-
Notifications
You must be signed in to change notification settings - Fork 4
/
test_DFMnet.py
145 lines (117 loc) · 5.74 KB
/
test_DFMnet.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
import time
import tensorflow as tf
import scipy.io as sio
import numpy as np
from scipy.spatial import cKDTree
flags = tf.app.flags
FLAGS = flags.FLAGS
flags.DEFINE_integer('num_evecs', 120,
'number of eigenvectors used for representation')
flags.DEFINE_integer('num_model', 5000, '')
flags.DEFINE_string('test_shapes_dir', './Shapes/', '')
flags.DEFINE_string('files_name', 'tr_reg_', 'name common to all the shapes')
flags.DEFINE_string('log_dir', './Testing/',
'directory to save targets results')
flags.DEFINE_string('matches_dir', './Matches/',
'directory to matches')
def get_test_pair_source(source_fname):
input_data = {}
source_file = '%s%s.mat' % (FLAGS.test_shapes_dir, source_fname)
# This loads the source but with a target name so next lines re-names
input_data.update(sio.loadmat(source_file))
input_data['source_evecs'] = input_data['target_evecs']
del input_data['target_evecs']
input_data['source_evecs_trans'] = input_data['target_evecs_trans']
del input_data['target_evecs_trans']
input_data['source_shot'] = input_data['target_shot']
del input_data['target_shot']
input_data['source_evals'] = np.transpose(input_data['target_evals'])
del input_data['target_evals']
return input_data
def get_test_pair_target(target_fname):
input_data = {}
target_file = '%s%s.mat' % (FLAGS.test_shapes_dir, target_fname)
input_data.update(sio.loadmat(target_file))
input_data['target_evals'] = np.transpose(input_data['target_evals'])
return input_data
def run_test():
# Start session
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
sess = tf.Session(config=config)
print('restoring graph...')
saver = tf.train.import_meta_graph('%smodel.ckpt-%s.meta'
% (FLAGS.log_dir, FLAGS.num_model))
saver.restore(sess, tf.train.latest_checkpoint('%s' % FLAGS.log_dir))
graph = tf.get_default_graph()
# Retrieve placeholder variables
source_evecs = graph.get_tensor_by_name('source_evecs:0')
source_evecs_trans = graph.get_tensor_by_name('source_evecs_trans:0')
target_evecs = graph.get_tensor_by_name('target_evecs:0')
target_evecs_trans = graph.get_tensor_by_name('target_evecs_trans:0')
source_shot = graph.get_tensor_by_name('source_shot:0')
target_shot = graph.get_tensor_by_name('target_shot:0')
phase = graph.get_tensor_by_name('phase:0')
source_evals = graph.get_tensor_by_name('source_evals:0')
target_evals = graph.get_tensor_by_name('target_evals:0')
Ct_est = graph.get_tensor_by_name(
'matrix_solve_ls/cholesky_solve/MatrixTriangularSolve_1:0'
)
for i in range(80, 99):
input_data_source = get_test_pair_source(FLAGS.files_name + '%.3d' % i)
source_evecs_ = input_data_source['source_evecs'][:, 0:FLAGS.num_evecs]
for j in range(i+1, 100):
t = time.time()
input_data_target = get_test_pair_target(FLAGS.files_name +
'%.3d' % j)
feed_dict = {
phase: True,
source_shot: [input_data_source['source_shot']],
target_shot: [input_data_target['target_shot']],
source_evecs: [input_data_source['source_evecs'][
:,
0:FLAGS.num_evecs
]
],
source_evecs_trans: [input_data_source[
'source_evecs_trans'
][
0:FLAGS.num_evecs,
:]
],
source_evals: [input_data_source[
'source_evals'
][0][0:FLAGS.num_evecs]],
target_evecs: [input_data_target[
'target_evecs'
][:, 0:FLAGS.num_evecs]],
target_evecs_trans: [input_data_target[
'target_evecs_trans'][
0:FLAGS.num_evecs,
:]
],
target_evals: [input_data_target[
'target_evals'][0][0:FLAGS.num_evecs]]
}
Ct_est_ = sess.run([Ct_est], feed_dict=feed_dict)
Ct = np.squeeze(Ct_est_) #Keep transposed
kdt = cKDTree(np.matmul(source_evecs_, Ct))
target_evecs_ = input_data_target['target_evecs'][:, 0:FLAGS.num_evecs]
dist, indices = kdt.query(target_evecs_, n_jobs=-1)
indices = indices + 1
print("Computed correspondences for pair: %s, %s." % (i, j) +
" Took %f seconds" % (time.time() - t))
params_to_save = {}
params_to_save['matches'] = indices
#params_to_save['C'] = Ct.T
# For Matlab where index start at 1
sio.savemat(FLAGS.matches_dir +
FLAGS.files_name + '%.3d-' % i +
FLAGS.files_name + '%.3d.mat' % j, params_to_save)
def main(_):
import time
start_time = time.time()
run_test()
print("--- %s seconds ---" % (time.time() - start_time))
if __name__ == '__main__':
tf.app.run()