-
Notifications
You must be signed in to change notification settings - Fork 0
/
tiny_face_model.py
241 lines (193 loc) · 9.65 KB
/
tiny_face_model.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
# -*- coding: utf-8 -*-
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow as tf
import numpy as np
import pickle
class Model():
def __init__(self, weight_file_path):
"""Overlay bounding boxes of face on images.
Args:
weight_file_path:
A pretrained weight file in the pickle format
generated by matconvnet_hr101_to_tf.py.
Returns:
None.
"""
self.dtype = tf.float32
self.weight_file_path = weight_file_path
with open(self.weight_file_path, "rb") as f:
self.mat_blocks_dict, self.mat_params_dict = pickle.load(f)
def get_data_by_key(self, key):
"""Helper to access a pretrained model data through a key."""
assert key in self.mat_params_dict, "key: " + key + " not found."
return self.mat_params_dict[key]
def _weight_variable_on_cpu(self, name, shape):
"""Helper to create a weight Variable stored on CPU memory.
Args:
name: name of the variable.
shape: list of ints: (height, width, channel, filter).
Returns:
initializer for Variable.
"""
assert len(shape) == 4
weights = self.get_data_by_key(name + "_filter") # (h, w, channel, filter)
assert list(weights.shape) == shape
initializer = tf.constant_initializer(weights, dtype=self.dtype)
with tf.device('/cpu:0'):
var = tf.get_variable(name + "_w", shape, initializer=initializer, dtype=self.dtype)
return var
def _bias_variable_on_cpu(self, name, shape):
"""Helper to create a bias Variable stored on CPU memory.
Args:
name: name of the variable.
shape: int, filter size.
Returns:
initializer for Variable.
"""
assert isinstance(shape, int)
bias = self.get_data_by_key(name + "_bias")
assert len(bias) == shape
initializer = tf.constant_initializer(bias, dtype=self.dtype)
with tf.device('/cpu:0'):
var = tf.get_variable(name + "_b", shape, initializer=initializer, dtype=self.dtype)
return var
def _bn_variable_on_cpu(self, name, shape):
"""Helper to create a batch normalization Variable stored on CPU memory.
Args:
name: name of the variable.
shape: int, filter size.
Returns:
initializer for Variable.
"""
assert isinstance(shape, int)
name2 = "bn" + name[3:]
if name.startswith("conv"):
name2 = "bn_" + name
scale = self.get_data_by_key(name2 + '_scale')
offset = self.get_data_by_key(name2 + '_offset')
mean = self.get_data_by_key(name2 + '_mean')
variance = self.get_data_by_key(name2 + '_variance')
with tf.device('/cpu:0'):
initializer = tf.constant_initializer(scale, dtype=self.dtype)
scale = tf.get_variable(name2 + "_scale", shape, initializer=initializer, dtype=self.dtype)
initializer = tf.constant_initializer(offset, dtype=self.dtype)
offset = tf.get_variable(name2 + "_offset", shape, initializer=initializer, dtype=self.dtype)
initializer = tf.constant_initializer(mean, dtype=self.dtype)
mean = tf.get_variable(name2 + "_mean", shape, initializer=initializer, dtype=self.dtype)
initializer = tf.constant_initializer(variance, dtype=self.dtype)
variance = tf.get_variable(name2 + "_variance", shape, initializer=initializer, dtype=self.dtype)
return scale, offset, mean, variance
def conv_block(self, bottom, name, shape, strides=[1,1,1,1], padding="SAME",
has_bias=False, add_relu=True, add_bn=True, eps=1.0e-5):
"""Create a block composed of multiple layers:
a conv layer
a batch normalization layer
an activation layer
Args:
bottom: A layer before this block.
name: Name of the block.
shape: List of ints: (height, width, channel, filter).
strides: Strides of conv layer.
padding: Padding of conv layer.
has_bias: Whether a bias term is added.
add_relu: Whether a ReLU layer is added.
add_bn: Whether a batch normalization layer is added.
eps: A small float number to avoid dividing by 0, used in a batch normalization layer.
Returns:
a block of layers
"""
assert len(shape) == 4
weight = self._weight_variable_on_cpu(name, shape)
conv = tf.nn.conv2d(bottom, weight, strides, padding=padding)
if has_bias:
bias = self._bias_variable_on_cpu(name, shape[3])
pre_activation = tf.nn.bias_add(conv, bias) if has_bias else conv
if add_bn:
# scale, offset, mean, variance = self._bn_variable_on_cpu("bn_" + name, shape[-1])
scale, offset, mean, variance = self._bn_variable_on_cpu(name, shape[-1])
pre_activation = tf.nn.batch_normalization(pre_activation, mean, variance, offset, scale, variance_epsilon=eps)
relu = tf.nn.relu(pre_activation) if add_relu else pre_activation
return relu
def conv_trans_layer(self, bottom, name, shape, strides=[1,1,1,1], padding="SAME", has_bias=False):
"""Create a block composed of multiple layers:
a transpose of conv layer
an activation layer
Args:
bottom: A layer before this block.
name: Name of the block.
shape: List of ints: (height, width, channel, filter).
strides: Strides of conv layer.
padding: Padding of conv layer.
has_bias: Whether a bias term is added.
add_relu: Whether a ReLU layer is added.
