h5_to_pb.py
from keras.models import load_model
import tensorflow as tf
import os
import os.path as osp
from keras import backend as K
#路径参数
input_path = 'input path'
weight_file = 'weight.h5'
weight_file_path = osp.join(input_path,weight_file)
output_graph_name = weight_file[:-3] + '.pb'
#转换函数
def h5_to_pb(h5_model,output_dir,model_name,out_prefix = "output_",log_tensorboard = True):
if osp.exists(output_dir) == False:
os.mkdir(output_dir)
out_nodes = []
for i in range(len(h5_model.outputs)):
out_nodes.append(out_prefix + str(i + 1))
tf.identity(h5_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(osp.join(output_dir,model_name),output_dir)
#输出路径
output_dir = osp.join(os.getcwd(),"trans_model")
#加载模型
h5_model = load_model(weight_file_path)
h5_to_pb(h5_model,output_dir = output_dir,model_name = output_graph_name)
print('model saved')
将转换成的pb模型进行加载
load_pb.py import tensorflow as tf from tensorflow.python.platform import gfile def load_pb(pb_file_path): sess = tf.Session() with gfile.FastGFile(pb_file_path, 'rb') as f: graph_def = tf.GraphDef() graph_def.ParseFromString(f.read()) sess.graph.as_default() tf.import_graph_def(graph_def, name='') print(sess.run('b:0')) #输入 input_x = sess.graph.get_tensor_by_name('x:0') input_y = sess.graph.get_tensor_by_name('y:0') #输出 op = sess.graph.get_tensor_by_name('op_to_store:0') #预测结果 ret = sess.run(op, {input_x: 3, input_y: 4}) print(ret)
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