-
Notifications
You must be signed in to change notification settings - Fork 26
/
vgg16.py
233 lines (166 loc) · 9.37 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
# Adapted from : VGG 16 model : https://github.com/machrisaa/tensorflow-vgg
import time
import os
import inspect
import numpy as np
from termcolor import colored
import tensorflow as tf
from losses import sigmoid_cross_entropy_balanced
import pdb
#from io import IO
VGG_MEAN = [103.939, 116.779, 123.68]
class Vgg16():
def __init__(self, input_image,reuse=None):
# self.cfgs 1= cfgs
# self.io = IO()
base_path = os.path.abspath(os.path.dirname(__file__))
weights_file = os.path.join(base_path, 'vgg16.npy')
self.data_dict = np.load(weights_file, encoding='latin1').item()
# self.io.print_info("Model weights loaded from {}".format(self.cfgs['model_weights_path']))
rgb_scaled = tf.subtract((input_image+tf.ones_like(input_image)),2)*255.
red, green, blue = tf.split(rgb_scaled, 3, 3)
self.images = tf.concat([blue - VGG_MEAN[0],
green - VGG_MEAN[1],
red - VGG_MEAN[2]],
3)
# self.images = tf.placeholder(tf.float32, [None, self.cfgs[run]['image_height'], self.cfgs[run]['image_width'], self.cfgs[run]['n_channels']])
# self.edgemaps = tf.placeholder(tf.float32, [None, self.cfgs[run]['image_height'], self.cfgs[run]['image_width'], 1])
self.define_model(reuse=reuse)
def define_model(self,reuse=None):
"""
Load VGG params from disk without FC layers A
Add branch layers (with deconv) after each CONV block
"""
with tf.variable_scope('hed'):
start_time = time.time()
self.conv1_1 = self.conv_layer_vgg(self.images, "conv1_1")
self.conv1_2 = self.conv_layer_vgg(self.conv1_1, "conv1_2")
self.side_1 = self.side_layer(self.conv1_2, "side_1", 1,reuse=reuse)
self.pool1 = self.max_pool(self.conv1_2, 'pool1')
# self.io.print_info('Added CONV-BLOCK-1+SIDE-1')
self.conv2_1 = self.conv_layer_vgg(self.pool1, "conv2_1")
self.conv2_2 = self.conv_layer_vgg(self.conv2_1, "conv2_2")
self.side_2 = self.side_layer(self.conv2_2, "side_2", 2,reuse=reuse)
self.pool2 = self.max_pool(self.conv2_2, 'pool2')
# self.io.print_info('Added CONV-BLOCK-2+SIDE-2')
self.conv3_1 = self.conv_layer_vgg(self.pool2, "conv3_1")
self.conv3_2 = self.conv_layer_vgg(self.conv3_1, "conv3_2")
self.conv3_3 = self.conv_layer_vgg(self.conv3_2, "conv3_3")
self.side_3 = self.side_layer(self.conv3_3, "side_3", 4,reuse=reuse)
self.pool3 = self.max_pool(self.conv3_3, 'pool3')
# self.io.print_info('Added CONV-BLOCK-3+SIDE-3')
self.conv4_1 = self.conv_layer_vgg(self.pool3, "conv4_1")
self.conv4_2 = self.conv_layer_vgg(self.conv4_1, "conv4_2")
self.conv4_3 = self.conv_layer_vgg(self.conv4_2, "conv4_3")
self.side_4 = self.side_layer(self.conv4_3, "side_4", 8,reuse=reuse)
self.pool4 = self.max_pool(self.conv4_3, 'pool4')
# self.io.print_info('Added CONV-BLOCK-4+SIDE-4')
self.conv5_1 = self.conv_layer_vgg(self.pool4, "conv5_1")
self.conv5_2 = self.conv_layer_vgg(self.conv5_1, "conv5_2")
self.conv5_3 = self.conv_layer_vgg(self.conv5_2, "conv5_3")
self.side_5 = self.side_layer(self.conv5_3, "side_5", 16,reuse=reuse)
# self.io.print_info('Added CONV-BLOCK-5+SIDE-5')
self.side_outputs = [self.side_1, self.side_2, self.side_3, self.side_4, self.side_5]
w_shape = [1, 1, len(self.side_outputs), 1]
if reuse == True:
tf.get_variable_scope().reuse_variables()
self.fuse = self.conv_layer(tf.concat(self.side_outputs, axis=3),
w_shape, name='fuse_1', use_bias=False,
w_init=tf.constant_initializer(0.2))
#tf.get_variable_scope().reuse == False
# self.io.print_info('Added FUSE layer')
# complete output maps from side layer and fuse layers
self.outputs = self.side_outputs + [self.fuse]
self.data_dict = None
# self.io.print_info("Build model finished: {:.4f}s".format(time.time() - start_time))
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_vgg(self, bottom, name):
