forked from liumusicforever/CNN_Face_Detection
-
Notifications
You must be signed in to change notification settings - Fork 0
/
train_detection_net.py
executable file
·156 lines (119 loc) · 6.17 KB
/
train_detection_net.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
'''
Implementation of "A Convolutional Neural Network Cascade for Face Detection "
Paper : https://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Li_A_Convolutional_Neural_2015_CVPR_paper.pdf
Author : Dennis Liu
Modify : 2017/11/10
Description : The example of training detection nets .
'''
import cv2
import numpy as np
import tensorflow as tf
import model
from data import DataSet
from dataset.fddb_crawler import parse_data_info
def train_det_net():
# get all training sample
data_info = parse_data_info(only_positive = False)
# data_info = [<image-path str>,[<nonface/face int>,<pattern-id int>]]
# training configuration
batch = 500
size = (48,48,3)
start_epoch = 0
end_epoch = 1000
train_validation_rate = 0.9 # training set / all sample
# load the pretrained model , set None if you don't have
pretrained = 'models/48_net_6.ckpt'
# load data iterater
dataset = DataSet(data_info,train_rate = train_validation_rate)
_ , train_op , val_op , next_ele = dataset.get_iterator(batch,size)
# load network
# learning rate is great impact in training models
net_12 = model.detect_12Net(lr = 0.001,size = (12,12,3))
net_24 = model.detect_24Net(lr = 0.001,size = (24,24,3))
net_48 = model.detect_48Net(lr = 0.001,size = (48,48,3))
sess = tf.InteractiveSession()
saver = tf.train.Saver()
if pretrained:
saver.restore(sess , pretrained)
else:
sess.run(tf.global_variables_initializer())
for epoch in xrange(start_epoch,end_epoch):
loss = 0
iteration = 0
sess.run(train_op)
# get each element of the training dataset until the end is reached
while True:
try:
# default of the size returned from data iterator is 48
inputs,clss ,pattern = sess.run(next_ele)
# <ndarray> , <0/1> , <one-hot of 45-class>
clss = clss.reshape(batch,2)
pattern = pattern.reshape(batch,45)
# resize image to fit each net
inputs_12 = np.array([cv2.resize(img,(net_12.size[0],net_12.size[1])) for img in inputs])
inputs_24 = np.array([cv2.resize(img,(net_24.size[0],net_24.size[1])) for img in inputs])
inputs_48 = np.array([cv2.resize(img,(net_48.size[0],net_48.size[1])) for img in inputs])
# forward 12net
net_12_fc = net_12.get_fc(inputs_12)
# forward 24net
net_24_fc = net_24.get_fc(inputs_24,net_12_fc)
train_nets = [net_12,net_24,net_48]
net_feed_dict = {net_12.inputs:inputs_12 , net_12.targets:clss,\
net_24.inputs:inputs_24 , net_24.targets:clss,net_24.from_12:net_12_fc,\
net_48.inputs:inputs_48 , net_48.targets:clss,net_48.from_24:net_24_fc}
# training net
sess.run([net.train_step for net in train_nets],\
feed_dict = net_feed_dict)
# loss computation
losses = sess.run([net.loss for net in train_nets],\
feed_dict = net_feed_dict)
if iteration % 100 == 0:
net_12_eva = net_12.evaluate(inputs_12,clss)
net_12_acc = sum(net_12_eva)/len(net_12_eva)
net_24_eva = net_24.evaluate(inputs_24,clss,net_12_fc)
net_24_acc = sum(net_24_eva)/len(net_24_eva)
net_48_eva = net_48.evaluate(inputs_48,clss,net_24_fc)
net_48_acc = sum(net_48_eva)/len(net_48_eva)
print ('Training Epoch {} --- Iter {} --- Training Accuracy: {}%,{}%,{}% --- Training Loss: {}'\
.format(epoch , iteration , net_12_acc , net_24_acc , net_48_acc , losses))
iteration += 1
except tf.errors.OutOfRangeError:
# print("End of training dataset.")
break
# get each element of the validation dataset until the end is reached
sess.run(val_op)
net_12_acc = []
net_24_acc = []
net_48_acc = []
while True:
try:
# the size returned from data iterator is 48
inputs,clss ,pattern = sess.run(next_ele)
clss = clss.reshape(batch,2)
# resize image to fit each net
inputs_12 = np.array([cv2.resize(img,(net_12.size[0],net_12.size[1])) for img in inputs])
inputs_24 = np.array([cv2.resize(img,(net_24.size[0],net_24.size[1])) for img in inputs])
inputs_48 = np.array([cv2.resize(img,(net_48.size[0],net_48.size[1])) for img in inputs])
# forward 12net
net_12_fc = net_12.get_fc(inputs_12)
# forward 24net
net_24_fc = net_24.get_fc(inputs_24,net_12_fc)
net_12_eva = net_12.evaluate(inputs_12,clss)
net_24_eva = net_24.evaluate(inputs_24,clss,net_12_fc)
net_48_eva = net_48.evaluate(inputs_48,clss,net_24_fc)
for i in range(len(net_12_eva)):
net_12_acc.append(net_12_eva[i])
net_24_acc.append(net_24_eva[i])
net_48_acc.append(net_48_eva[i])
except tf.errors.OutOfRangeError:
# print("End of validation dataset.")
break
print ('Validation Epoch {} Validation Accuracy: {}%,{}%,{}%'\
.format(epoch , sum(net_12_acc)/len(net_12_acc),\
sum(net_24_acc)/len(net_24_acc),\
sum(net_48_acc)/len(net_48_acc)))
saver = tf.train.Saver()
save_path = saver.save(sess, "models/48_net_{}.ckpt".format(epoch))
print ("Model saved in file: ", save_path)
if __name__ == "__main__":
train_det_net()