-
Notifications
You must be signed in to change notification settings - Fork 51
/
Copy pathmain.py
158 lines (137 loc) · 6.51 KB
/
main.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
"""
date: 2017/11/10
author: lslcode [jasonli8848@qq.com]
"""
import os
import gc
import xml.etree.ElementTree as etxml
import random
import skimage.io
import skimage.transform
import numpy as np
import tensorflow as tf
import ssd300
import time
'''
SSD检测
'''
def testing():
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.9)
with tf.Session(config=tf.ConfigProto(gpu_options=gpu_options)) as sess:
ssd_model = ssd300.SSD300(sess,False)
sess.run(tf.global_variables_initializer())
saver = tf.train.Saver(var_list=tf.trainable_variables())
if os.path.exists('./session_params/session.ckpt.index') :
saver.restore(sess, './session_params/session.ckpt')
image, actual,file_list = get_traindata_voc2007(1)
pred_class, pred_class_val, pred_location = ssd_model.run(image,None)
print('file_list:' + str(file_list))
for index, act in zip(range(len(image)), actual):
for a in act :
print('【img-'+str(index)+' actual】:' + str(a))
print('pred_class:' + str(pred_class[index]))
print('pred_class_val:' + str(pred_class_val[index]))
print('pred_location:' + str(pred_location[index]))
else:
print('No Data Exists!')
sess.close()
'''
SSD训练
'''
def training():
batch_size = 15
running_count = 0
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.9)
with tf.Session(config=tf.ConfigProto(gpu_options=gpu_options)) as sess:
ssd_model = ssd300.SSD300(sess,True)
sess.run(tf.global_variables_initializer())
saver = tf.train.Saver(var_list=tf.trainable_variables())
if os.path.exists('./session_params/session.ckpt.index') :
print('\nStart Restore')
saver.restore(sess, './session_params/session.ckpt')
print('\nEnd Restore')
print('\nStart Training')
min_loss_location = 100000.
min_loss_class = 100000.
while((min_loss_location + min_loss_class) > 0.001 and running_count < 100000):
running_count += 1
train_data, actual_data,_ = get_traindata_voc2007(batch_size)
if len(train_data) > 0:
loss_all,loss_class,loss_location,pred_class,pred_location = ssd_model.run(train_data, actual_data)
l = np.sum(loss_location)
c = np.sum(loss_class)
if min_loss_location > l:
min_loss_location = l
if min_loss_class > c:
min_loss_class = c
print('Running:【' + str(running_count) + '】|Loss All:【'+str(min_loss_location + min_loss_class)+'|'+ str(loss_all) + '】|Location:【'+ str(np.sum(loss_location)) + '】|Class:【'+ str(np.sum(loss_class)) + '】|pred_class:【'+ str(np.sum(pred_class))+'|'+str(np.amax(pred_class))+'|'+ str(np.min(pred_class)) + '】|pred_location:【'+ str(np.sum(pred_location))+'|'+str(np.amax(pred_location))+'|'+ str(np.min(pred_location)) + '】')
# 定期保存ckpt
if running_count % 100 == 0:
saver.save(sess, './session_params/session.ckpt')
print('session.ckpt has been saved.')
gc.collect()
else:
print('No Data Exists!')
break
saver.save(sess, './session_params/session.ckpt')
sess.close()
gc.collect()
print('End Training')
'''
获取voc2007训练图片数据
train_data：训练批次图像，格式[None,width,height,3]
actual_data：图像标注数据，格式[None,[None,center_x,center_y,width,height,lable]]
'''
file_name_list = os.listdir('./train_datasets/voc2007/JPEGImages/')
lable_arr = ['background','aeroplane','bicycle','bird','boat','bottle','bus','car','cat','chair','cow','diningtable','dog','horse','motorbike','person','pottedplant','sheep','sofa','train','tvmonitor']
# 图像白化，格式:[R,G,B]
whitened_RGB_mean = [123.68, 116.78, 103.94]
def get_traindata_voc2007(batch_size):
def get_actual_data_from_xml(xml_path):
actual_item = []
try:
annotation_node = etxml.parse(xml_path).getroot()
img_width = float(annotation_node.find('size').find('width').text.strip())
img_height = float(annotation_node.find('size').find('height').text.strip())
object_node_list = annotation_node.findall('object')
for obj_node in object_node_list:
lable = lable_arr.index(obj_node.find('name').text.strip())
bndbox = obj_node.find('bndbox')
x_min = float(bndbox.find('xmin').text.strip())
y_min = float(bndbox.find('ymin').text.strip())
x_max = float(bndbox.find('xmax').text.strip())
y_max = float(bndbox.find('ymax').text.strip())
# 位置数据用比例来表示，格式[center_x,center_y,width,height,lable]
actual_item.append([((x_min + x_max)/2/img_width), ((y_min + y_max)/2/img_height), ((x_max - x_min) / img_width), ((y_max - y_min) / img_height), lable])
return actual_item
except:
return None
train_data = []
actual_data = []
file_list = random.sample(file_name_list, batch_size)
for f_name in file_list :
img_path = './train_datasets/voc2007/JPEGImages/' + f_name
xml_path = './train_datasets/voc2007/Annotations/' + f_name.replace('.jpg','.xml')
if os.path.splitext(img_path)[1].lower() == '.jpg' :
actual_item = get_actual_data_from_xml(xml_path)
if actual_item != None :
actual_data.append(actual_item)
else :
print('Error : '+xml_path)
continue
img = skimage.io.imread(img_path)
img = skimage.transform.resize(img, (300, 300))
# 图像白化预处理
img = img - whitened_RGB_mean
train_data.append(img)
return train_data, actual_data,file_list
'''
主程序入口
'''
if __name__ == '__main__':
print('\nStart Running')
# 检测
#testing()
# 训练
training()
print('\nEnd Running')