-
Notifications
You must be signed in to change notification settings - Fork 1
/
Copy pathtest_twostage_iom_fusion.py
120 lines (104 loc) · 6.28 KB
/
test_twostage_iom_fusion.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
from absl import app, flags, logging
from absl.flags import FLAGS
import cv2
import os
import numpy as np
import tensorflow as tf
if tf.__version__.startswith('1'):# important is you want to run with tf1.x,
print('[*] enable eager execution')
tf.compat.v1.enable_eager_execution()
import modules
import csv
import math
from modules.evaluations import get_val_data, perform_val,perform_val_fusion, perform_val_yts
from modules.models import ArcFaceModel, IoMFaceModelFromArFace, build_or_load_IoMmodel,IoMFaceModelFromArFace2,IoMFaceModelFromArFace3,IoMFaceModelFromArFace_T,IoMFaceModelFromArFace_T1
from modules.utils import set_memory_growth, load_yaml, l2_norm
# modules.utils.set_memory_growth()
flags.DEFINE_string('cfg_path', './configs/iom_res50.yaml', 'config file path')
flags.DEFINE_string('cfg_path2', './configs/iom_res50.yaml', 'config file path')
flags.DEFINE_string('ckpt_epoch', '', 'ckpt epoch')
flags.DEFINE_string('gpu', '0', 'which gpu to use')
flags.DEFINE_string('img_path', '', 'path to input image')
def main(_argv):
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
os.environ['CUDA_VISIBLE_DEVICES'] = FLAGS.gpu
logger = tf.get_logger()
logger.disabled = True
logger.setLevel(logging.FATAL)
set_memory_growth()
def getModel(cfg_path):
cfg = load_yaml(cfg_path)
m = cfg['m']
q = cfg['q']
model = build_or_load_IoMmodel(cfg)
model.summary(line_length=80)
return model,cfg
model, cfg = getModel(FLAGS.cfg_path)
model2, cfg2 = getModel(FLAGS.cfg_path2)
cfg['embd_shape'] = cfg['m'] * cfg['q']
def evl(isLUT, measure):
# print("[*] Perform Retrieval Evaluation on Y.T.F and F.S...")
# mAp_ytf, rr_ytf = perform_val_yts(cfg['eval_batch_size'], model, cfg['test_dataset_ytf'], img_ext='jpg',
# isLUT=isLUT, cfg=cfg)
# mAp_fs, rr_fs = perform_val_yts(cfg['eval_batch_size'], model, cfg['test_dataset_fs'], img_ext='png',
# isLUT=isLUT, cfg=cfg)
# print(" Y.T.F mAP {:.4f}, F.S mAP: {:.2f}".format(mAp_ytf, mAp_fs))
# print(" Y.T.F CMC-1 {:.4f}, F.S CMC-1: {:.2f}".format(rr_ytf[0], rr_fs[0]))
mAp_fs = mAp_ytf = 0
rr_ytf = rr_fs = [0]
if isLUT == 0 and measure == 'Jaccard':
isLUT = q
print("[*] Loading LFW, AgeDB30 and CFP-FP...")
lfw, agedb_30, cfp_fp, lfw_issame, agedb_30_issame, cfp_fp_issame = \
get_val_data(cfg['test_dataset'])
cfg['eval_batch_size'] = 100
print("[*] Perform Evaluation on LFW...")
acc_lfw, best_th_lfw, auc_lfw, eer_lfw, embeddings_lfw = perform_val_fusion(
cfg['embd_shape'], cfg['eval_batch_size'], model,model2, lfw, lfw_issame,
is_ccrop=cfg['is_ccrop'], cfg=cfg, isLUT=isLUT, measure=measure)
print(" acc {:.4f}, th: {:.2f}, auc {:.4f}, EER {:.4f}".format(acc_lfw, best_th_lfw, auc_lfw, eer_lfw))
# with open('embeddings/' + cfg['sub_name'] + '_embeddings_lfw.csv', 'w', newline='') as file:
# writer = csv.writer(file, escapechar='/', quoting=csv.QUOTE_NONE)
# writer.writerows(embeddings_lfw)
print("[*] Perform Evaluation on AgeDB30...")
acc_agedb30, best_th_agedb30, auc_agedb30, eer_agedb30, embeddings_agedb30 = perform_val_fusion(
cfg['embd_shape'], cfg['eval_batch_size'], model,model2, agedb_30,
agedb_30_issame, is_ccrop=cfg['is_ccrop'], cfg=cfg, isLUT=isLUT, measure=measure)
print(" acc {:.4f}, th: {:.2f}, auc {:.4f}, EER {:.4f}".format(acc_agedb30, best_th_agedb30, auc_agedb30,
eer_agedb30))
print("[*] Perform Evaluation on CFP-FP...")
acc_cfp_fp, best_th_cfp_fp, auc_cfp_fp, eer_cfp_fp, embeddings_cfp_fp = perform_val_fusion(
cfg['embd_shape'], cfg['eval_batch_size'], model,model2, cfp_fp, cfp_fp_issame,
is_ccrop=cfg['is_ccrop'], cfg=cfg, isLUT=isLUT, measure=measure)
print(" acc {:.4f}, th: {:.2f}, auc {:.4f}, EER {:.4f}".format(acc_cfp_fp, best_th_cfp_fp, auc_cfp_fp,
eer_cfp_fp))
log_str = '''| q = {:.2f}, m = {:.2f},LUT={} | LFW | AgeDB30 | CFP - FP |
|------------------------|--------|---------|----------|
| Accuracy | {:.4f} | {:.4f} | {:.4f} |
| EER | {:.4f} | {:.4f} | {:.4f} |
| AUC | {:.4f} | {:.4f} | {:.4f} |
| Threshold | {:.4f} | {:.4f} | {:.4f} |
| | mAP | CMC-1 | |
| Y.T.F | {:.4f} | {:.4f} | |
| F.S | {:.4f} | {:.4f} | | \n\n '''.format(q, m, isLUT,
acc_lfw, acc_agedb30, acc_cfp_fp,
eer_lfw, eer_agedb30, eer_cfp_fp,
auc_lfw, auc_agedb30, auc_cfp_fp,
best_th_lfw, best_th_agedb30,
best_th_cfp_fp,
mAp_ytf, rr_ytf[0],
mAp_fs, rr_fs[0])
print(log_str)
log_str2 = '''| q = {:.2f}, m = {:.2f},LUT={},dist={} \t {:.4f} \t {:.4f} \t {:.4f} \t {:.4f} \t {:.4f} \t {:.4f} \t {:.4f} \t {:.4f} \t {:.4f} \t {:.4f} \t {:.4f} \t {:.4f} \t {:.4f}\n\n '''.format(
q, m, isLUT, measure, mAp_ytf, mAp_fs, rr_ytf[0], rr_fs[0], eer_lfw, eer_agedb30, eer_cfp_fp, acc_lfw,
acc_agedb30, acc_cfp_fp, auc_lfw, auc_agedb30, auc_cfp_fp)
with open('./logs/' + cfg['sub_name']+'_fuse_'+ cfg2['sub_name'] + "_learning_Output.md", "a") as text_file:
text_file.write(log_str2)
print(log_str2)
evl(0, 'Hamming')
# evl(int(math.log2(q)), 'Hamming')
if __name__ == '__main__':
try:
app.run(main)
except SystemExit:
pass