"""
Adding a conv layer + weight parameters from a dict
"""
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 conv_layer(self, x, W_shape, b_shape=None, name=None,
padding='SAME', use_bias=True, w_init=None, b_init=None):
W = self.weight_variable(W_shape, w_init, 'Variable')
tf.summary.histogram('weights_{}'.format(name), W)
if use_bias:
b = self.bias_variable([b_shape], b_init, 'Variable_1')
tf.summary.histogram('biases_{}'.format(name), b)
conv = tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding=padding)
return conv + b if use_bias else conv
def deconv_layer(self, x, upscale, name, padding='SAME', w_init=None):
x_shape = tf.shape(x)
in_shape = x.shape.as_list()
w_shape = [upscale * 2, upscale * 2, in_shape[-1], 1]
strides = [1, upscale, upscale, 1]
W = self.weight_variable(w_shape, w_init, 'Variable_2')
tf.summary.histogram('weights_{}'.format(name), W)
out_shape = tf.stack([x_shape[0], x_shape[1], x_shape[2], w_shape[2]]) * tf.constant(strides, tf.int32)
deconv = tf.nn.conv2d_transpose(x, W, out_shape, strides=strides, padding=padding)
return deconv
def side_layer(self, inputs, name, upscale,reuse=None):
"""
https://github.com/s9xie/hed/blob/9e74dd710773d8d8a469ad905c76f4a7fa08f945/examples/hed/train_val.prototxt#L122
1x1 conv followed with Deconvoltion layer to upscale the size of input image sans color
"""
with tf.variable_scope(name,reuse=reuse):
in_shape = inputs.shape.as_list()
w_shape = [1, 1, in_shape[-1], 1]
classifier = self.conv_layer(inputs, w_shape, b_shape=1,
w_init=tf.constant_initializer(),
b_init=tf.constant_initializer(),
name=name + '_reduction')
classifier = self.deconv_layer(classifier, upscale=upscale,
name='{}_deconv_{}'.format(name, upscale),
w_init=tf.truncated_normal_initializer(stddev=0.1))
return classifier
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 weight_variable(self, shape, initial, name):
return tf.get_variable(name, shape=shape, initializer=initial)
def bias_variable(self, shape, initial, name):
return tf.get_variable(name, shape=shape, initializer=initial)
def setup_testing(self, session):
"""
Apply sigmoid non-linearity to side layer ouputs + fuse layer outputs for predictions
"""
self.predictions = []
for idx, b in enumerate(self.outputs):
output = tf.nn.sigmoid(b, name='output_{}'.format(idx))
self.predictions.append(output)
def setup_training(self, session):
"""
Apply sigmoid non-linearity to side layer ouputs + fuse layer outputs
Compute total loss := side_layer_loss + fuse_layer_loss
Compute predicted edge maps from fuse layer as pseudo performance metric to track
"""
self.predictions = []
self.loss = 0
self.io.print_warning('Deep supervision application set to {}'.format(self.cfgs['deep_supervision']))
for idx, b in enumerate(self.side_outputs):
output = tf.nn.sigmoid(b, name='output_{}'.format(idx))
cost = sigmoid_cross_entropy_balanced(b, self.edgemaps, name='cross_entropy{}'.format(idx))
self.predictions.append(output)
if self.cfgs['deep_supervision']:
self.loss += (self.cfgs['loss_weights'] * cost)
fuse_output = tf.nn.sigmoid(self.fuse, name='fuse')
fuse_cost = sigmoid_cross_entropy_balanced(self.fuse, self.edgemaps, name='cross_entropy_fuse')
self.predictions.append(fuse_output)
self.loss += (self.cfgs['loss_weights'] * fuse_cost)
pred = tf.cast(tf.greater(fuse_output, 0.5), tf.int32, name='predictions')
error = tf.cast(tf.not_equal(pred, tf.cast(self.edgemaps, tf.int32)), tf.float32)
self.error = tf.reduce_mean(error, name='pixel_error')
tf.summary.scalar('loss', self.loss)
tf.summary.scalar('error', self.error)
self.merged_summary = tf.summary.merge_all()
self.train_writer = tf.summary.FileWriter(self.cfgs['save_dir'] + '/train', session.graph)
self.val_writer = tf.summary.FileWriter(self.cfgs['save_dir'] + '/val')