-
Notifications
You must be signed in to change notification settings - Fork 77
/
dcn_vgg.py
63 lines (50 loc) · 2.84 KB
/
dcn_vgg.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
'''
This code is part of the Keras VGG-16 model
'''
from __future__ import print_function
from __future__ import absolute_import
from keras.models import Model
from keras.layers import Input
from keras.layers import Convolution2D, MaxPooling2D
from keras.layers.convolutional import AtrousConvolution2D
from keras.utils.data_utils import get_file
from keras import backend as K
TH_WEIGHTS_PATH_NO_TOP = 'https://github.com/fchollet/deep-learning-models/releases/download/v0.1/vgg16_weights_th_dim_ordering_th_kernels_notop.h5'
def dcn_vgg(input_tensor=None):
input_shape = (3, None, None)
if input_tensor is None:
img_input = Input(shape=input_shape)
else:
if not K.is_keras_tensor(input_tensor):
img_input = Input(tensor=input_tensor, shape=input_shape)
else:
img_input = input_tensor
# conv_1
x = Convolution2D(64, 3, 3, activation='relu', border_mode='same', name='block1_conv1')(img_input)
x = Convolution2D(64, 3, 3, activation='relu', border_mode='same', name='block1_conv2')(x)
x = MaxPooling2D((2, 2), strides=(2, 2), name='block1_pool')(x)
# conv_2
x = Convolution2D(128, 3, 3, activation='relu', border_mode='same', name='block2_conv1')(x)
x = Convolution2D(128, 3, 3, activation='relu', border_mode='same', name='block2_conv2')(x)
x = MaxPooling2D((2, 2), strides=(2, 2), name='block2_pool')(x)
# conv_3
x = Convolution2D(256, 3, 3, activation='relu', border_mode='same', name='block3_conv1')(x)
x = Convolution2D(256, 3, 3, activation='relu', border_mode='same', name='block3_conv2')(x)
x = Convolution2D(256, 3, 3, activation='relu', border_mode='same', name='block3_conv3')(x)
x = MaxPooling2D((2, 2), strides=(2, 2), name='block3_pool', border_mode='same')(x)
# conv_4
x = Convolution2D(512, 3, 3, activation='relu', border_mode='same', name='block4_conv1')(x)
x = Convolution2D(512, 3, 3, activation='relu', border_mode='same', name='block4_conv2')(x)
x = Convolution2D(512, 3, 3, activation='relu', border_mode='same', name='block4_conv3')(x)
x = MaxPooling2D((2, 2), strides=(1, 1), name='block4_pool', border_mode='same')(x)
# conv_5
x = AtrousConvolution2D(512, 3, 3, activation='relu', border_mode='same', name='block5_conv1', atrous_rate=(2, 2))(x)
x = AtrousConvolution2D(512, 3, 3, activation='relu', border_mode='same', name='block5_conv2', atrous_rate=(2, 2))(x)
x = AtrousConvolution2D(512, 3, 3, activation='relu', border_mode='same', name='block5_conv3', atrous_rate=(2, 2))(x)
# Create model
model = Model(img_input, x)
# Load weights
weights_path = get_file('vgg16_weights_th_dim_ordering_th_kernels_notop.h5', TH_WEIGHTS_PATH_NO_TOP,
cache_subdir='models')
model.load_weights(weights_path)
return model