forked from LarryJiang134/Image_manipulation_detection
-
Notifications
You must be signed in to change notification settings - Fork 0
/
vgg16.py
314 lines (255 loc) · 15.8 KB
/
vgg16.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
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
# --------------------------------------------------------
# Tensorflow Two Stream Faster R-CNN
# Licensed under The MIT License [see LICENSE for details]
# Written by Hangyan Jiang
# --------------------------------------------------------
import tensorflow as tf
import tensorflow.contrib.slim as slim
from lib.utils.compact_bilinear_pooling import compact_bilinear_pooling_layer
import numpy as np
import lib.config.config as cfg
from lib.nets.network import Network
class vgg16(Network):
def __init__(self, batch_size=1):
Network.__init__(self, batch_size=batch_size)
def build_network(self, sess, is_training=True):
with tf.variable_scope('vgg_16', 'vgg_16'):
# select initializer
if cfg.FLAGS.initializer == "truncated":
initializer = tf.truncated_normal_initializer(mean=0.0, stddev=0.01)
initializer_bbox = tf.truncated_normal_initializer(mean=0.0, stddev=0.001)
else:
initializer = tf.random_normal_initializer(mean=0.0, stddev=0.01)
initializer_bbox = tf.random_normal_initializer(mean=0.0, stddev=0.001)
q = [4.0, 12.0, 2.0]
filter1 = [[0, 0, 0, 0, 0],
[0, -1, 2, -1, 0],
[0, 2, -4, 2, 0],
[0, -1, 2, -1, 0],
[0, 0, 0, 0, 0]]
filter2 = [[-1, 2, -2, 2, -1],
[2, -6, 8, -6, 2],
[-2, 8, -12, 8, -2],
[2, -6, 8, -6, 2],
[-1, 2, -2, 2, -1]]
filter3 = [[0, 0, 0, 0, 0],
[0, 0, 0, 0, 0],
[0, 1, -2, 1, 0],
[0, 0, 0, 0, 0],
[0, 0, 0, 0, 0]]
filter1 = np.asarray(filter1, dtype=float) / q[0]
filter2 = np.asarray(filter2, dtype=float) / q[1]
filter3 = np.asarray(filter3, dtype=float) / q[2]
filters = [[filter1, filter1, filter1], [filter2, filter2, filter2], [filter3, filter3, filter3]]
filters = np.einsum('klij->ijlk', filters)
filters = filters.flatten()
initializer_srm = tf.constant_initializer(filters)
# Build RGB stream head
net = self.build_head(is_training)
# Build Noise stream head
net2 = self.build_head_forNoise(is_training, initializer, initializer_srm)
# Build rpn
rpn_cls_prob, rpn_bbox_pred, rpn_cls_score, rpn_cls_score_reshape = self.build_rpn(net, is_training, initializer)
# Build proposals
rois = self.build_proposals(is_training, rpn_cls_prob, rpn_bbox_pred, rpn_cls_score)
# Build predictions
cls_score, cls_prob, bbox_pred = self.build_predictions(net, net2, rois, is_training, initializer, initializer_bbox)
self._predictions["rpn_cls_score"] = rpn_cls_score
self._predictions["rpn_cls_score_reshape"] = rpn_cls_score_reshape
self._predictions["rpn_cls_prob"] = rpn_cls_prob
self._predictions["rpn_bbox_pred"] = rpn_bbox_pred
self._predictions["cls_score"] = cls_score
self._predictions["cls_prob"] = cls_prob
self._predictions["bbox_pred"] = bbox_pred
self._predictions["rois"] = rois
self._score_summaries.update(self._predictions)
return rois, cls_prob, bbox_pred
def get_variables_to_restore(self, variables, var_keep_dic, sess, pretrained_model):
variables_to_restore = []
noise_variable = {}
for v in variables:
# exclude the conv weights that are fc weights in vgg16
if v.name == 'vgg_16/fc6/weights:0' or v.name == 'vgg_16/fc7/weights:0' \
or v.name == 'vgg_16/cbp_fc6/weights:0' or v.name == 'vgg_16/cbp_fc7/weights:0':
self._variables_to_fix[v.name] = v
continue
# exclude the first conv layer to swap RGB to BGR
if v.name == 'vgg_16/conv1/conv1_1/weights:0' or v.name == 'vgg_16/conv1n/conv1n_1/weights:0':
self._variables_to_fix[v.name] = v
continue
if v.name.split(':')[0] in var_keep_dic:
print('Variables restored: %s' % v.name)
variables_to_restore.append(v)
# # From VGG pretrained weights file(RGB weights), load weights for noise stream
# name = v.name.split('/')
# if len(name) < 4:
# continue
# name[1] += 'n'
# name[2] = name[1] + name[2][len(name[1])-1:]
# noise_counterpart = name[0] + '/' + name[1] + '/' + name[2] + '/' + name[3]
# for u in variables:
# if u.name == noise_counterpart:
# noise_variable[v.name.split(':')[0]] = u
# print('Variables restored: %s' % u.name)
#
# with tf.variable_scope('Restore_Noise_Variables'):
# with tf.device("/cpu:0"):
# # fix the vgg16 noise stream variables
# restorer = tf.train.Saver(noise_variable)
# restorer.restore(sess, pretrained_model)
# # From VGG pretrained weights file(RGB weights), load weights for noise stream except for conv layer 1, 2
# name = v.name.split('/')
# if len(name) < 4 or (name[1] != 'conv1' and name[1] != 'conv2'):
# continue
# name[1] += 'n'
# name[2] = name[1] + name[2][len(name[1]) - 1:]
# noise_counterpart = name[0] + '/' + name[1] + '/' + name[2] + '/' + name[3]
# for u in variables:
# if u.name == noise_counterpart:
# noise_variable[v.name.split(':')[0]] = u
# print('Variables restored: %s' % u.name)
#
# with tf.variable_scope('Restore_Noise_Variables'):
# with tf.device("/cpu:0"):
# # fix the vgg16 noise stream variables
# restorer = tf.train.Saver(noise_variable)
# restorer.restore(sess, pretrained_model)
return variables_to_restore
def fix_variables(self, sess, pretrained_model):
print('Fix VGG16 layers..')
with tf.variable_scope('Fix_VGG16'):
with tf.device("/cpu:0"):
# fix the vgg16 issue from conv weights to fc weights
# fix RGB to BGR
fc6_conv = tf.get_variable("fc6_conv", [7, 7, 512, 4096], trainable=False)
fc7_conv = tf.get_variable("fc7_conv", [1, 1, 4096, 4096], trainable=False)
conv1_rgb = tf.get_variable("conv1_rgb", [3, 3, 3, 64], trainable=False)
# cbp_fc6_conv = tf.get_variable("cbp_fc6_conv", [7, 7, 512, 4096], trainable=False)
# cbp_fc7_conv = tf.get_variable("cbp_fc7_conv", [1, 1, 4096, 4096], trainable=False)
# noise_conv1_rgb = tf.get_variable("noise_conv1_rgb", [3, 3, 3, 64], trainable=False)
restorer_fc = tf.train.Saver({"vgg_16/fc6/weights": fc6_conv,
"vgg_16/fc7/weights": fc7_conv,
"vgg_16/conv1/conv1_1/weights": conv1_rgb})
restorer_fc.restore(sess, pretrained_model)
sess.run(tf.assign(self._variables_to_fix['vgg_16/fc6/weights:0'], tf.reshape(fc6_conv,
self._variables_to_fix[
'vgg_16/fc6/weights:0'].get_shape())))
sess.run(tf.assign(self._variables_to_fix['vgg_16/fc7/weights:0'], tf.reshape(fc7_conv,
self._variables_to_fix[
'vgg_16/fc7/weights:0'].get_shape())))
sess.run(tf.assign(self._variables_to_fix['vgg_16/conv1/conv1_1/weights:0'],
