0
for name, m in net.named_modules():
if isinstance(m, nn.Conv2d):
m.weight.data = torch.nn.Parameter(store_data[element].clone())
element += 1
store_data = []
store_data_bias = []
store_data_rm = []
store_data_rv = []
for name, m in net_old.named_modules():
if isinstance(m, nn.BatchNorm2d):
store_data.append(m.weight.data)
store_data_bias.append(m.bias.data)
store_data_rm.append(m.running_mean)
store_data_rv.append(m.running_var)
element = 0
for name, m in net.named_modules():
if isinstance(m, nn.BatchNorm2d):
m.weight.data = torch.nn.Parameter(store_data[element].clone())
m.bias.data = torch.nn.Parameter(store_data_bias[element].clone())
m.running_var = store_data_rv[element].clone()
m.running_mean = store_data_rm[element].clone()
element += 1
agent_old = checkpoint['agent']
store_data = []
for name, m in agent_old.named_modules():
if isinstance(m, nn.Conv2d):
store_data.append(m.weight.data)
element = 0
for name, m in agent.named_modules():
if isinstance(m, nn.Conv2d):
m.weight.data = torch.nn.Parameter(store_data[element].clone())
element += 1
store_data = []
store_data_bias = []
store_data_rm = []
store_data_rv = []
for name, m in agent_old.named_modules():
if isinstance(m, nn.BatchNorm2d):
store_data.append(m.weight.data)
store_data_bias.append(m.bias.data)
store_data_rm.append(m.running_mean)
store_data_rv.append(m.running_var)
element = 0
for name, m in agent.named_modules():
if isinstance(m, nn.BatchNorm2d):
m.weight.data = torch.nn.Parameter(store_data[element].clone())
m.bias.data = torch.nn.Parameter(store_data_bias[element].clone())
m.running_var = store_data_rv[element].clone()
m.running_mean = store_data_rm[element].clone()
element += 1
agent.linear.weight.data = torch.nn.Parameter(agent_old.module.linear.weight.data.clone())
agent.linear.bias.data = torch.nn.Parameter(agent_old.module.linear.bias.data.clone())
net.linear.weight.data = torch.nn.Parameter(net_old.module.linear.weight.data.clone())
net.linear.bias.data = torch.nn.Parameter(net_old.module.linear.bias.data.clone())
del net_old
del agent_old
return net, agent
def get_net_and_agent(model, num_class, dataset = None):
if model == 'resnet26':
if dataset is not None:
source = '../cv/' + dataset + '/' + dataset + '.t7'
rnet = resnet26(num_class)
agent = agent_net.resnet(sum(rnet.layer_config))
rnet, agent = load_weights_to_flatresnet(source, rnet, agent, dataset)
return rnet, agent