/
ST_model_nonTrainable_AlexNetOnFaces.py
279 lines (204 loc) · 11.5 KB
/
ST_model_nonTrainable_AlexNetOnFaces.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
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
import numpy as np
import tensorflow as tf
from tensorflow.python.training import moving_averages
class Pose_Estimation(object):
def __init__(self, images, labels, mode, ifdropout, keep_rate_fc6, keep_rate_fc7, lr_rate_fac, net_data, batch_size, mean_labels, std_labels):
self.batch_size = batch_size
self._images = images
self.labels = labels
self.mode = mode
self.ifdropout = ifdropout
self.keep_rate_fc6 = keep_rate_fc6
self.keep_rate_fc7 = keep_rate_fc7
self.ifadd_weight_decay = 0 #ifadd_weight_decay
self.net_data = net_data
self.lr_rate_fac = lr_rate_fac
self._extra_train_ops = []
self.optimizer = 'Adam'
self.mean_labels = mean_labels
self.std_labels = std_labels
#self.train_mean_vec = train_mean_vec
def _build_graph(self):
"""Build a whole graph for the model."""
self.global_step = tf.Variable(0, name='global_step', trainable=False)
self._build_model()
if self.mode == 'train':
self._build_train_op()
#self.summaries = tf.merge_all_summaries()
def _stride_arr(self, stride):
"""Map a stride scalar to the stride array for tf.nn.conv2d."""
return [1, stride, stride, 1]
def _build_model(self):
"""Build the core model within the graph."""
with tf.variable_scope('Spatial_Transformer'):
x = self._images
x = tf.image.resize_bilinear(x, tf.constant([227,227], dtype=tf.int32)) # the image should be 227 x 227 x 3
self.resized_img = x
theta = self._ST('ST2', x, 3, (16,16), 3, 16, self._stride_arr(1))
#print "*** ", x.get_shape()
with tf.variable_scope('costs'):
self.predictions = theta
self.preds_unNormalized = theta * (self.std_labels + 0.000000000000000001) + self.mean_labels
pred_dim1 = theta.get_shape()[0]
pred_dim2 = theta.get_shape()[1]
del theta
pow_res = tf.pow(self.predictions-self.labels, 2)
xent = tf.reduce_sum(pow_res,1)
self.cost = tf.reduce_mean(xent, name='xent')
if self.ifadd_weight_decay == 1:
self.cost += self._decay()
def conv(self, input, kernel, biases, k_h, k_w, c_o, s_h, s_w, padding="VALID", group=1):
'''From https://github.com/ethereon/caffe-tensorflow
'''
c_i = input.get_shape()[-1]
assert c_i%group==0
assert c_o%group==0
convolve = lambda i, k: tf.nn.conv2d(i, k, [1, s_h, s_w, 1], padding=padding)
if group==1:
conv = convolve(input, kernel)
else:
input_groups = tf.split(input, group, 3)
kernel_groups = tf.split(kernel, group, 3)
output_groups = [convolve(i, k) for i,k in zip(input_groups, kernel_groups)]
conv = tf.concat(output_groups, 3)
return tf.reshape(tf.nn.bias_add(conv, biases), [-1]+conv.get_shape().as_list()[1:])
def _ST(self, name, x, channel_x, out_size, filter_size, out_filters, strides):
""" Spatial Transformer. """
with tf.variable_scope(name):
# zero-mean input [B,G,R]: [93.5940, 104.7624, 129.1863] --> provided by vgg-face
"""
with tf.name_scope('preprocess') as scope:
mean = tf.constant(tf.reshape(self.train_mean_vec*255.0, [3]), dtype=tf.float32, shape=[1, 1, 1, 3], name='img_mean')
