-
Notifications
You must be signed in to change notification settings - Fork 0
/
save.py
68 lines (51 loc) · 2.67 KB
/
save.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
import os ,argparse
import tensorflow as tf
#The original freeze_graph function
#from tensorflow.python.tools.freeze_graph import freeze_graph
dir = os.path.dirname(os.path.realpath(__file__))
def freeze_graph(model_dir,output_node_names):
"""Extract the sub graph defined by the output nodes and convert
all its variables into constant
Args:
model_dir:the root folder containing the checkpoint state file
output_node_names: a string,containing all the output node's names,
comma separated
"""
if not tf.gfile.Exists(model_dir):
raise AssertionError(
"Export directory doesn't exists. Pleast specify an export"
"directory: %s" % model_dir)
if not output_node_names:
print("You need to supply the name of a node to --output_node_names.")
return -1
#We retrieve our checkpoint fullpath
checkpoint = tf.train.get_checkpoint_state(model_dir)
input_checkpoint = checkpoint.model_checkpoint_path
#We precise the file fullname of our freezed graph
absolute_model_dir = "/".join(input_checkpoint.split('/')[:-1])
output_graph = absolute_model_dir + "/frozen_model.pb"
#We clear devices to allow TensorFlow to control on which device it will load operations
clear_devices = True
#We start a session using a temporary fresh Graph
with tf.Session(graph = tf.Graph()) as sess:
#We import the meta graph in the current default Graph
saver = tf.train.import_meta_graph(input_checkpoint + '.meta',clear_devices=clear_devices)
#We restore the weights
saver.restore(sess,input_checkpoint)
#We use a built-in TF helper to export variables to constants
output_graph_def = tf.graph_util.convert_variables_to_constants(
sess,#The session is used to retrieve the weights
tf.get_default_graph().as_graph_def(),#THe graph_def is used to retrieve the nodes
output_node_names.split(",")#The output node names are used to the usefull nodes
)
#Finally we serialize and dump the output graph to the filesystem
with tf.gfile.GFile(output_graph,"wb") as f:
f.write(output_graph_def.SerializeToString())
print("%d ops in the final graph." % len(output_graph_def.node))
return output_graph_def
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument("--model_dir",type = str,default ="",help = "Model folder to export")
parser.add_argument("--output_node_names",type = str,default ="",help = "The name of the output nodes,comma separated")
args = parser.parse_args()
freeze_graph(args.model_dir,args.output_node_names)