tf.reverse(conv1_rgb, [2])))
# restorer_cbp_noise = tf.train.Saver({"vgg_16/fc6/weights": cbp_fc6_conv,
# "vgg_16/fc7/weights": cbp_fc7_conv,
# "vgg_16/conv1/conv1_1/weights": noise_conv1_rgb})
# restorer_cbp_noise.restore(sess, pretrained_model)
#
# sess.run(tf.assign(self._variables_to_fix['vgg_16/cbp_fc6/weights:0'], tf.reshape(cbp_fc6_conv,
# self._variables_to_fix[
# 'vgg_16/cbp_fc6/weights:0'].get_shape())))
# sess.run(tf.assign(self._variables_to_fix['vgg_16/cbp_fc7/weights:0'], tf.reshape(cbp_fc7_conv,
# self._variables_to_fix[
# 'vgg_16/cbp_fc7/weights:0'].get_shape())))
# sess.run(tf.assign(self._variables_to_fix['vgg_16/conv1n/conv1n_1/weights:0'],
# tf.reverse(noise_conv1_rgb, [2])))
def build_head(self, is_training):
# Main network
# Layer 1
net = slim.repeat(self._image, 2, slim.conv2d, 64, [3, 3], trainable=False, scope='conv1')
net = slim.max_pool2d(net, [2, 2], padding='SAME', scope='pool1')
# Layer 2
net = slim.repeat(net, 2, slim.conv2d, 128, [3, 3], trainable=False, scope='conv2')
net = slim.max_pool2d(net, [2, 2], padding='SAME', scope='pool2')
# Layer 3
net = slim.repeat(net, 3, slim.conv2d, 256, [3, 3], trainable=is_training, scope='conv3')
net = slim.max_pool2d(net, [2, 2], padding='SAME', scope='pool3')
# Layer 4
net = slim.repeat(net, 3, slim.conv2d, 512, [3, 3], trainable=is_training, scope='conv4')
net = slim.max_pool2d(net, [2, 2], padding='SAME', scope='pool4')
# Layer 5
net = slim.repeat(net, 3, slim.conv2d, 512, [3, 3], trainable=is_training, scope='conv5')
# Append network to summaries
self._act_summaries.append(net)
# Append network as head layer
self._layers['head'] = net
return net
def build_head_forNoise(self, is_training, initializer, initializer_srm):
def truncate_2(x):
neg = ((x + 2) + abs(x + 2)) / 2 - 2
return -(2 - neg + abs(2 - neg)) / 2 + 2
# Main network
# Layer SRM
net = slim.conv2d(self._image, 3, [5, 5], trainable=False, weights_initializer=initializer_srm,
activation_fn=None, padding='SAME', stride=1, scope='srm')
net = truncate_2(net)
# Layer 1
net = slim.repeat(net, 2, slim.conv2d, 64, [3, 3], trainable=is_training, weights_initializer=initializer, scope='conv1n')
net = slim.max_pool2d(net, [2, 2], padding='SAME', scope='pool1n')
# Layer 2
net = slim.repeat(net, 2, slim.conv2d, 128, [3, 3], trainable=is_training, weights_initializer=initializer, scope='conv2n')
net = slim.max_pool2d(net, [2, 2], padding='SAME', scope='pool2n')
# Layer 3
net = slim.repeat(net, 3, slim.conv2d, 256, [3, 3], trainable=is_training, weights_initializer=initializer, scope='conv3n')
net = slim.max_pool2d(net, [2, 2], padding='SAME', scope='pool3n')
# Layer 4
net = slim.repeat(net, 3, slim.conv2d, 512, [3, 3], trainable=is_training, weights_initializer=initializer, scope='conv4n')
net = slim.max_pool2d(net, [2, 2], padding='SAME', scope='pool4n')
# Layer 5
net = slim.repeat(net, 3, slim.conv2d, 512, [3, 3], trainable=is_training, weights_initializer=initializer, scope='conv5n')
# Append network to summaries
self._act_summaries.append(net)
# Append network as head layer
self._layers['head2'] = net
return net
def build_rpn(self, net, is_training, initializer):