x = x - mean
"""
# conv1
with tf.name_scope('conv1') as scope:
#conv(11, 11, 96, 4, 4, padding='VALID', name='conv1')
k_h = 11; k_w = 11; c_o = 96; s_h = 4; s_w = 4
conv1W = tf.Variable(self.net_data["conv1"]["weights"], trainable=False, name='W')
conv1b = tf.Variable(self.net_data["conv1"]["biases"], trainable=False, name='baises')
conv1_in = self.conv(x, conv1W, conv1b, k_h, k_w, c_o, s_h, s_w, padding="SAME", group=1)
conv1 = tf.nn.relu(conv1_in, name='conv1')
#print x.get_shape(), conv1.get_shape()
self.conv1 = conv1
k_h = 3; k_w = 3; s_h = 2; s_w = 2; padding = 'VALID'
maxpool1 = tf.nn.max_pool(conv1, ksize=[1, k_h, k_w, 1], strides=[1, s_h, s_w, 1], padding=padding, name='pool1')
radius = 2; alpha = 2e-05; beta = 0.75; bias = 1.0
lrn1 = tf.nn.local_response_normalization(maxpool1,
depth_radius=radius,
alpha=alpha,
beta=beta,
bias=bias, name='norm1')
# conv2
with tf.name_scope('conv2') as scope:
#conv(5, 5, 256, 1, 1, group=2, name='conv2')
k_h = 5; k_w = 5; c_o = 256; s_h = 1; s_w = 1; group = 2
conv2W = tf.Variable(self.net_data["conv2"]["weights"], trainable=False, name='W')
conv2b = tf.Variable(self.net_data["conv2"]["biases"], trainable=False, name='baises')
conv2_in = self.conv(lrn1, conv2W, conv2b, k_h, k_w, c_o, s_h, s_w, padding="SAME", group=group)
conv2 = tf.nn.relu(conv2_in, name='conv2')
#print conv2.get_shape(), self.net_data["conv2"]["weights"].shape, self.net_data["conv2"]["biases"].shape
self.conv2 = conv2
#maxpool2
#max_pool(3, 3, 2, 2, padding='VALID', name='pool2')
k_h = 3; k_w = 3; s_h = 2; s_w = 2; padding = 'VALID'
maxpool2 = tf.nn.max_pool(conv2, ksize=[1, k_h, k_w, 1], strides=[1, s_h, s_w, 1], padding=padding, name='pool2')
#print maxpool2.get_shape()
#lrn2
#lrn(2, 2e-05, 0.75, name='norm2')
radius = 2; alpha = 2e-05; beta = 0.75; bias = 1.0
lrn2 = tf.nn.local_response_normalization(maxpool2,
depth_radius=radius,
alpha=alpha,
beta=beta,
bias=bias, name='norm2')
# conv3
with tf.name_scope('conv3') as scope:
#conv(3, 3, 384, 1, 1, name='conv3')
k_h = 3; k_w = 3; c_o = 384; s_h = 1; s_w = 1; group = 1
conv3W = tf.Variable(self.net_data["conv3"]["weights"], trainable=False, name='W')
conv3b = tf.Variable(self.net_data["conv3"]["biases"], trainable=False, name='baises')
conv3_in = self.conv(lrn2, conv3W, conv3b, k_h, k_w, c_o, s_h, s_w, padding="SAME", group=group)
conv3 = tf.nn.relu(conv3_in, name='conv3')
#print conv3.get_shape(), self.net_data["conv3"]["weights"].shape, self.net_data["conv3"]["biases"].shape
# conv4
with tf.name_scope('conv4') as scope:
#conv(3, 3, 384, 1, 1, group=2, name='conv4')
k_h = 3; k_w = 3; c_o = 384; s_h = 1; s_w = 1; group = 2
conv4W = tf.Variable(self.net_data["conv4"]["weights"], trainable=False, name='W')
conv4b = tf.Variable(self.net_data["conv4"]["biases"], trainable=False, name='baises')
conv4_in = self.conv(conv3, conv4W, conv4b, k_h, k_w, c_o, s_h, s_w, padding="SAME", group=group)
conv4 = tf.nn.relu(conv4_in, name='conv4')
#print conv4.get_shape()
self.conv4 = conv4
# conv5
with tf.name_scope('conv5') as scope:
#conv(3, 3, 256, 1, 1, group=2, name='conv5')
k_h = 3; k_w = 3; c_o = 256; s_h = 1; s_w = 1; group = 2
conv5W = tf.Variable(self.net_data["conv5"]["weights"], trainable=False, name='W')
conv5b = tf.Variable(self.net_data["conv5"]["biases"], trainable=False, name='baises')
self.conv5b = conv5b
conv5_in = self.conv(conv4, conv5W, conv5b, k_h, k_w, c_o, s_h, s_w, padding="SAME", group=group)
conv5 = tf.nn.relu(conv5_in, name='conv5')
#print conv5.get_shape()
self.conv5 = conv5
#maxpool5
#max_pool(3, 3, 2, 2, padding='VALID', name='pool5')
k_h = 3; k_w = 3; s_h = 2; s_w = 2; padding = 'VALID'
maxpool5 = tf.nn.max_pool(conv5, ksize=[1, k_h, k_w, 1], strides=[1, s_h, s_w, 1], padding=padding, name='pool5')
#print maxpool5.get_shape(), maxpool5.get_shape()[1:], int(np.prod(maxpool5.get_shape()[1:]))
# fc6
with tf.variable_scope('fc6') as scope:
#fc(4096, name='fc6')
fc6W = tf.Variable(self.net_data["fc6"]["weights"], trainable=False, name='W')
fc6b = tf.Variable(self.net_data["fc6"]["biases"], trainable=False, name='baises')
self.fc6W = fc6W
self.fc6b = fc6b
fc6 = tf.nn.relu_layer(tf.reshape(maxpool5, [-1, int(np.prod(maxpool5.get_shape()[1:]))]), fc6W, fc6b, name='fc6')
#print fc6.get_shape()
if self.ifdropout == 1:
fc6 = tf.nn.dropout(fc6, self.keep_rate_fc6, name='fc6_dropout')
# fc7
with tf.variable_scope('fc7') as scope:
#fc(4096, name='fc7')
fc7W = tf.Variable(self.net_data["fc7"]["weights"], trainable=False, name='W')
fc7b = tf.Variable(self.net_data["fc7"]["biases"], trainable=False, name='baises')
self.fc7b = fc7b
fc7 = tf.nn.relu_layer(fc6, fc7W, fc7b, name='fc7')
#print fc7.get_shape()
if self.ifdropout == 1:
fc7 = tf.nn.dropout(fc7, self.keep_rate_fc7, name='fc7_dropout')
# fc8
with tf.variable_scope('fc8') as scope:
# Move everything into depth so we can perform a single matrix multiplication.
fc7 = tf.reshape(fc7, [self.batch_size, -1])
dim = fc7.get_shape()[1].value
#print "fc7 dim:\n"
#print fc7.get_shape(), dim
fc8W = tf.Variable(tf.random_normal(tf.stack([dim, self.labels.shape[1]]), mean=0.0, stddev=0.01), trainable=False, name='W')
fc8b = tf.Variable(tf.zeros([self.labels.shape[1]]), trainable=False, name='baises')
self.fc8b = fc8b
theta = tf.nn.xw_plus_b(fc7, fc8W, fc8b)
self.theta = theta
self.fc8W = fc8W
self.fc8b = fc8b
# %% We'll create a spatial transformer module to identify discriminative
# %% patches
#h_trans = self._transform(theta, x, out_size, channel_x)
#print h_trans.get_shape()
return theta
def _build_train_op(self):
"""Build training specific ops for the graph."""
#self.lrn_rate = tf.constant(self.hps.lrn_rate, tf.float32)
#tf.scalar_summary('learning rate', self.lrn_rate)
"""
trainable_variables = tf.trainable_variables()
grads = tf.gradients(self.cost, trainable_variables)
"""
if self.optimizer == 'sgd':
optimizer = tf.train.GradientDescentOptimizer(self.lrn_rate)
elif self.optimizer == 'Adam':
optimizer = tf.train.AdamOptimizer(0.001 * self.lr_rate_fac)
elif self.optimizer == 'mom':
optimizer = tf.train.MomentumOptimizer(self.lrn_rate, 0.9)
self.train_op = optimizer.minimize(self.cost)