3D_CNN_Example/classify_type.py at master · rceballos98/3D_CNN_Example · GitHub

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
dansileshi/conv3dnet.py forked from akors/conv3dnet.py
Created 11 months ago


import tensorflow as tf
import numpy as np

FC_SIZE = 1024
DTYPE = tf.float32


def _weight_variable(name, shape):
    return tf.get_variable(name, shape, DTYPE, tf.truncated_normal_initializer(stddev=0.1))


def _bias_variable(name, shape):
    return tf.get_variable(name, shape, DTYPE, tf.constant_initializer(0.1, dtype=DTYPE))


def inference(boxes, dataconfig):
    prev_layer = boxes

    in_filters = dataconfig.num_props
    with tf.variable_scope('conv1') as scope:
        out_filters = 16
        kernel = _weight_variable('weights', [5, 5, 5, in_filters, out_filters])
        conv = tf.nn.conv3d(prev_layer, kernel, [1, 1, 1, 1, 1], padding='SAME')
        biases = _bias_variable('biases', [out_filters])
        bias = tf.nn.bias_add(conv, biases)
        conv1 = tf.nn.relu(bias, name=scope.name)

        prev_layer = conv1
        in_filters = out_filters

    pool1 = tf.nn.max_pool3d(prev_layer, ksize=[1, 3, 3, 3, 1], strides=[1, 2, 2, 2, 1], padding='SAME')
    norm1 = pool1  # tf.nn.lrn(pool1, 4, bias=1.0, alpha=0.001 / 9.0, beta = 0.75, name='norm1')

    prev_layer = norm1

    with tf.variable_scope('conv2') as scope:
        out_filters = 32
        kernel = _weight_variable('weights', [5, 5, 5, in_filters, out_filters])
        conv = tf.nn.conv3d(prev_layer, kernel, [1, 1, 1, 1, 1], padding='SAME')
        biases = _bias_variable('biases', [out_filters])
        bias = tf.nn.bias_add(conv, biases)
        conv2 = tf.nn.relu(bias, name=scope.name)

        prev_layer = conv2
        in_filters = out_filters

    # normalize prev_layer here
    prev_layer = tf.nn.max_pool3d(prev_layer, ksize=[1, 3, 3, 3, 1], strides=[1, 2, 2, 2, 1], padding='SAME')

    with tf.variable_scope('conv3_1') as scope:
        out_filters = 64
        kernel = _weight_variable('weights', [5, 5, 5, in_filters, out_filters])
        conv = tf.nn.conv3d(prev_layer, kernel, [1, 1, 1, 1, 1], padding='SAME')
        biases = _bias_variable('biases', [out_filters])
        bias = tf.nn.bias_add(conv, biases)
        prev_layer = tf.nn.relu(bias, name=scope.name)
        in_filters = out_filters

    with tf.variable_scope('conv3_2') as scope:
        out_filters = 64
        kernel = _weight_variable('weights', [5, 5, 5, in_filters, out_filters])
        conv = tf.nn.conv3d(prev_layer, kernel, [1, 1, 1, 1, 1], padding='SAME')
        biases = _bias_variable('biases', [out_filters])
        bias = tf.nn.bias_add(conv, biases)
        prev_layer = tf.nn.relu(bias, name=scope.name)
        in_filters = out_filters

    with tf.variable_scope('conv3_3') as scope:
        out_filters = 32
        kernel = _weight_variable('weights', [5, 5, 5, in_filters, out_filters])
        conv = tf.nn.conv3d(prev_layer, kernel, [1, 1, 1, 1, 1], padding='SAME')
        biases = _bias_variable('biases', [out_filters])
        bias = tf.nn.bias_add(conv, biases)
        prev_layer = tf.nn.relu(bias, name=scope.name)
        in_filters = out_filters

    # normalize prev_layer here
    prev_layer = tf.nn.max_pool3d(prev_layer, ksize=[1, 3, 3, 3, 1], strides=[1, 2, 2, 2, 1], padding='SAME')


    with tf.variable_scope('local3') as scope:
        dim = np.prod(prev_layer.get_shape().as_list()[1:])
        prev_layer_flat = tf.reshape(prev_layer, [-1, dim])
        weights = _weight_variable('weights', [dim, FC_SIZE])
        biases = _bias_variable('biases', [FC_SIZE])
        local3 = tf.nn.relu(tf.matmul(prev_layer_flat, weights) + biases, name=scope.name)

    prev_layer = local3

    with tf.variable_scope('local4') as scope:
        dim = np.prod(prev_layer.get_shape().as_list()[1:])
        prev_layer_flat = tf.reshape(prev_layer, [-1, dim])
        weights = _weight_variable('weights', [dim, FC_SIZE])
        biases = _bias_variable('biases', [FC_SIZE])
        local4 = tf.nn.relu(tf.matmul(prev_layer_flat, weights) + biases, name=scope.name)

    prev_layer = local4

    with tf.variable_scope('softmax_linear') as scope:
        dim = np.prod(prev_layer.get_shape().as_list()[1:])
        weights = _weight_variable('weights', [dim, dataconfig.num_classes])
        biases = _bias_variable('biases', [dataconfig.num_classes])
        softmax_linear = tf.add(tf.matmul(prev_layer, weights), biases, name=scope.name)

    return softmax_linear


def loss(logits, labels):
    cross_entropy = tf.nn.softmax_cross_entropy_with_logits(
        logits, labels, name='cross_entropy_per_example')

    return tf.reduce_mean(cross_entropy, name='xentropy_mean')