-
Notifications
You must be signed in to change notification settings - Fork 1
/
Copy pathtest.py
197 lines (171 loc) · 6.45 KB
/
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
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
import time
import os
import scipy.io as sio
from options.test_options import TestOptions
opt = TestOptions().parse() # set CUDA_VISIBLE_DEVICES before import torch
from data.data_loader import CreateDataLoader
from models.models import create_model
from util.visualizer import Visualizer
from pdb import set_trace as st
from util import html
import numpy as np
from sklearn.metrics import mean_squared_error
from math import sqrt
import matplotlib.pyplot as plt
import util.util as util
import torch
import scipy.io as sio
from sklearn.cluster import KMeans
import models.metrics as metrics
import matplotlib.pyplot as plt
opt.nThreads = 1 # test code only supports nThreads=1
opt.batchSize = 1 #test code only supports batchSize=1
opt.serial_batches = True # no shuffle
#load data
# Load data
data_loader = CreateDataLoader(opt)
dataset_paired, paired_dataset_size = data_loader.load_data_pair()
dataset_unpaired, unpaired_dataset_size = data_loader.load_data_unpair()
train_dataset_a = dataset_paired.dataset.train_data_a
train_dataset_b = dataset_paired.dataset.train_data_b
test_dataset_a = dataset_paired.dataset.test_data_a
test_dataset_b = dataset_paired.dataset.test_data_b
untrain_dataset_a = dataset_unpaired.dataset.train_data_a
untrain_dataset_b = dataset_unpaired.dataset.train_data_b
#sio.savemat('ua.mat',{'upaireda':untrain_dataset_a})
#sio.savemat('ub.mat',{'upairedb':untrain_dataset_b})
data_0 = sio.loadmat('rand10/label.mat')
data_dict=dict(data_0)
data0 = data_dict['label']
label_true = np.zeros((len(train_dataset_a)))
for i in range(len(train_dataset_a)):
label_true[i]=data0[i]
label_true_all = np.zeros(len(train_dataset_a)+2*len(untrain_dataset_a))
for i in range(len(train_dataset_a)+2*len(untrain_dataset_a)):
label_true_all[i]=data0[i]
label_true_UN = np.zeros(2*len(untrain_dataset_a))
for i in range(2*len(untrain_dataset_a)):
label_true_UN[i]=data0[i+len(train_dataset_a)]
model = create_model(opt)
visualizer = Visualizer(opt)
n_clusters = 5
n_com = 100
dim1 = 1750
dim2 = 79
# create website
web_dir = os.path.join(opt.results_dir, opt.name, '%s_%s' % (opt.phase, opt.which_epoch))
webpage = html.HTML(web_dir, 'Experiment = %s, Phase = %s, Epoch = %s' % (opt.name, opt.phase, opt.which_epoch))
ACC_all=[]
NMI_all=[]
fa_500 = []
fb_500 = []
for i in range(len(untrain_dataset_a)):
tempimage_a = torch.from_numpy(untrain_dataset_a[i]).view(1,1,dim1)
tempimage_b = torch.from_numpy(untrain_dataset_b[i]).view(1,1,dim2)
model.set_input(tempimage_a, tempimage_b)
dataset_fakeA, dataset_fakeB, t1_200, t2_200 = model.test_unpaired()
data_fakeA = dataset_fakeA.data.view(1,dim1).tolist()
data_fakeB = dataset_fakeB.data.view(1,dim2).tolist()
fa_500.append(data_fakeA)
fb_500.append(data_fakeB)
test_dataset_A2000 = np.array(list(train_dataset_a) + list(untrain_dataset_a) + list(fa_500))
test_dataset_B2000 = np.array(list(train_dataset_b) + list(fb_500) + list(untrain_dataset_b))
sio.savemat('fakea2.mat',{'fa':fa_500})
sio.savemat('fakeb2.mat',{'fb':fb_500})
commonZ_step2 = []
for i in range(len(test_dataset_A2000)):
