-
Notifications
You must be signed in to change notification settings - Fork 2
/
vgg16part.py
131 lines (98 loc) · 4.54 KB
/
vgg16part.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
import inspect
import os
import numpy as np
import tensorflow as tf
import time
# save np.load
np_load_old = np.load
# modify the default parameters of np.load
np.load = lambda *a,**k: np_load_old(*a, allow_pickle=True, **k)
VGG_MEAN = [103.939, 116.779, 123.68]
class Vgg16:
def __init__(self, vgg16_npy_path=None):
if vgg16_npy_path is None:
path = inspect.getfile(Vgg16)
path = os.path.abspath(os.path.join(path, os.pardir))
path = os.path.join(path, "vgg16.npy")
vgg16_npy_path = path
print(path)
self.data_dict = np.load(vgg16_npy_path, encoding='latin1').item()
print("npy file loaded")
def build(self, rgb):
"""
load variable from npy to build the VGG
:param rgb: rgb image [batch, height, width, 3] values scaled [0, 1]
"""
start_time = time.time()
print("build model started")
rgb_scaled = rgb * 255.0
# Convert RGB to BGR
red, green, blue = tf.split(axis=3, num_or_size_splits=3, value=rgb_scaled)
# assert red.get_shape().as_list()[1:] == [224, 224, 1]
# assert green.get_shape().as_list()[1:] == [224, 224, 1]
# assert blue.get_shape().as_list()[1:] == [224, 224, 1]
bgr = tf.concat(axis=3, values=[
blue - VGG_MEAN[0],
green - VGG_MEAN[1],
red - VGG_MEAN[2],
])
# assert bgr.get_shape().as_list()[1:] == [224, 224, 3]
self.conv1_1 = self.conv_layer(bgr, "conv1_1")
self.conv1_2 = self.conv_layer(self.conv1_1, "conv1_2")
# self.pool1 = self.max_pool(self.conv1_2, 'pool1')
# self.conv2_1 = self.conv_layer(self.pool1, "conv2_1")
# self.conv2_2 = self.conv_layer(self.conv2_1, "conv2_2")
# self.pool2 = self.max_pool(self.conv2_2, 'pool2')
# self.conv3_1 = self.conv_layer(self.pool2, "conv3_1")
# self.conv3_2 = self.conv_layer(self.conv3_1, "conv3_2")
# self.conv3_3 = self.conv_layer(self.conv3_2, "conv3_3")
# self.pool3 = self.max_pool(self.conv3_3, 'pool3')
# self.conv4_1 = self.conv_layer(self.pool3, "conv4_1")
# self.conv4_2 = self.conv_layer(self.conv4_1, "conv4_2")
# self.conv4_3 = self.conv_layer(self.conv4_2, "conv4_3")
# self.pool4 = self.max_pool(self.conv4_3, 'pool4')
# self.conv5_1 = self.conv_layer(self.pool4, "conv5_1")
# self.conv5_2 = self.conv_layer(self.conv5_1, "conv5_2")
# self.conv5_3 = self.conv_layer(self.conv5_2, "conv5_3")
# self.pool5 = self.max_pool(self.conv5_3, 'pool5')
# self.fc6 = self.fc_layer(self.pool5, "fc6")
# assert self.fc6.get_shape().as_list()[1:] == [4096]
# self.relu6 = tf.nn.relu(self.fc6)
# self.fc7 = self.fc_layer(self.relu6, "fc7")
# self.relu7 = tf.nn.relu(self.fc7)
# self.fc8 = self.fc_layer(self.relu7, "fc8")
# self.prob = tf.nn.softmax(self.fc8, name="prob")
self.prob = self.conv1_2
# self.data_dict = None
# print(("build model finished: %ds" % (time.time() - start_time)))
def avg_pool(self, bottom, name):
return tf.nn.avg_pool(bottom, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME', name=name)
def max_pool(self, bottom, name):
return tf.nn.max_pool(bottom, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME', name=name)
def conv_layer(self, bottom, name):
with tf.variable_scope(name):
filt = self.get_conv_filter(name)
conv = tf.nn.conv2d(bottom, filt, [1, 1, 1, 1], padding='SAME')
conv_biases = self.get_bias(name)
bias = tf.nn.bias_add(conv, conv_biases)
relu = tf.nn.relu(bias)
return relu
def fc_layer(self, bottom, name):
with tf.variable_scope(name):
shape = bottom.get_shape().as_list()
dim = 1
for d in shape[1:]:
dim *= d
x = tf.reshape(bottom, [-1, dim])
weights = self.get_fc_weight(name)
biases = self.get_bias(name)
# Fully connected layer. Note that the '+' operation automatically
# broadcasts the biases.
fc = tf.nn.bias_add(tf.matmul(x, weights), biases)
return fc
def get_conv_filter(self, name):
return tf.constant(self.data_dict[name][0], name="filter")
def get_bias(self, name):
return tf.constant(self.data_dict[name][1], name="biases")
def get_fc_weight(self, name):
return tf.constant(self.data_dict[name][0], name="weights")