-
Notifications
You must be signed in to change notification settings - Fork 7
/
mobilenet.py
326 lines (280 loc) · 14.7 KB
/
mobilenet.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
315
316
317
318
319
320
321
322
323
324
325
326
#coding=utf-8
# 定义mobilenet_v1和mobilenet_v2
# 进度：已完成
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
# Python提供了__future__模块，把下一个新版本的特性导入到当前版本，
# 于是我们就可以在当前版本中测试一些新版本的特性。
import sys
import os
import const
import tensorflow as tf
# mobilenet_v1网络定义
def mobilenet_v1(inputs, alpha, is_training):
assert const.use_batch_norm == True
# assert断言是声明其布尔值必须为真的判定，如果发生异常就说明表达示为假
# 缩小因子， 只能为1，0.75，0.5，0.25
if alpha not in [0.25, 0.50, 0.75, 1.0]:
raise ValueError('alpha can be one of'
'`0.25`, `0.50`, `0.75` or `1.0` only.')
filter_initializer = tf.contrib.layers.xavier_initializer()
# 卷积，BN，RELU
def _conv2d(inputs, filters, kernel_size, stride, scope=''):
with tf.variable_scope(scope):
outputs = tf.layers.conv2d(inputs, filters, kernel_size,
strides=(stride, stride), padding='same',
activation=None, use_bias=False,
kernel_initializer=filter_initializer)
# 非线性激活之前进行BN批标准化
outputs = tf.layers.batch_normalization(outputs, training=is_training)
outputs = tf.nn.relu(outputs)
return outputs
# 深度可分离卷积，标准卷积分解成深度卷积(depthwise convolution)和逐点卷积(pointwise convolution)
def _depthwise_conv2d(inputs,
pointwise_conv_filters,
depthwise_conv_kernel_size,
stride,
scope=''):
with tf.variable_scope(scope):
with tf.variable_scope('depthwise_conv'):
outputs = tf.contrib.layers.separable_conv2d(
inputs,
None,
depthwise_conv_kernel_size,
depth_multiplier=1,
stride=(stride, stride),
padding='SAME',
activation_fn=None,
weights_initializer=filter_initializer,
biases_initializer=None)
outputs = tf.layers.batch_normalization(outputs, training=is_training)
outputs = tf.nn.relu(outputs)
with tf.variable_scope('pointwise_conv'):
pointwise_conv_filters = int(pointwise_conv_filters * alpha)
outputs = tf.layers.conv2d(outputs,
pointwise_conv_filters,
(1,1),
padding='same',
activation=None,
use_bias=False,
kernel_initializer=filter_initializer)
outputs = tf.layers.batch_normalization(outputs, training=is_training)
outputs = tf.nn.relu(outputs)
return outputs
# 平均池化
def _avg_pool2d(inputs, scope=''):
inputs_shape = inputs.get_shape().as_list()
assert len(inputs_shape) == 4
pool_height = inputs_shape[1]
pool_width = inputs_shape[2]
with tf.variable_scope(scope):
outputs = tf.layers.average_pooling2d(inputs,
[pool_height, pool_width],
strides=(1, 1),
padding='valid')
return outputs
'''
执行分类任务的网络结构，通常还可以作为实现其他任务的网络结构的 base architecture，
为了方便代码复用，这里只需要实现出卷积层构成的主体部分，
外部调用者根据各自的需求使用这里返回的 output 和 end_points。
比如对于分类任务，按照如下方式使用这个函数
image_height = 224
image_width = 224
image_channels = 3
x = tf.placeholder(tf.float32, [None, image_height, image_width, image_channels])
is_training = tf.placeholder(tf.bool, name='is_training')
output, net = mobilenet_v1(x, 1.0, is_training)
print('output shape is: %r' % (output.get_shape().as_list()))
output = tf.layers.flatten(output)
output = tf.layers.dense(output,
units=1024, # 1024 class
activation=None,
use_bias=True,
kernel_initializer=tf.contrib.layers.xavier_initializer())
print('output shape is: %r' % (output.get_shape().as_list()))
'''
with tf.variable_scope('mobilenet_v1', 'mobilenet_v1', [inputs]):
end_points = {}
net = inputs
net = _conv2d(net, 32, [3,3], stride=2, scope='block0')
end_points['block0'] = net
net = _depthwise_conv2d(net, 64, [3, 3], stride=1, scope='block1')
end_points['block1'] = net
net = _depthwise_conv2d(net, 128, [3, 3], stride=2, scope='block2')
end_points['block2'] = net
net = _depthwise_conv2d(net, 128, [3, 3], stride=1, scope='block3')
end_points['block3'] = net
net = _depthwise_conv2d(net, 256, [3, 3], stride=2, scope='block4')
