/
training.py
executable file
·119 lines (78 loc) · 3.47 KB
/
training.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
import torch
import torch.optim
from net import mainNet
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
def train(dataloader_source, dataloader_target):
model = mainNet().to(device)
optimizer = torch.optim.Adam(model.parameters(), lr=1e-5)
criteria1 = torch.nn.CrossEntropyLoss()
criteria2 = torch.nn.MSELoss()
num_ep = 1
for epoch in range (num_ep):
model.train()
running_loss = 0
len_dataloader = min(len(dataloader_source), len(dataloader_target))
data_source_iter = iter(dataloader_source)
data_target_iter = iter(dataloader_target)
i = 0
while i < len_dataloader:
data_source = data_source_iter.next()
inputs, lbl, li = data_source
inputs = inputs.cuda()
lbl = lbl.cuda()
li = li.cuda()
lbl_f = torch.Tensor.float(lbl)
lbl_size = len(lbl)
optimizer.zero_grad()
dom_label = torch.zeros(lbl_size).long().to(device)
fout = model(inputs)[0]
dout = model(inputs)[1]
data_target = data_target_iter.next()
inputs_t, lbl_t = data_target
inputs_t = inputs_t.cuda()
lbl_t = lbl_t.cuda()
lbl__tf = torch.Tensor.float(lbl_t)
lbl_size_t = len(lbl_t)
dom_label_t = torch.ones(lbl_size_t).long().to(device)
dout_t = model(inputs_t)[1]
loss = criteria1(dout, dom_label) + criteria2(fout, li) + criteria1(dout_t, dom_label_t)
loss.backward()
optimizer.step()
running_loss += loss.item()
if i%50 == 0:
print(epoch, i, loss.item())
i += 1
torch.save(model, 'model-'+str(epoch)+'.pth')
return model
def train_deep(dataloader_source):
model = mainNet().to(device)
optimizer = torch.optim.Adam(model.parameters(), lr=1e-5)
criteria2 = torch.nn.MSELoss()
num_ep = 1
for epoch in range (num_ep):
model.train()
running_loss = 0
# len_dataloader = min(len(dataloader_source), len(dataloader_target))
len_dataloader = len(dataloader_source)
data_source_iter = iter(dataloader_source)
# data_target_iter = iter(dataloader_target)
i = 0
while i < len_dataloader:
data_source = data_source_iter.next()
inputs, lbl, li = data_source
inputs = inputs.cuda()
lbl = lbl.cuda()
li = li.cuda()
lbl_f = torch.Tensor.float(lbl)
lbl_size = len(lbl)
optimizer.zero_grad()
fout = model(inputs)[0]
loss = criteria2(fout, li)
loss.backward()
optimizer.step()
running_loss += loss.item()
if i%50 == 0:
print(epoch, i, loss.item())
i += 1
torch.save(model, 'deep-model-'+str(epoch)+'.pth')
return model