Returns:
a block of layers
"""
assert len(shape) == 4
weight = self._weight_variable_on_cpu(name, shape)
nb, h, w, nc = tf.split(tf.shape(bottom), num_or_size_splits=4)
output_shape = tf.stack([nb, (h - 1) * strides[1] - 3 + shape[0], (w - 1) * strides[2] - 3 + shape[1], nc])[:, 0]
conv = tf.nn.conv2d_transpose(bottom, weight, output_shape, strides, padding=padding)
if has_bias:
bias = self._bias_variable_on_cpu(name, shape[3])
conv = tf.nn.bias_add(conv, bias) if has_bias else conv
return conv
def residual_block(self, bottom, name, in_channel, neck_channel, out_channel, trunk):
"""Create a residual block.
Args:
bottom: A layer before this block.
name: Name of the block.
in_channel: number of channels in a input tensor.
neck_channel: number of channels in a bottleneck block.
out_channel: number of channels in a output tensor.
trunk: a tensor in a identity path.
Returns:
a block of layers
"""
_strides = [1, 2, 2, 1] if name.startswith("res3a") or name.startswith("res4a") else [1, 1, 1, 1]
res = self.conv_block(bottom, name + '_branch2a', shape=[1, 1, in_channel, neck_channel],
strides=_strides, padding="VALID", add_relu=True)
res = self.conv_block(res, name + '_branch2b', shape=[3, 3, neck_channel, neck_channel],
padding="SAME", add_relu=True)
res = self.conv_block(res, name + '_branch2c', shape=[1, 1, neck_channel, out_channel],
padding="VALID", add_relu=False)
res = trunk + res
res = tf.nn.relu(res)
return res
def tiny_face(self, image):
"""Create a tiny face model.
Args:
image: an input image.
Returns:
a score tensor
"""
img = tf.pad(image, [[0, 0], [3, 3], [3, 3], [0, 0]], "CONSTANT")
conv = self.conv_block(img, 'conv1', shape=[7, 7, 3, 64], strides=[1, 2, 2, 1], padding="VALID", add_relu=True)
pool1 = tf.nn.max_pool(conv, ksize=[1, 3, 3, 1], strides=[1, 2, 2, 1], padding='SAME')
res2a_branch1 = self.conv_block(pool1, 'res2a_branch1', shape=[1, 1, 64, 256], padding="VALID", add_relu=False)
res2a = self.residual_block(pool1, 'res2a', 64, 64, 256, res2a_branch1)
res2b = self.residual_block(res2a, 'res2b', 256, 64, 256, res2a)
res2c = self.residual_block(res2b, 'res2c', 256, 64, 256, res2b)
res3a_branch1 = self.conv_block(res2c, 'res3a_branch1', shape=[1, 1, 256, 512], strides=[1, 2, 2, 1], padding="VALID", add_relu=False)
res3a = self.residual_block(res2c, 'res3a', 256, 128, 512, res3a_branch1)
res3b1 = self.residual_block(res3a, 'res3b1', 512, 128, 512, res3a)
res3b2 = self.residual_block(res3b1, 'res3b2', 512, 128, 512, res3b1)
res3b3 = self.residual_block(res3b2, 'res3b3', 512, 128, 512, res3b2)
res4a_branch1 = self.conv_block(res3b3, 'res4a_branch1', shape=[1, 1, 512, 1024], strides=[1, 2, 2, 1], padding="VALID", add_relu=False)
res4a = self.residual_block(res3b3, 'res4a', 512, 256, 1024, res4a_branch1)
res4b = res4a
for i in range(1, 23):
res4b = self.residual_block(res4b, 'res4b' + str(i), 1024, 256, 1024, res4b)
score_res4 = self.conv_block(res4b, 'score_res4', shape=[1, 1, 1024, 125], padding="VALID",
has_bias=True, add_relu=False, add_bn=False)
score4 = self.conv_trans_layer(score_res4, 'score4', shape=[4, 4, 125, 125], strides=[1, 2, 2, 1], padding="SAME")
score_res3 = self.conv_block(res3b3, 'score_res3', shape=[1, 1, 512, 125], padding="VALID",
has_bias=True, add_bn=False, add_relu=False)
bs, height, width = tf.split(tf.shape(score4), num_or_size_splits=4)[0:3]
_size = tf.convert_to_tensor([height[0], width[0]])
_offsets = tf.zeros([bs[0], 2])
score_res3c = tf.image.extract_glimpse(score_res3, _size, _offsets, centered=True, normalized=False)
score_final = score4 + score_res3c
return score_final