-
Notifications
You must be signed in to change notification settings - Fork 0
/
vggsnet.py
179 lines (129 loc) · 6.31 KB
/
vggsnet.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
"""This is an TensorFLow implementation of AlexNet by Alex Krizhevsky at all.
Paper:
(http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf)
Explanation can be found in my blog post:
https://kratzert.github.io/2017/02/24/finetuning-alexnet-with-tensorflow.html
This script enables finetuning AlexNet on any given Dataset with any number of
classes. The structure of this script is strongly inspired by the fast.ai
Deep Learning class by Jeremy Howard and Rachel Thomas, especially their vgg16
finetuning script:
Link:
- https://github.com/fastai/courses/blob/master/deeplearning1/nbs/vgg16.py
The pretrained weights can be downloaded here and should be placed in the same
folder as this file:
- http://www.cs.toronto.edu/~guerzhoy/tf_alexnet/
@author: Frederik Kratzert (contact: f.kratzert(at)gmail.com)
"""
import tensorflow as tf
import numpy as np
class VggsNet(object):
"""Implementation of the VggsNet."""
def __init__(self, x, keep_prob, num_classes, skip_layer,
weights_path='DEFAULT'):
"""Create the graph of the AlexNet model.
Args:
x: Placeholder for the input tensor.
keep_prob: Dropout probability.
num_classes: Number of classes in the dataset.
skip_layer: List of names of the layer, that get trained from
scratch
weights_path: Complete path to the pretrained weight file, if it
isn't in the same folder as this code
"""
# Parse input arguments into class variables
self.X = x
self.NUM_CLASSES = num_classes
self.KEEP_PROB = keep_prob
self.SKIP_LAYER = skip_layer
if weights_path == 'DEFAULT':
self.WEIGHTS_PATH = 'prevgg.npy'
else:
self.WEIGHTS_PATH = weights_path
# Call the create function to build the computational graph of AlexNet
self.create()
def create(self):
# """Create the network graph."""
conv1 = conv(self.X,7, 7, 96, 2, 2, padding='VALID', name='conv1')
norm1 = lrn(conv1,2, 0.00010000000475, 0.75, name='norm1')
pool1 = max_pool(norm1,3, 3, 3, 3, name='pool1')
conv2 = conv(pool1,5, 5, 256, 1, 1, padding='VALID', name='conv2')
pool2 = max_pool(conv2,2, 2, 2, 2, name='pool2')
conv3 = conv(pool2,3, 3, 512, 1, 1, name='conv3')
conv4 = conv(conv3,3, 3, 512, 1, 1, name='conv4')
conv5 = conv(conv4,3, 3, 512, 1, 1, name='conv5')
pool5 = max_pool(conv5,3, 3, 3, 3, name='pool5')
flattended = tf.reshape(pool5,[-1,6*6*512])
fc6 = fc(flattended,6*6*512,4096, name='fc6')
dropout6 = dropout(fc6, self.KEEP_PROB)
fc7 = fc(dropout6,4096,4096, name='fc7')
dropout7 = dropout(fc7, self.KEEP_PROB)
self.pool5 = pool5
self.fc7 = fc7
self.fc8 = tf.layers.dense(dropout7,self.NUM_CLASSES,name='fc8')
def load_initial_weights(self, session):
"""Load weights from file into network.
As the weights from http://www.cs.toronto.edu/~guerzhoy/tf_alexnet/
Maybe the weights geted from caffe to tensorflow are different to above
weights,it's a dict of dict
"""
# Load the weights into memory
weights_dict = np.load(self.WEIGHTS_PATH, encoding='bytes').item()
# Loop over all layer names stored in the weights dict
for op_name in weights_dict:
# Check if layer should be trained from scratch
if op_name not in self.SKIP_LAYER:
with tf.variable_scope(op_name, reuse=True):
# Assign weights/biases to their corresponding tf variable
for data in weights_dict[op_name]:
# Biases
if data == 'biases':
var = tf.get_variable('biases', trainable=True)
session.run(var.assign(weights_dict[op_name][data]))
# Weights
else:
var = tf.get_variable('weights', trainable=True)
session.run(var.assign(weights_dict[op_name][data]))
def conv(x, filter_height, filter_width, num_filters, stride_y, stride_x, name,
padding='SAME', groups=1):
with tf.variable_scope(name) as scope:
# Create tf variables for the weights and biases of the conv layer
# Get number of input channels
input_channels = int(x.get_shape()[-1])
weights = tf.get_variable('weights', shape=[filter_height,
filter_width,
input_channels,
num_filters])
biases = tf.get_variable('biases', shape=[num_filters])
conv = tf.nn.conv2d(x,weights,strides=[1,stride_y,stride_x,1],padding=padding)
# Apply relu function
relu = tf.nn.relu(conv+biases, name=scope.name)
return relu
def fc(x, num_in, num_out, name, relu=True):
"""Create a fully connected layer."""
with tf.variable_scope(name) as scope:
# Create tf variables for the weights and biases
weights = tf.get_variable('weights', shape=[num_in, num_out],
trainable=True)
biases = tf.get_variable('biases', [num_out], trainable=True)
# Matrix multiply weights and inputs and add bias
act = tf.nn.xw_plus_b(x, weights, biases, name=scope.name)
if relu:
# Apply ReLu non linearity
relu = tf.nn.relu(act)
return relu
else:
return act
def max_pool(x, filter_height, filter_width, stride_y, stride_x, name,
padding='SAME'):
"""Create a max pooling layer."""
return tf.nn.max_pool(x, ksize=[1, filter_height, filter_width, 1],
strides=[1, stride_y, stride_x, 1],
padding=padding, name=name)
def lrn(x, radius, alpha, beta, name, bias=1.0):
"""Create a local response normalization layer."""
return tf.nn.local_response_normalization(x, depth_radius=radius,
alpha=alpha, beta=beta,
bias=bias, name=name)
def dropout(x, keep_prob):
"""Create a dropout layer."""
return tf.nn.dropout(x, keep_prob)