CSRnet/Convet2tf.py at master · DiaoXY/CSRnet · 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
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
# -*- coding: utf-8 -*-
"""
Created on Mon Nov  5 20:37:27 2018

@author: lenovo
"""

from keras.models import Model
from keras.layers import *
import os
import tensorflow as tf

from keras import backend as K


def keras_to_tensorflow(keras_model, output_dir, model_name,out_prefix="output_", log_tensorboard=True):

   if os.path.exists(output_dir) == False:
       os.mkdir(output_dir)

   out_nodes = []

   for i in range(len(keras_model.outputs)):
       out_nodes.append(out_prefix + str(i + 1))
       tf.identity(keras_model.output[i], out_prefix + str(i + 1))

   sess = K.get_session()

   from tensorflow.python.framework import graph_util, graph_io

   init_graph = sess.graph.as_graph_def()

   main_graph = graph_util.convert_variables_to_constants(sess, init_graph, out_nodes)

   graph_io.write_graph(main_graph, output_dir, name=model_name, as_text=False)

   if log_tensorboard:
       from tensorflow.python.tools import import_pb_to_tensorboard

       import_pb_to_tensorboard.import_to_tensorboard(
           os.path.join(output_dir, model_name),
           output_dir)


"""
We explicitly redefine the Squeezent architecture since Keras has no predefined Squeezenet
"""

def squeezenet_fire_module(input, input_channel_small=16, input_channel_large=64):

   channel_axis = 3

   input = Conv2D(input_channel_small, (1,1), padding="valid" )(input)
   input = Activation("relu")(input)

   input_branch_1 = Conv2D(input_channel_large, (1,1), padding="valid" )(input)
   input_branch_1 = Activation("relu")(input_branch_1)

   input_branch_2 = Conv2D(input_channel_large, (3, 3), padding="same")(input)
   input_branch_2 = Activation("relu")(input_branch_2)

   input = concatenate([input_branch_1, input_branch_2], axis=channel_axis)

   return input


def SqueezeNet(input_shape=(224,224,3)):


   image_input = Input(shape=input_shape)


   network = Conv2D(64, (3,3), padding="same")(image_input)
   network = Activation("relu")(network)
   network = Conv2D(64, (3,3), padding="same")(network)
   network = Activation("relu")(network)
   network = MaxPool2D( pool_size=(2,2) , strides=(2,2))(network)

   network = Conv2D(128, (3,3), padding="same")(network)
   network = Activation("relu")(network)
   network = Conv2D(128, (3,3), padding="same")(network)
   network = Activation("relu")(network)
   network = MaxPool2D( pool_size=(2,2) , strides=(2,2))(network)

   network = Conv2D(256, (3,3), padding="same")(network)
   network = Activation("relu")(network)
   network = Conv2D(256, (3,3), padding="same")(network)
   network = Activation("relu")(network)
   network = Conv2D(256, (3,3), padding="same")(network)
   network = Activation("relu")(network)
   network = MaxPool2D( pool_size=(2,2) , strides=(2,2))(network)

   network = Conv2D(512, (3,3), padding="same")(network)
   network = Activation("relu")(network)
   network = Conv2D(512, (3,3), padding="same")(network)
   network = Activation("relu")(network)
   network = Conv2D(512, (3,3), padding="same")(network)
   network = Activation("relu")(network)
   network = MaxPool2D( pool_size=(2,2) , strides=(2,2))(network)

   network = Conv2D(512, (3,3), padding="same",dilation_rate = 2)(network)
   network = Activation("relu")(network)
   network = Conv2D(512, (3,3), padding="same",dilation_rate = 2)(network)
   network = Activation("relu")(network)
   network = Conv2D(512, (3,3), padding="same",dilation_rate = 2)(network)
   network = Activation("relu")(network)
   network = Conv2D(256, (3,3), padding="same",dilation_rate = 2)(network)
   network = Activation("relu")(network)
   network = Conv2D(128, (3,3), padding="same",dilation_rate = 2)(network)
   network = Activation("relu")(network)
   network = Conv2D(64, (3,3), padding="same",dilation_rate = 2)(network)
   network = Activation("relu")(network)


   """
   network = squeezenet_fire_module(input=network, input_channel_small=16, input_channel_large=64)
   network = squeezenet_fire_module(input=network, input_channel_small=16, input_channel_large=64)
   network = MaxPool2D(pool_size=(3,3), strides=(2,2))(network)

   network = squeezenet_fire_module(input=network, input_channel_small=32, input_channel_large=128)
   network = squeezenet_fire_module(input=network, input_channel_small=32, input_channel_large=128)
   network = MaxPool2D(pool_size=(3, 3), strides=(2, 2))(network)

   network = squeezenet_fire_module(input=network, input_channel_small=48, input_channel_large=192)
   network = squeezenet_fire_module(input=network, input_channel_small=48, input_channel_large=192)
   network = squeezenet_fire_module(input=network, input_channel_small=64, input_channel_large=256)
   network = squeezenet_fire_module(input=network, input_channel_small=64, input_channel_large=256)
   """
   #Remove layers like Dropout and BatchNormalization, they are only needed in training
   #network = Dropout(0.5)(network)

   network = Conv2D(1, kernel_size=(1,1), padding="same", name="last_conv")(network)
   network = Activation("relu")(network)

   #network = GlobalAvgPool2D()(network)
   #network = Activation("softmax",name="output")(network)


   input_image = image_input
   model = Model(inputs=input_image, outputs=network)

   return model


keras_model = SqueezeNet()

keras_model.load_weights("weights/model_A_weights.h5")


output_dir = os.path.join(os.getcwd(),"checkpoint")

keras_to_tensorflow(keras_model,output_dir=output_dir,model_name="squeezenet.pb")

print("MODEL SAVED")