tempimage_a = torch.from_numpy(test_dataset_A2000[i]).view(1,1,dim1)
tempimage_b = torch.from_numpy(test_dataset_B2000[i]).view(1,1,dim2)
model.set_input(tempimage_a, tempimage_b)
t_200 =np.array(model.test_commonZ().data.view(n_com).tolist())
commonZ_step2.append(t_200)
estimator = KMeans(n_clusters)
estimator.fit(commonZ_step2)
centroids_step2 =estimator.cluster_centers_
label_pred = estimator.labels_
acc = metrics.acc(label_true_all, label_pred)
nmi = metrics.nmi(label_true_all, label_pred)
ACC_all.append(acc)
NMI_all.append(nmi)
print(' '*8 + '|==> acc: %.4f, nmi: %.4f <==|'
% (acc, nmi))
sio.savemat('commonZ.mat', {'commonZ':commonZ_step2})
# test
#groundtruth = []
#rmse_x = []
#rmse_y = []
#fa_600 = []
#fb_600 = []
#print(len(untrain_dataset_a))
#print(len(test_dataset_a))
#for i in range(len(untrain_dataset_a)):
# #groundtruth.append(lab_a)
# #print("THis is the ith " , i)
# #print(untrain_dataset_a)
# images_a = torch.from_numpy(untrain_dataset_a[i]).view(1,1,1750)
# images_b = torch.from_numpy(untrain_dataset_b[i]).view(1,1,79)
# #groundtruth.append(lab_a)
# model.set_input(images_a, images_b)
# dataset_fakeA, dataset_fakeB = model.test_unpaired()
# data_fakeA = dataset_fakeA.data.view(1,1750).tolist()
# data_fakeB = dataset_fakeB.data.view(1,79).tolist()
# fa_600.append(data_fakeA)
# fb_600.append(data_fakeB)
#
# #visuals = model.get_current_visuals()
# #img_path = 'image'+ str(i)
# #print('process image... %s' % img_path)
# #visualizer.save_images(webpage, visuals, img_path)
#############################
#test_dataset_A2400 = np.array(list(test_dataset_a) + list(untrain_dataset_a) + list(fa_600) )
#test_dataset_B2400 = np.array(list(test_dataset_b) + list(fb_600) + list(untrain_dataset_b) )
#
##test_dataset_A2000 = np.array(list(train_dataset_a))
##test_dataset_B2000 = np.array(list(train_dataset_b))
#################################
##for i in range(len(test_dataset_A2000)):
## print(test_dataset_A2000[i])
## print(test_dataset_B2000[i])
#
#
##print(test_dataset_2000)
##print('test_dataset_2000')
#
##for i, (images_a, images_b) in enumerate(dataset_paired):
## print(images_a)
## print(torch.from_numpy(test_dataset_A2000[0]).view(1,1,28,28))
#commonZ = []
#for i in range(len(test_dataset_A2400)):
# image_a = torch.from_numpy(test_dataset_A2400[i]).view(1,1,1750)
# print(i)
# image_b = torch.from_numpy(test_dataset_B2400[i]).view(1,1,79)
# #groundtruth.append(lab_a)
# #print("THis is the ith " , i)
# #print(untrain_dataset_a)
#
# model.set_input(image_a, image_b)
# t_200 =np.array(model.test_commonZ().data.tolist())
# commonZ.append(t_200)
#
## visuals = model.get_current_visuals()
## img_path = 'image'+ str(i)
## print('process image... %s' % img_path)
## visualizer.save_images(webpage, visuals, img_path)
#
##print(commonZ)
####################################
#sio.savemat('commonZ.mat',{'Z':commonZ})
#sio.savemat('databaseA.mat',{'dataA':test_dataset_A2400})
#sio.savemat('databaseB.mat',{'dataB':test_dataset_B2400})
#sio.savemat('fakea.mat',{'fa':fa_600})
#sio.savemat('fakeb.mat',{'fb':fb_600})
######################################
#
#
#
#
##sio.savemat('lab.mat',{'groundtruth':groundtruth})
##print(np.mean(rmse_y))
##print(np.mean(rmse_x))
##print((np.mean(rmse_x)+np.mean(rmse_y))/2)
##webpage.save()