end_points['block4'] = net
net = _depthwise_conv2d(net, 256, [3, 3], stride=1, scope='block5')
end_points['block5'] = net
net = _depthwise_conv2d(net, 512, [3, 3], stride=2, scope='block6')
end_points['block6'] = net
net = _depthwise_conv2d(net, 512, [3, 3], stride=1, scope='block7')
end_points['block7'] = net
net = _depthwise_conv2d(net, 512, [3, 3], stride=1, scope='block8')
end_points['block8'] = net
net = _depthwise_conv2d(net, 512, [3, 3], stride=1, scope='block9')
end_points['block9'] = net
net = _depthwise_conv2d(net, 512, [3, 3], stride=1, scope='block10')
end_points['block10'] = net
net = _depthwise_conv2d(net, 512, [3, 3], stride=1, scope='block11')
end_points['block11'] = net
net = _depthwise_conv2d(net, 1024, [3, 3], stride=2, scope='block12')
end_points['block12'] = net
net = _depthwise_conv2d(net, 1024, [3, 3], stride=1, scope='block13')
end_points['block13'] = net
output = _avg_pool2d(net, scope='output')
return output, end_points
# mobilenet_v2网络定义
def mobilenet_v2_func_blocks(is_training):
assert const.use_batch_norm == True
filter_initializer = tf.contrib.layers.xavier_initializer()
activation_func = tf.nn.relu6
# 普通卷积
def conv2d(inputs, filters, kernel_size, stride, scope=''):
with tf.variable_scope(scope):
with tf.variable_scope('conv2d'):
outputs = tf.layers.conv2d(inputs, filters, kernel_size, strides=(stride, stride),
padding='same', activation=None, use_bias=False,
kernel_initializer=filter_initializer)
outputs = tf.layers.batch_normalization(outputs, training=is_training)
outputs = tf.nn.relu(outputs)
return outputs
# 逐点卷积
def _1x1_conv2d(inputs, filters, stride):
kernel_size = [1, 1]
with tf.variable_scope('1x1_conv2d'):
outputs = tf.layers.conv2d(inputs, filters, kernel_size, strides=(stride, stride),
padding='same', activation=None,use_bias=False,
kernel_initializer=filter_initializer)
outputs = tf.layers.batch_normalization(outputs, training=is_training)
return outputs
# 升维
def expansion_conv2d(inputs, expansion, stride):
input_shape = inputs.get_shape().as_list()
assert len(input_shape) == 4
filters = input_shape[3] * expansion
kernel_size = [1, 1]
with tf.variable_scope('expansion_1x1_conv2d'):
outputs = tf.layers.conv2d(inputs, filters, kernel_size, strides=(stride, stride),
padding='same', activation=None, use_bias=False,
kernel_initializer=filter_initializer)
outputs = tf.layers.batch_normalization(outputs, training=is_training)
outputs = activation_func(outputs)
return outputs
# 降维
def projection_conv2d(inputs, filters, stride):
kernel_size = [1, 1]
with tf.variable_scope('projection_1x1_conv2d'):
outputs = tf.layers.conv2d(inputs, filters, kernel_size, strides=(stride, stride),
padding='same', activation=None, use_bias=False,
kernel_initializer=filter_initializer)
outputs = tf.layers.batch_normalization(outputs, training=is_training)
return outputs
# 深度可分离卷积
def depthwise_conv2d(inputs, depthwise_conv_kernel_size,stride):
with tf.variable_scope('depthwise_conv2d'):
outputs = tf.contrib.layers.separable_conv2d(
inputs,
None,
depthwise_conv_kernel_size,
depth_multiplier=1,
stride=(stride,stride),
padding='SAME',
activation_fn=None,
weights_initializer=filter_initializer,
biases_initializer=None)
outputs = tf.layers.batch_normalization(outputs, training=is_training)
outputs = activation_func(outputs)
return outputs
# 池化
def avg_pool2d(inputs, scope=''):
inputs_shape = inputs.get_shape().as_list()
assert len(inputs_shape) == 4
pool_height = inputs_shape[1]
pool_width = inputs_shape[2]
with tf.variable_scope(scope):
outputs = tf.layers.average_pooling2d(inputs, [pool_height, pool_width],
strides=(1, 1),padding='valid')
return outputs
# 倒置的残差
def inverted_residual_block(inputs, filters, stride, expansion=6,scope=''):
assert stride == 1 or stride == 2