# Build anchor component
self._anchor_component()
# Create RPN Layer
rpn = slim.conv2d(net, 512, [3, 3], trainable=is_training, weights_initializer=initializer, scope="rpn_conv/3x3")
self._act_summaries.append(rpn)
rpn_cls_score = slim.conv2d(rpn, self._num_anchors * 2, [1, 1], trainable=is_training, weights_initializer=initializer, padding='VALID', activation_fn=None, scope='rpn_cls_score')
# Change it so that the score has 2 as its channel size
rpn_cls_score_reshape = self._reshape_layer(rpn_cls_score, 2, 'rpn_cls_score_reshape')
rpn_cls_prob_reshape = self._softmax_layer(rpn_cls_score_reshape, "rpn_cls_prob_reshape")
rpn_cls_prob = self._reshape_layer(rpn_cls_prob_reshape, self._num_anchors * 2, "rpn_cls_prob")
rpn_bbox_pred = slim.conv2d(rpn, self._num_anchors * 4, [1, 1], trainable=is_training, weights_initializer=initializer, padding='VALID', activation_fn=None, scope='rpn_bbox_pred')
return rpn_cls_prob, rpn_bbox_pred, rpn_cls_score, rpn_cls_score_reshape
def build_proposals(self, is_training, rpn_cls_prob, rpn_bbox_pred, rpn_cls_score):
if is_training:
rois, roi_scores = self._proposal_layer(rpn_cls_prob, rpn_bbox_pred, "rois")
rpn_labels = self._anchor_target_layer(rpn_cls_score, "anchor")
# Try to have a deterministic order for the computing graph, for reproducibility
with tf.control_dependencies([rpn_labels]):
rois, _ = self._proposal_target_layer(rois, roi_scores, "rpn_rois")
else:
if cfg.FLAGS.test_mode == 'nms':
rois, _ = self._proposal_layer(rpn_cls_prob, rpn_bbox_pred, "rois")
elif cfg.FLAGS.test_mode == 'top':
rois, _ = self._proposal_top_layer(rpn_cls_prob, rpn_bbox_pred, "rois")
else:
raise NotImplementedError
return rois
def build_predictions(self, net, net2, rois, is_training, initializer, initializer_bbox):
# Crop image ROIs
pool5 = self._crop_pool_layer(net, rois, "pool5")
# pool5_flat = slim.flatten(pool5, scope='flatten')
pool5_forNoise = self._crop_pool_layer(net2, rois, "pool5_forNoise")
# Compact Bilinear Pooling
cbp = compact_bilinear_pooling_layer(pool5, pool5_forNoise, 512)
cbp_flat = slim.flatten(cbp, scope='cbp_flatten')
# Fully connected layers
# fc6 = slim.fully_connected(pool5_flat, 4096, scope='bbox_fc6')
fc6_cbp = slim.fully_connected(cbp_flat, 4096, scope='fc6')
if is_training:
# fc6 = slim.dropout(fc6, keep_prob=0.5, is_training=True, scope='dropout6')
fc6_cbp = slim.dropout(fc6_cbp, keep_prob=0.5, is_training=True, scope='cbp_dropout6')
# fc7 = slim.fully_connected(fc6, 4096, scope='bbox_fc7')
fc7_cbp = slim.fully_connected(fc6_cbp, 4096, scope='fc7')
if is_training:
# fc7 = slim.dropout(fc7, keep_prob=0.5, is_training=True, scope='dropout7')
fc7_cbp = slim.dropout(fc7_cbp, keep_prob=0.5, is_training=True, scope='cbp_dropout7')
# Scores and predictions
cls_score = slim.fully_connected(fc7_cbp, self._num_classes, weights_initializer=initializer, trainable=is_training, activation_fn=None, scope='cls_score')
cls_prob = self._softmax_layer(cls_score, "cls_prob")
bbox_prediction = slim.fully_connected(fc7_cbp, self._num_classes * 4, weights_initializer=initializer_bbox, trainable=is_training, activation_fn=None, scope='bbox_pred')
return cls_score, cls_prob, bbox_prediction