-
Notifications
You must be signed in to change notification settings - Fork 4
/
train.py
158 lines (124 loc) · 6.89 KB
/
train.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
import torch
import torch.nn as nn
from utils import weight_init
from RNet import RelationNetWork
from embed import CNNEncoder
from MiniImagenet2 import MiniImagenet
from torch.utils.data import DataLoader
from torch.autograd import Variable
import os
import numpy as np
from logger import Logger
LOG_DIR = './log'
logger = Logger(LOG_DIR)
os.environ['CUDA_VISIBLE_DEVICES'] = '4'
def main():
n_way = 5
k_shot = 1
k_query = 15
batchsz = 5
best_acc = 0
mdfile1 = './ckpy/feature-%d-way-%d-shot.pkl' %(n_way,k_shot)
mdfile2 = './ckpy/relation-%d-way-%d-shot.pkl' %(n_way,k_shot)
feature_embed = CNNEncoder().cuda()
Relation_score = RelationNetWork(64, 8).cuda() # relation_dim == 8 ??
feature_embed.apply(weight_init)
Relation_score.apply(weight_init)
feature_optim = torch.optim.Adam(feature_embed.parameters(), lr=0.001)
relation_opim = torch.optim.Adam(Relation_score.parameters(), lr=0.001)
loss_fn = torch.nn.MSELoss().cuda()
if os.path.exists(mdfile1):
print("load mdfile1...")
feature_embed.load_state_dict(torch.load(mdfile1))
if os.path.exists(mdfile2):
print("load mdfile2...")
Relation_score.load_state_dict(torch.load(mdfile2))
for epoch in range(1000):
mini = MiniImagenet('./mini-imagenet/', mode='train', n_way=n_way, k_shot=k_shot, k_query=k_query, batchsz=1000, resize=84) #38400
db = DataLoader(mini,batch_size=batchsz,shuffle=True,num_workers=4,pin_memory=True) # 64 , 5*(1+15) , c, h, w
mini_val = MiniImagenet('./mini-imagenet/', mode='val', n_way=n_way, k_shot=k_shot, k_query=k_query, batchsz=200, resize=84) #9600
db_val = DataLoader(mini_val,batch_size=batchsz,shuffle=True,num_workers=4,pin_memory=True)
for step,batch in enumerate(db):
support_x = Variable(batch[0]).cuda() # [batch_size, n_way*(k_shot+k_query), c , h , w]
support_y = Variable(batch[1]).cuda()
query_x = Variable(batch[2]).cuda()
query_y = Variable(batch[3]).cuda()
bh,set1,c,h,w = support_x.size()
set2 = query_x.size(1)
feature_embed.train()
Relation_score.train()
support_xf = feature_embed(support_x.view(bh*set1,c,h,w)).view(bh,set1,64,19,19) # 在 test 的 时候 重复
query_xf = feature_embed(query_x.view(bh*set2,c,h,w)).view(bh,set2,64,19,19)
# print("query_f:", query_xf.size())
support_xf = support_xf.unsqueeze(1).expand(bh,set2,set1,64,19,19)
query_xf = query_xf.unsqueeze(2).expand(bh,set2,set1,64,19,19)
comb = torch.cat((support_xf,query_xf),dim=3) # bh,set2,set1,2c,h,w
# print(comb.is_cuda)
# print(comb.view(bh*set2*set1,2*64,19,19).is_cuda)
score = Relation_score(comb.view(bh*set2*set1,2*64,19,19)).view(bh,set2,set1,1).squeeze(3)
support_yf = support_y.unsqueeze(1).expand(bh,set2,set1)
query_yf = query_y.unsqueeze(2).expand(bh,set2,set1)
label = torch.eq(support_yf,query_yf).float()
feature_optim.zero_grad()
relation_opim.zero_grad()
loss = loss_fn(score,label)
loss.backward()
#torch.nn.utils.clip_grad_norm(feature_embed.parameters(),0.5) # 梯度裁剪? 降低学习率?
#torch.nn.utils.clip_grad_norm(Relation_score.parameters(),0.5)
feature_optim.step()
relation_opim.step()
# if step%100==0:
# print("step:",epoch+1,"train_loss: ",loss.data[0])
logger.log_value('{}-way-{}-shot loss:'.format(n_way, k_shot),loss.data[0])
if step%200==0:
print("---------test--------")
total_correct = 0
total_num = 0
accuracy = 0
for j,batch_test in enumerate(db_val):
# if (j%100==0):
# print(j,'-------------')
support_x = Variable(batch_test[0]).cuda()
support_y = Variable(batch_test[1]).cuda()
query_x = Variable(batch_test[2]).cuda()
query_y = Variable(batch_test[3]).cuda()
bh,set1,c,h,w = support_x.size()
set2 = query_x.size(1)
feature_embed.eval()
Relation_score.eval()
support_xf = feature_embed(support_x.view(bh*set1,c,h,w)).view(bh,set1,64,19,19) # 在 test 的 时候 重复
query_xf = feature_embed(query_x.view(bh*set2,c,h,w)).view(bh,set2,64,19,19)
support_xf = support_xf.unsqueeze(1).expand(bh,set2,set1,64,19,19)
query_xf = query_xf.unsqueeze(2).expand(bh,set2,set1,64,19,19)
comb = torch.cat((support_xf,query_xf),dim=3) # bh,set2,set1,2c,h,w
score = Relation_score(comb.view(bh*set2*set1,2*64,19,19)).view(bh,set2,set1,1).squeeze(3)
rn_score_np = score.cpu().data.numpy() # 转numpy cpu
pred = []
support_y_np = support_y.cpu().data.numpy()
for ii,tb in enumerate(rn_score_np):
for jj,tset in enumerate(tb):
sim = []
for way in range(n_way):
sim.append(np.sum(tset[way*k_shot:(way+1)*k_shot]))
idx = np.array(sim).argmax()
pred.append(support_y_np[ii,idx*k_shot]) # 同一个类标签相同 ,注意还有batch维度
# ×k_shot是因为,上一个步用sum将k_shot压缩了
#此时的pred.size = [b.set2]
#print("pred.size=", np.array(pred).shape)
pred = Variable(torch.from_numpy(np.array(pred).reshape(bh,set2))).cuda()
correct = torch.eq(pred,query_y).sum()
total_correct += correct.data[0]
total_num += query_y.size(0)*query_y.size(1)
accuracy = total_correct/total_num
logger.log_value('acc : ',accuracy)
print("epoch:",epoch,"acc:",accuracy)
if accuracy>best_acc:
print("-------------------epoch",epoch,"step:",step,"acc:",accuracy,"---------------------------------------")
best_acc = accuracy
torch.save(feature_embed.state_dict(),mdfile1)
torch.save(Relation_score.state_dict(),mdfile2)
#if step% == 0 and step != 0:
# print("%d-way %d-shot %d batch | epoch:%d step:%d, loss:%f" %(n_way,k_shot,batchsz,epoch,step,loss.cpu().data[0]))
logger.step()
if __name__=='__main__':
main()