depthwise_conv_kernel_size = [3, 3]
pointwise_conv_filters = filters
with tf.variable_scope(scope):
net = inputs
net = expansion_conv2d(net, expansion, stride=1) # 升维
net = depthwise_conv2d(net, depthwise_conv_kernel_size, stride=stride) # 特征提取
net = projection_conv2d(net, pointwise_conv_filters, stride=1) # 降维
if stride == 1:
# print('----------------- test, net.get_shape().as_list()[3] = %r' % net.get_shape().as_list()[3])
# print('----------------- test, inputs.get_shape().as_list()[3] = %r' % inputs.get_shape().as_list()[3])
# 如果 net.get_shape().as_list()[3] != inputs.get_shape().as_list()[3]
# 借助一个 1x1 的卷积让他们的 channels 相等，然后再相加
if net.get_shape().as_list()[3] != inputs.get_shape().as_list()[3]:
inputs = _1x1_conv2d(inputs, net.get_shape().as_list()[3], stride=1)
net = net + inputs
return net
else:
# stride == 2
return net
# 定义功能块的集合
func_blocks = {}
func_blocks['conv2d'] = conv2d
func_blocks['inverted_residual_block'] = inverted_residual_block
func_blocks['avg_pool2d'] = avg_pool2d
func_blocks['filter_initializer'] = filter_initializer
func_blocks['activation_func'] = activation_func
return func_blocks
def mobilenet_v2(inputs, is_training):
assert const.use_batch_norm == True
func_blocks = mobilenet_v2_func_blocks(is_training)
_conv2d = func_blocks['conv2d']
_inverted_residual_block = func_blocks['inverted_residual_block']
_avg_pool2d = func_blocks['avg_pool2d']
with tf.variable_scope('mobilenet_v2', 'mobilenet_v2', [inputs]):
end_points = {}
net = inputs
net = _conv2d(net, 32, [3, 3], stride=2, scope='block0_0')
end_points['block0'] = net
print('!! debug block0, net shape is: {}'.format(net.get_shape()))
net = _inverted_residual_block(net, 16, stride=1, expansion=1, scope='block1_0')
end_points['block1'] = net
print('!! debug block1, net shape is: {}'.format(net.get_shape()))
net = _inverted_residual_block(net, 24, stride=2, scope='block2_0')
net = _inverted_residual_block(net, 24, stride=1, scope='block2_1')
end_points['block2'] = net
print('!! debug block2, net shape is: {}'.format(net.get_shape()))
net = _inverted_residual_block(net, 32, stride=2, scope='block3_0')
net = _inverted_residual_block(net, 32, stride=1, scope='block3_1')
net = _inverted_residual_block(net, 32, stride=1, scope='block3_2')
end_points['block3'] = net
print('!! debug block3, net shape is: {}'.format(net.get_shape()))
net = _inverted_residual_block(net, 64, stride=2, scope='block4_0')
net = _inverted_residual_block(net, 64, stride=1, scope='block4_1')
net = _inverted_residual_block(net, 64, stride=1, scope='block4_2')
net = _inverted_residual_block(net, 64, stride=1, scope='block4_3')
end_points['block4'] = net
print('!! debug block4, net shape is: {}'.format(net.get_shape()))
net = _inverted_residual_block(net, 96, stride=1, scope='block5_0')
net = _inverted_residual_block(net, 96, stride=1, scope='block5_1')
net = _inverted_residual_block(net, 96, stride=1, scope='block5_2')
end_points['block5'] = net
print('!! debug block5, net shape is: {}'.format(net.get_shape()))
net = _inverted_residual_block(net, 160, stride=2, scope='block6_0')
net = _inverted_residual_block(net, 160, stride=1, scope='block6_1')
net = _inverted_residual_block(net, 160, stride=1, scope='block6_2')
end_points['block6'] = net
print('!! debug block6, net shape is: {}'.format(net.get_shape()))
net = _inverted_residual_block(net, 320, stride=1, scope='block7_0')
end_points['block7'] = net
print('!! debug block7, net shape is: {}'.format(net.get_shape()))
net = _conv2d(net, 1280, [1, 1], stride=1, scope='block8_0')
end_points['block8'] = net
print('!! debug block8, net shape is: {}'.format(net.get_shape()))
output = _avg_pool2d(net, scope='output')
print('!! debug after avg_pool, net shape is: {}'.format(output.get_shape()))
return output, end_points